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Adoption and implementation of innovative diagnostic tools for Alzheimer's Disease: challenges and barriers in primary care

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Adoption and implementation of innovative diagnostic tools for Alzheimer's Disease: challenges and barriers in primary care

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Content ADOPTION AND IMPLEMENTATION OF INNOVATIVE DIAGNOSTIC TOOLS FOR
ALZHEIMER’S DISEASE:
CHALLENGES AND BARRIERS IN PRIMARY CARE
By
Hossein Pourmand
A Dissertation Presented to the
FACULTY OF THE SOL PRICE SCHOOL OF PUBLIC POLICY
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF POLICY, PLANNING, AND DEVELOPMENT
August 2024
Copyright 2024 Hossein Pourmand

ii
Dedication
This dissertation is dedicated to the memory of my late father, Kioumarse Pourmand, MD,
whose passion for medicine and patient care and unwavering belief in the power of education
inspired me throughout my life. His encouragement and pride in my pursuit of higher education
have been a constant source of motivation, and I know he would be proud to see this achievement
realized.
To my mother, Lili, who has always been my pillar of strength and support, thank you for
your endless love and encouragement.
To my wife, Roya, and son, Pedram, your patience, understanding, and support have been
invaluable throughout this journey. Your love and belief in me has been a driving force behind my
success.
To my family members near and far, too many to mention individually, and to my friends
and USC colleagues, your encouragement and support have meant the world to me. Thank you for
standing by me every step of the way.

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Acknowledgments
I would like to begin by expressing my deepest gratitude to Dr. Deborah Natoli who, as
DPPD program director encouraged me to apply and provided unwavering guidance and support
throughout my doctoral journey. Your faith in my potential and your encouragement as a
committee member made all the difference. Thank you also for you vision and leadership of the
DPPD program.
To my dissertation chair, Dr. Julie Zissimopoulos, I am incredibly grateful for the
opportunity to be a part of your research group. It has been both an honor and a privilege to work
under your mentorship. Your expertise, patience, and insightful feedback have been instrumental
in shaping this research. I look forward to opportunities to remain engaged with your research
team.
I also want to extend my heartfelt thanks to Dr. Phil Dalton, whose thoughtful critiques and
support as a committee member played a significant role in refining this work. Your perspectives
and commitment to this project have been invaluable.
A special acknowledgment goes to Alireza Atri, MD, PhD a distinguished cognitive
neurologist, neuroscientist, clinical researcher and educator in the fields of Alzheimer's disease
(AD) and Related Dementias (ADRD). Your guidance on the latest advances in Alzheimer's
diagnosis and treatment were tremendously helpful in shaping the direction of this study. Your
expertise and generosity in sharing your knowledge are deeply appreciated.
I would like to also thank the faculty at the USC Sol Price School of Public Policy for their
support of the DPPD program, teaching the courses, and mentoring students and guidance the
research.

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Table of Contents
Dedication………………………………………………………………………………………...ii
Acknowledgments..……………………………………………………………………………....iii
List of Tables……………………………………………………………………………………..vi
List of Figures……………………………………………………………………………………vii
Abstract………………………………………………………………………………………….viii
Chapter I: Introduction…………………………………………………………………………….1
Background………………………………………………………………………………..2
Alzheimer’s Disease: Toward a Biological Definition……………………………………4
Risk Factors and Prevention………………………………………………………………7
Prevalence………………………………………………………………………………..11
Detection and Diagnosis…………………………………………………………………14
Barriers to Time and Accurate Diagnosis………………………………………………..17
Ethical Concerns and Issues……………………………………………………………...20
Socioeconomic Costs…………………………………………………………………….21
Public Policies……………………………………………………………………………23
Research Problem………………………………………………………………………..27
Research Question……………………………………………………………………….29
Significance of the Study………………………………………………………………...30
Overview of Research Design…………………………………………………………...32
Summary…………………………………………………………………………………33
Chapter II: Literature Review……………………………………………………………………34
Barriers to Timely Detection and Diagnosis……………………………………………..34
Innovative Diagnostic Tools..............................................................................................50
Current and Future Treatments………………………………………………………..…55
Diffusion of Innovation in Clinical Practice……………………………………………..58
Summary…………………………………………………………………………………62
Chapter III: Research Method
Introduction………………………………………………………………………………64
Research Design and Rationale………………………………………………………… 64
Qualitative Study………………………………………………………………...64
Philosophical Framework………………………………………………………..67
Research Approach: Grounded Theory Methodology………………………….. 68
The Role of the Researcher………………………………………………………68
Study Particpants……………………………………………………………...…70
Sampling Plan, Techniques, and Population……………………………………..72
Limitations of Sample Population……………………………………………….74
Data Collection…………………………………………………………………..75

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Interview Process……………………………………………………………….. 76
Transcription Process…………………………………………………………….77
Data Analysis…………………………………………………………………….78
Procedures Followed……………………………………………………………..80
Ethical Considerations…………………………………………………………………. 82
Trustworthiness…………………………………………………………………………..83
Limitations……………………………………………………………………………….85
Summary…………………………………………………………………………………87
Chapter IV: Results………………………………………………………………………………88
Overview of Methodology……………………………………………………………….88
Study Participannts………………………………………………………………………91
Data and Analysis………………………………………………………………………..94
Coding Process…………………………………………………………………..94
Summary of Themes and Results………………………………………………………..98
Theme 1: Clinical and Diagnostic Complexities……………………………….100
Theme 2: Systemic Constraints and Operational Barriers……………………...107
Theme 3: Economic and Financial Challenges…………………………………110
Theme 4: Patient Perceptions, Beliefs, and Attitudes…………………………..112
Theme 5: Gaps in Physician Knowledge……………………………………….116
Theme 6: Availability and Limitations of Disease-Modifying Treatments…….118
Summary………………………………………………………………………………..123
Chapter V: Discussion………………………………………………………………………….126
Interpretation of the Findings…………………………………………………………...127
Clinical and Diagnostic Complexities………………………………………….127
Systemic Constraints and Operational Barriers………………………………...130
Economic and Financial Challenges……………………………………………132
Patient Perceptions, Beliefs, and Attitudes……………………………………..133
Gaps in Physician Knowledge………………………………………………….137
Availability, Limitations and Risks of Disease-Modifying Treatments………..140
Implications for Practice………………………………………………………………..143
Limitations and Recommendations for Future Research……………………………….146
Conclusions……………………………………………………………………………..149
References………………………………………………………………………………………152
Appendix A: Email Request for Participation in Study………………………………………...165
Appendix B: Guiding Questions for Semi-structured Interviews………………………………166
Appendix C: Institutional Review Board Approval…………………………………………….168
Appendix D: List of Codes from Open Coding………………………………………………. .170

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List of Tables
Table 1: Summary of Physician-related Factors as Barriers to Timely and Accurate Diagnosis..45
Table 2: Summary of Patient or Caregiver-related Factors as Barriers to Timely Diagnosis…...48
Table 3: Characteristics of Study Participants………………………………………………….. 93
Table 4: List of eight categories and associated codes………………………………………….. 97
Table 5: The Relationship Between the Six Themes and Participant Characteristics……………122

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List of Figures
Figure 1: Alzheimer’s Disease continuum……………………………………………………….. 7
Figure 2: American Health Association’s model for defining brain health, with specific
prevention interventions and risk factor management in primary care……………………………9
Figure 3: Distribution of adopter innovativeness based on time of adoption……………………..61
Figure 4: Breakdown of study participant’s years in practice……………………………………92
Figure 5: Data Coding and Analysis Process…………………………………………………… 95

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Abstract
Despite many advances in the past two to three decades in our understanding of
Alzheimer’s disease, timely and accurate diagnosis remains a significant issue. Recent advances
in biomarker-based diagnostic tools and their increasing availability in clinical practice offers
optimism in addressing these issues. However, the timely and effective adoption of innovative
biomarker-based diagnostic tools for Alzheimer's Disease (AD) in primary care may face
significant challenges. This qualitative study, underpinned by the Diffusion of Innovation theory,
explores the barriers to adopting these tools among primary care physicians. Using grounded
theory methodology, semi-structured interviews were conducted with 24 primary care physicians
practicing in various settings in Southern California, including solo practices, small group
practices, and outpatient clinics within large health systems such as academic medical centers.
The study identified six key themes that highlight the complexity of adopting new
diagnostic technologies in primary care: Clinical and Diagnostic Complexities, Systemic and
Operational Barriers, Economic and Financial Challenges, Patient-related Issues, Physician
Knowledge and Training Gaps, and Therapeutic and Treatment Limitations. These themes
illustrate the multifaceted nature of the barriers, ranging from the lack of time and resources,
workflow disruptions, and insufficient training, to patient skepticism and concerns about the
accuracy and reliability of new diagnostic tools. The extent to which each theme is a barrier varies
based on the type of practice, years of experience, and, to a lesser extent, gender.
The findings underscore the need for targeted strategies to support primary care physicians
in integrating innovative diagnostic tools into their practices. This includes improving access to
resources, streamlining workflows, and enhancing physician education and training. Furthermore,
the study's results align with the Diffusion of Innovation theory, demonstrating that the perceived

ix
relative advantage, complexity, and compatibility of new diagnostic tools significantly influence
their adoption in primary care settings.
This research contributed to the broader understanding of how primary care physicians
navigate the challenges of diagnosing Alzheimer’s Disease and offers insights into how healthcare
systems and policymakers can better support the adoption of innovative diagnostic technologies.
Future research should build on these findings using a mixed-methods approach to further explore
and quantify these barriers across a larger and more diverse population of primary care physicians.

1
Chapter I: Introduction
The purpose of this qualitative study was to identify the barriers and challenges to the
adoption and implementation of innovative diagnostic tools for Alzheimer’s disease and related
dementia (ADRD) by primary care physicians. Primary care physicians account for diagnosing
85% of dementia cases in the United States (Drabo, et al., 2019). Rapid progress is being made in
the development of new biomarkers and diagnostic tools for improving early and timely detection
and diagnosis. With the emergence of disease-modifying treatments, there is now a greater urgency
for timely and accurate detection and diagnosis of Alzheimer’s disease.
This study represented the first time that the voices of primary care physicians were heard
directly in relation to innovative diagnostic tools for Alzheimer’s disease. The results of this study
were aimed at improving our understanding of factors affecting the use and implementation of
these innovative diagnostic tools, which are expected to represent a paradigm shift in the diagnosis
and management of this complex disease. These tools have the potential to enable early, timely,
and accurate detection and diagnosis, thereby providing patients the opportunity to benefit from
disease-modifying treatments (DMT), that are just now entering clinical practice.
For the purposes of this study, primary care physicians refer to those who have postgraduate specialization in family medicine or internal medicine in a variety of outpatient settings,
such as private practices (solo or group), hospital outpatient departments, and community health
centers. Specialist physicians refer to those who have post-graduate specialization in neurology,
geriatrics, or neuropsychiatry, and currently diagnose 15% of dementia cases.
This chapter is divided into the following sections in order to provide a comprehensive overview
of Alzheimer’s disease: (a) Background, (b) Alzheimer’s Disease: Toward a Biological Definition,
(c) Risk Factors and Prevention, (d) Prevalence, (e) Detection and Diagnosis, (f) Barriers to

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Accurate and Timely, (g) Ethical Concerns and Issues, (h) Socioeconomic Costs, and (i) Public
Policies. The research problem, question, and design are then presented, followed by the
significance of the study.
Background
Dementia is an overall term for a group of symptoms including difficulties with memory,
language, problem-solving, and other thinking skills. It is considered a highly complex and
heterogeneous disease, as evidenced by a variety of causes, clinical presentations, prognosis, and
progression. Alzheimer’s disease is the most common cause of dementia, accounting for 60-70%
of cases (Molineuvo, et al., 2018). Alzheimer's disease, named after Dr. Alois Alzheimer, who
first described the disease in 1906, is a progressive and irreversible neurodegenerative disorder
that primarily affects the brain, leading to cognitive decline, loss of memory and brain function,
behavior, and personality changes. Approximately 95% of Alzheimer’s disease cases are
considered late-onset, defined as occurring in individuals age 65 and older. The focus of this study
is late-onset Alzheimer’s.
Alzheimer's disease is a major global health concern as the aging population increases.
Governments, organizations, researchers, and clinicians worldwide have intensified efforts to
understand the disease, develop diagnostic tools, and search for effective treatments. In 2018, an
estimated 6.9 million Americans age 65 and older were living with Alzheimer’s dementia. The
cost of medical and long-term care was estimated to be $290 billion in 2019. Barring the
development of therapeutic breakthroughs to prevent, slow, or cure the disease, this number could
grow to 13.8 million by 2060, with costs exceeding $1.5 trillion (Zissimopoulos, Crimmins, & St.
Clair, The Value of Delaying Alzheimer's Disease Onset, 2015).

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Currently, there is no single definitive test for Alzheimer’s dementia, and a diagnosis is
made on the basis of a combination of factors, which are discussed later in this chapter. Delays in
detection and diagnosis, misdiagnosis, and missed diagnosis, particularly in primary care settings,
have been identified as persistent issues (Jacobson, Joe, & Zissimopoulos, Barrier to seeking care
for memory problems: A vignette study, 2022). This has created myriad negative consequences
for patients, their family members, and caregivers. A timely and accurate diagnosis is critical for
access to pharmaceutical and nonpharmaceutical interventions and quality of life. Several
promising new biomarkers and diagnostic tests are being developed and expected to enter clinical
practice in the next several years, with the potential to make accurate and early diagnosis more
accessible and affordable (Zetterberg & Bendlin, 2021).
In a recent Alzheimer’s Association survey of primary care physicians, 82% reported being
on the front lines of providing critical elements of dementia care (Alzheimer's Association Report,
2020). Primary care physicians have a long-established and trusted relationship with their older
patients. Adults over age 65 make an average of 2.8 primary care visits annually (Petersen, et al.,
2012). Primary care physicians account for diagnosing 85% of dementia cases in the United States,
with just under half of those being identified as Alzheimer’s disease and 33% being unspecified
dementia. As disease-modifying treatments are becoming available, the identification of dementia
type is even more essential for optimal disease management and patient care (Drabo, et al., 2019).
Use of dementia specialists after a diagnosis by a primary care physician is low, at 22%
within one year and only 36% within five years. A key driver is the shortage of specialists in many
parts of the country (Drabo, et al., 2019). As innovative diagnostic tools, new treatment paradigms,
and disease-modifying therapies emerge, the critical role of primary care physicians must evolve
(Liss, et al., 2021).

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Alzheimer’s Disease: Toward a Biological Definition
With the emergence of fluid and imaging biomarkers in recent years, the classical
definition of Alzheimer’s disease is evolving from being based on symptoms and clinical
consequences to disease pathology. James et al. (2016) have conducted extensive research on
mixed pathologies in Alzheimer's disease, highlighting the importance of considering not just AD
pathology but also other coexisting pathologies. Their work has shown that mixed pathologies are
highly prevalent and play a significant role in the clinical expression of dementia (James, 2016).
In an earlier study, Barnes et al. (2015 (Barnes, et al., 2015)) reported that mixed pathologies
account for most cases of clinical Alzheimer's dementia, and the presence of multiple pathologies
is associated with more severe cognitive impairment than any single pathology alone (Barnes, et
al., 2015). These findings underscore the importance of considering mixed pathologies in the
understanding and treatment of Alzheimer's disease dementia.
The National Institute on Aging (NIA) and the Alzheimer’s Association formed working
groups in 2011, 2018, and 2023 to draft clinical guidelines for Alzheimer’s diagnosis based on
pathology markers and clinical symptoms. The 2023 draft incorporated blood-based biomarkers
into the criteria for categorization, diagnosis, and staging of the disease. This draft represents a
significant shift from previous guidelines by emphasizing a biological definition of AD based on
biomarkers rather than solely clinical symptoms.
The proposed guidelines introduce a biomarker-based approach to diagnosing AD, even in
asymptomatic individuals. Individuals would be classified based on their biomarker profile,
reflecting the presence of amyloid, tau, and neurodegeneration pathologies (labeled AT(N)). This
marks a departure from the traditional syndromic diagnosis relying primarily on clinical
manifestations (Alzheimer’s Association Workgroup , 2024)

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A key component of the draft guidelines is a numeric clinical staging system ranging from
1 to 6, with stages 1-3 representing the pre-dementia phase and stages 4-6 the dementia phase.
This staging incorporates both current cognitive/functional performance and evidence of cognitive
decline, aiming to capture the disease continuum from asymptomatic to dementia stages. The
staging criteria consider biomarker profiles and clinical impairment severity, allowing for a more
granular characterization of disease progression. For instance, stage 2 would encompass
cognitively unimpaired individuals with subtle clinical changes who do not meet the criteria for
mild cognitive impairment. (Alzheimer’s Association Workgroup , 2024)
While the proposed guidelines aim to align AD diagnosis with current scientific
understanding, concerns have been raised about the potential risks of overdiagnosis, particularly
in asymptomatic individuals, and the societal implications of such a shift (American Geriatrics
Society, 2023; Liss, et al., 2021). As the field transitions towards biomarker-based diagnosis, this
draft criteria attempts to bridge research and clinical practice, though their adoption may require
further evaluation and revisions, along with an understanding of implications for the timing of,
and approach to, detection and diagnosis in primary care.
At a biological level, hallmarks of Alzheimer’s include the abnormal accumulation of the
proteins beta-amyloid, tau, and phosphorylated tau. Recent research breakthroughs point to
Alzheimer’s having a one to two-decade-long asymptomatic or preclinical phase, during which
these abnormal accumulations may be occurring, but cognitive impairment is not detectable
(Dubois, et al., 2021). With recent and relatively rapid advances in blood-based biomarkers for
both amyloid and tau pathology, it is only a matter of time before the detection and diagnosis of
asymptomatic Alzheimer’s disease enter clinical practice (Schindler & Bateman, 2021). However,
not all individuals with underlying AD pathology will go on to develop clinical symptoms. The

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risk of progression is dependent on several factors, including genetics, age, sex, race, and ethnicity.
Initial studies have pointed to 20% to 30% of patients with preclinical Alzheimer’s progressing to
MCI (Porsteinsson, Isaacson, Knox, Sabbagh, & Rubino, 2021). The expansion of the definition
of Alzheimer’s Disease to include patients testing positive for AD pathology could significantly
increase the number of primary care patients expressing concerns about cognition and requiring
periodic testing and care.
The clinical stage is the period starting with the manifestation of symptoms and lasting
until death. Within the clinical stage, Mild Cognitive Impairment (MCI) due to dementia is
characterized by very mild symptoms that may not interfere with everyday activities. The disease
may next progress to mild Alzheimer’s dementia, with symptoms interfering with some daily
activities. As symptoms worsen and interfere with many daily activities, the disease enters the
moderate phase. In the severe phase, the symptoms interfere with most daily activities and the
patient becomes dependent on others for care. They ultimately lose their ability to communicate
and become bed-bound, requiring around-the-clock skilled care. The progression from the
asymptomatic phase to the symptomatic phase, where brain changes that cause memory problems
occur, is called the Alzheimer’s disease continuum (Liss, et al., 2021). Figure 1 shows the
Alzheimer’s disease continuum (Alzheimer's Association Report, 2023).
The pace at which symptoms progress from one phase to another and the duration of each
phase varies from person to person and may be dependent on genetics, environment, and other
factors (Gustavsson, et al., 2022). One-third of those with MCI will develop Alzheimer’s (the most
common form of dementia) within five years. As blood-based biomarkers are validated and enter
clinical practice, they could be used for diagnosis and tracking the progression of the disease
(Vellas & Aisen, 2021).

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Figure 1: Alzheimer’s Disease continuum. (Source: 2023 Alzheimer’s disease facts and figures,
Alzheimer’s Association Report.)
Risk Factors and Prevention
Researchers have identified age, genetics, and family history as non-modifiable risk factors
for Alzheimer’s disease. Age is the greatest of these risk factors, with 80 percent of Alzheimer’s
patients being age 75 and older, while only 16 percent are between age 65 and 74. The prevalence
of AD in people age 65 to 74 is five percent, and that for age 85 and over is 33% (Alzheimer's
Association Report, 2023). The prevalence and incidence of Alzheimer’s disease are discussed
later in this chapter.
Researchers have discovered and continue to find genes that are associated with an increase
in the risk of Alzheimer’s. Of these, apolipoprotein e4 (APOE-e4) genotype is the major genetic
risk factor. The e3 form APOE is believed to have a neutral effect on risk, while the e2 form may
decrease the risk of developing the disease. Having the e4 form APOE does not guarantee that an
individual will develop Alzheimer’s, but there is a three-fold increase in risk with having one copy
and an eight to twelve-fold greater risk with two copies of the e4 form. Researchers estimate that
between 40% and 65% of individuals diagnosed with Alzheimer’s have one or two copies of
APOE-e4 (Alzheimer's Association Report, 2022). Recent studies have found that a higher
percentage of African Americans than European Americans have at least one copy of the e4 form

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(Rajan, et al., 2017). The higher prevalence and incidence of Alzheimer’s for African Americans
will be discussed in the next section, and genetics may be only one of several contributing factors.
With the advent and increasing adoption of direct-to-consumer genetic testing, primary
care physicians are confronted with patients’ genetic information (Korthauer, et al., 2021). Genetic
health risk tests, such as those offered by 23andMe, identify genes associated with the risk of
several diseases, including late-stage Alzheimer’s disease. These tests can show whether an
individual has inherited the APOE-e4 risk gene and primary care physicians may increasingly be
confronted with patients wanting to discuss their results. Primary care physicians lack adequate
readiness for delivering genetic and genomic services, with only 40% having received any training
in basic or clinical genetics, and 36% familiar with specific genetic tests. Nonetheless, 70% believe
that patients would benefit from genetic testing (Sharma, Cox, Kruger, Channamsetty, & Haga,
2022). The knowledge gaps were one of many challenges in integrating genomic medicine into
clinical practice. Other identified barriers included the lack of well-established and clear evidence
of clinical utility for these tests and associated risk factors, as well as low participation of diverse
groups in genomics research studies, thereby limiting the predictive value of disease risk
(Bernhardt, et al., 2012).
Modifiable risk factors for Alzheimer’s dementia have been the subject of research during
the past twenty years. Researchers have identified twelve modifiable risk factors, including
physical activity, smoking, blood pressure, diet, obesity, diabetes, education, and staying socially
and mentally active (Weuve, et al., 2018). Several studies pointed to addressing these factors to
potentially reduce the risk of dementia and cognitive decline (Isaacson, et al., 2018; Baumgart, et
al., 2015).

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In 2021, the American Heart Association (AHA) published a scientific statement for
primary care providers for assessing these modifiable risk factors and promoting cognitive wellbeing. Lazar et al. (2021) identified several studies pointing to modifiable cardiovascular disease
risk factors being associated with future cognitive impairment, including dementia. Figure 2 shows
the AHA’s model for defining brain health, with specific prevention interventions and risk factor
management in primary care (Lazar, et al., 2021). The prevention interventions are in line with the
2010 Affordable Care Act, which mandated that private health plans insurance policies, and public
insurance programs cover a range of preventive services at no cost sharing by the beneficiary (Koh
& Sebelius, 2010).
Figure 2: American Health Association’s model for defining brain health, with specific
prevention interventions and risk factor management in primary care (Lazar, et al., 2021).
Several barriers that primary care physicians may encounter when engaging patients to
address Alzheimer's disease (AD) risk factors and prevention strategies have been identified.
Hochhalter et al. (2012) interviewed 28 primary care physicians in Colorado, Texas, and North
Carolina. Some participants felt that the evidence on the efficacy of preventive strategies for

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cognitive health was not sufficient. Time constraints emerged as a key barrier to discussions
about maintaining cognitive health. Many participants also acknowledged that they do not bring
up cognition issues unless prompted by the patient, observed changes in behavior or verbal skills,
or known family history of cognitive impairment (Hochhalter, et al., 2012).
With the development of biomarkers for the detection of Alzheimer’s disease in the
asymptomatic phase, the appropriate adoption of innovative diagnostic tools in primary care may
offer a large window of opportunity for early risk reduction interventions (Isaacson, et al., 2018).
The concept of the "Alzheimer's disease (AD) exposome" has emerged as a comprehensive
framework to understand the complex interplay between genetic and environmental factors that
contribute to the risk and development of AD. Several recent advances have shed light on this
intricate relationship. One significant advancement is the recognition of the role played by air
pollution, a major environmental exposure, in increasing the risk of AD and cognitive decline.
Studies have linked exposure to particulate matter and other air pollutants to neuroinflammation,
oxidative stress, and the accumulation of amyloid-beta and tau proteins, which are hallmarks of
AD pathology (Finch & Kulminski, 2019).
Research studies have also highlighted the importance of considering the timing and
duration of exposures throughout an individual's lifespan, as well as the potential for geneenvironment interactions (GxE). Studies have shown that certain genetic risk factors for AD, such
as the APOE ε4 allele, may interact with environmental exposures like air pollution, diet, and
lifestyle factors, amplifying or mitigating the risk of cognitive decline and AD development.
(Ding, Wang, Tang, & Shi, 2022; Finch & Kulminski, 2019; Crimmins, 2020)
By integrating these diverse exposome factors, researchers are gaining a more
comprehensive understanding of the complex etiology of AD, paving the way for the development

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of targeted preventive strategies and personalized interventions based on an individual's unique
genetic and environmental risk profile. Our knowledge of primary care physicians’ attitudes and
beliefs about detecting and diagnosing asymptomatic disease is lacking and needs to be addressed.
Prevalence
In the absence of a national dementia screening and tracking program, estimates of
prevalence and incidence and their trends have relied on nationally representative surveys and
healthcare claims data. There was variability in these estimates, with studies using claims data
finding statistically lower prevalence and incidence than those based on survey data. An accurate
understanding of prevalence and incidence can, among other benefits, lead to better policies to
address gaps in the detection and diagnosis of dementia, and better care for patients. The adoption
and implementation of new biomarker-based diagnostic tools in clinical practice, particularly in
primary care, can lead to a more accurate understanding of prevalence and incidence along the
entire cognitive spectrum, trends over time, and across different age, racial, and ethnic groups.
The estimates of the prevalence of dementia in the United States are between 7.9% and
10.8%. (Haye, et al., 2023; Manly, et al., 2022; Rajan, et al., 2021). This equates to 6.7 million
Americans age 65 and older living with Alzheimer’s dementia. This number could grow to 13.8
million by 2060, primarily due to the aging of the US population, if there are no medical
breakthroughs to prevent, slow, or cure the disease. Estimates of the prevalence and incidence of
dementia have identified differences by sex and among racial and ethnic groups. Almost twothirds, or 4.1 million sufferers, are women. Blacks and Hispanics are found to have a higher risk
of dementia compared to whites (Chen & Zissimopoulos, 2018).
Haye et al. (2023) used Medicare claims data to estimate diagnosed dementia prevalence
and incidence. The diagnosed dementia prevalence in the United States was estimated to be 7.9%

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and the incidence is estimated to be 2.8% among the entire Medicare population for the year 2017.
The prevalence was estimated to be highest for Blacks, at 10.85%, followed by 9.97% for
Hispanics, 9.67% for American Indian/Alaska Native, 7.70% for whites, and 7.19% for Asians.
The prevalence for females was higher than that for males (7.6% versus 6.5%). The prevalence
among age groups ranged from 1.84% for beneficiaries age 65 to 69 years, to 31% for those over
age 90. The incidence followed similar patterns, with Blacks at 3.65%, Hispanics at 3.31%,
American Indian/Alaska Native at 3.74%, whites at 2.73%, and Asians at 2.55% (Haye, et al.,
2023).
Manly et al. (2022) estimated the prevalence of dementia and Mild Cognitive Impairment
in the United States for the year 2016, using data from the Health and Retirement Study (HRS).
HRS is an ongoing nationally representative survey study of individuals 51 years and older in age.
The prevalence of dementia was estimated to be 10%, and that for Mild Cognitive Impairment to
be 22% (Manly, et al., 2022). Compared to the prevalence estimate from dementia diagnosis from
Haye et al. (2023), the prevalence was two percentage points higher. This points to the possibility
that 20% of the population is undiagnosed (Haye, et al., 2023). An earlier study found that among
individuals with incident dementia (determined via a cognitive assessment) between the years
2000 and 2004, the undiagnosed rate was 15%. Blacks and Hispanics were more likely than whites
to be undiagnosed. This racial and ethnic disparity may be due to physician, patient, or health
system factors, and is a gap in the research literature (Chen, Tysinger, Crimmins, & Zissimopoulos,
2019).
While the overall prevalence of dementia is increasing due to rising life expectancy, studies
comparing the prevalence over time periods have shown differing results across data sources.
Studies based on cognitive tests from HRS show statistically significant declines in prevalence

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from 2000 to 2012 (Chen & Zissimopoulos, 2018; Zhu, Chen, Crimmins, & Zissimopoulos, 2021;
Langa, et al., 2017). Chen et al. (2018) reported a 25% reduction in prevalence for whites and
blacks over the 12-year period, whereas for Hispanics, the reduction was only 8.6%.
During the 2006 to 2012 time period, prevalence based on diagnosis codes and Medicare
claims data increased by 1% point. Prevalence was also higher for women than men and for blacks
and Hispanics than whites. The factors driving this increase may include increased awareness of
dementia by primary care physicians and greater confidence in making and disclosing a diagnosis.
The decline in age-specific prevalence is thought to be driven in part by improvements in
modifiable risk factors such as cardiovascular health, education, and social and cognitive
engagement (Prince, et al., 2016).
The prevalence of preclinical Alzheimer’s disease has been studied to a far lesser extent.
Researchers have begun to use biomarkers to study amyloid positivity rates among different age
groups. Jansen, et al. (2018) examined the amyloid positivity rate among persons without
cognitive impairment or dementia. The prevalence of amyloid pathology increased from age 50 to
90 years from 10% to 44% among participants with normal cognition. The broader availability and
adoption of biomarker-based detection and diagnosis tools for pre-clinical AD may have
significant implications for primary care. Given that not all individuals with preclinical
Alzheimer's progress to symptomatic disease, primary care providers may find themselves playing
a crucial role in counseling and educating patients about the nature of preclinical Alzheimer's,
potential outcomes, and the importance of ongoing monitoring.
With the advent of innovative biomarker-based diagnostic tools and disease-modifying
therapies, an accurate understanding of the prevalence and incidence of dementia, their trends, and
any differences by gender, race, and ethnicity is important in preparing the health system.

14
Innovative diagnostic tools, if widely adopted and implemented, may play a key role in improving
the accuracy of prevalence and incidence rates and our understanding of the burden of the disease
and health policy implications. With disease-modifying therapies targeted to patients with mild
cognitive impairment or mild dementia due to Alzheimer’s, and eventually to those in the
preclinical phase of the disease, it will be important to understand the size of this population.
Detection and Diagnosis
Primary care physicians account for diagnosing 85% of dementia cases in the United
States. Use of dementia specialists after a diagnosis by a primary care physician is low, at 22%
within one year and only 36% within five years (Drabo, et al., 2019). A recent Alzheimer’s
Association survey of primary care physicians reported that 82% are on the front lines of dementia
diagnosis. Furthermore, 55% believed that the number of specialists was low and inadequate to
meet patient demand (Alzheimer's Association Report, 2020). The critical role of primary care
physicians in the diagnosis and management of Alzheimer’s dementia, as well as their relationship
with specialists, are expected to continue but evolve as new diagnostic tools and disease-modifying
therapies emerge. The successful adoption and implementation of these innovations in clinical
practice will be critical in alleviating the challenges that primary care physicians have long faced
in the diagnosis, care, and management of this disease.
Throughout the twentieth century and the early part of this century, autopsy findings were
deemed to be the gold standard for a definitive diagnosis. In the last decade, two diagnostic tools
validated as the gold standard for in vivo assessment and diagnosis of the patient are neuroimaging
(Positron Emission Tomography (PET) scan) and cerebrospinal fluid (CSF) analysis. Each of these
tests has associated validated biomarkers related to beta-amyloid, tau, and phosphorylated tau
levels. CSF testing is invasive, requiring lumbar puncture, and exposing individuals to certain

15
risks. Additionally, CSF testing requires specific training and expertise, and approximately half
occur in an inpatient setting (Trunz, et al., 2021). PET scans are expensive, costing on average
between $3,000 and $5,000. Until 2023, Medicare coverage for PET scans was limited to once per
lifetime for patients enrolled in clinical trials. However, with recent regulatory approval of
disease-modifying therapies such as Aduhelm and Leqembi, Medicare has expanded its coverage
and removed the limitation on reimbursement (Steenhuysen, 2023). In clinical trials, these drugs
were given to patients with confirmed presence of AD pathologic changes via amyloid PET scan
or CSF (Liss, et al., 2021).
Given the financial and logistic issues associated with the use of PET scans and CSF
analysis for the diagnosis of Alzheimer’s disease, blood-based biomarkers are an active area of
research. A number of blood-based biomarkers have already been identified and clinically
validated, with additional ones in the research pipeline. These biomarkers have a strong correlation
with PET and CSF biomarkers in terms of accuracy. The ones that have been clinically validated
are expecting approval from the United States Food and Drug Administration (FDA) in the coming
months and years (Barthélemy, et al., 2024; Hampel, et al., 2018). C2N Diagnostics, an early-stage
medical diagnostics company, has developed and is marketing PrecivityADTM, a blood test that
can detect amyloid plaques in the brain. The company’s website states that the test is currently
intended for use in patients aged 55 and older with signs and symptoms of memory decline, and
can only be ordered by a physician. The test currently costs $1,250 and is not covered by private
insurance, Medicare, or Medicaid. The company has a financial assistance program for individuals
who medically and financially qualify (C2N Diagnostics, Inc., 2023).
Currently, physicians use a combination of other tools and techniques to detect and
diagnose Alzheimer’s and dementia. These generally involve interactions with the patient over

16
several visits. For patients reporting symptoms related to cognition, a medical and family history,
and physical evaluation is conducted. There are several potentially reversible conditions with
symptoms similar to that of dementia, such as depression, hypothyroidism, visual and auditory
problems, and vitamin B12 deficiency. A blood test and urinalysis are typically ordered to detect
these conditions. Structural brain imaging, preferably with MRI (or a CT scan) may also be ordered
to assess for non-neurodegenerative treatable conditions (Atri, Imaging of neurodegenerative
cognitive and behavioral disorders: practical considerations for dementia clinical practice, 2016).
In the absence of potentially reversible causes of symptoms, early and effective cognitive
testing of patients is key for diagnosis. There are at least a dozen cognitive assessment tools, but
the three most recommended tests are the Mini-Mental State Examination (MMSE), the Montreal
Cognitive Assessment (MoCA), and the Mini-Cog (Galvin & Sadowsky, 2012). These tests take
between 5 and 12 minutes to administer. Informant-based assessments such as the Alzheimer’s
Questionnaire (AQ) or the Ascertain Dementia 8-item Questionnaire (AD8) may also be used
when testing the patient is challenging. These two tests take between 3 and 5 minutes to
administer. These tests can be used repeatedly and routinely as needed and are conducted by the
primary care physician.
Timely, accurate, and consistent diagnosis of Alzheimer’s disease has been identified as
the best opportunity for early interventions, better management of symptoms and their progression
with current pharmacological interventions, patient safety, cost savings, and delays in
institutionalization. In the context of the Alzheimer’s disease continuum (Figure 1), timely
diagnosis is distinct from early diagnosis.
Timely diagnosis refers to a diagnosis at the MCI due to Alzheimer’s disease stage when
concerns about changes in cognition, behavior, or functioning are brought to a physician’s

17
attention by an individual or a care partner. Accurate diagnosis at this early stage also enables
individuals to benefit from some of the latest disease-modifying therapies or participate in clinical
trials for next-generation therapeutics. Early diagnosis refers to a diagnosis at the pre-clinical stage,
where an individual has no symptoms, but biological changes in the brain may have started to
occur (Dubois, Padovani, Scheltens, Rossi, & Dell'Agnello, 2016) (Liss, et al., 2021).
Barriers to Timely and Accurate Diagnosis
Despite the established importance and benefits of timely diagnosis, Alzheimer’s disease
is generally diagnosed in the moderate and severe stages after an individual has developed
symptoms, typically involving loss of memory and cognition. The estimated time between the
onset of symptoms and diagnosis of dementia is 31.2 months for non-Hispanic Whites, 34.6
months for non-Hispanic Blacks, and 43.8 months for Hispanics (Mattke, et al., 2023).
Furthermore, between 15% and 50% of dementia cases are undiagnosed, and the figures are even
higher for mild cognitive impairment (Jacobson, Joe, & Zissimopoulos, Barrier to seeking care
for memory problems: A vignette study, 2022; Chen, Tysinger, Crimmins, & Zissimopoulos, 2019;
Amjad, et al., 2018). Misdiagnosis has also been identified as an issue, with nearly 25% of patients
not receiving an accurate diagnosis. The rate of inaccuracy for MCI due to Alzheimer’s disease
stage is even greater (Burke & Goldfarb, 2023).
Studies have identified several barriers to a timely and accurate diagnosis by primary care
physicians. These barriers, which are discussed in the review of the literature in Chapter II, fall
into 4 categories: physician-related, clinical profile of Alzheimer’s disease, setting-related, and
patient-related (Judge, Roberts, Khandker, Ambegaonkar, & Black, 2019).
Physician-related barriers to timely diagnosis include lack of adequate knowledge in
identifying early symptoms, lack of confidence in the ability to diagnose AD, risk of misdiagnosis,

18
reluctance to disclose the diagnosis in light of variability in patient’s response to the diagnosis,
time constraints, limited reimbursement, and inadequate care coordination. Research into primary
care physicians’ attitudes and evaluation and management practices has been limited but needs to
be better understood given the prospects of innovative diagnostic tools and disease-modifying
therapeutics (Bernstein, et al., 2019).
Barriers to effective cognitive testing, diagnosis, and disclosure by primary care physicians
include lack of time to evaluate patients, lack of definitive and efficient diagnostic tools, lack of
effective treatments, gaps in knowledge and skills, discomfort with disclosing a diagnosis of
Alzheimer’s, and the need to educate patients and in many cases family members about the results.
Mattke et al. (2023) convened a Work Group of national experts to identify potential policy
recommendations, including reimbursements by third-party payers, to increase the use of brief
cognitive assessments (BCAs) in primary care. The routine use of BCAs may be achieved through
the availability of effective assessment tools, that can be efficiently integrated into a primary care
workflow. Payment policies may need to also be adjusted to encourage the use of BCAs (Mattke,
et al., 2023). Disclosure of a diagnosis to an individual or the care partner is also a significant
issue, with only 60% made aware. Physician training on the use of assessment or diagnostic tools,
the benefits of early and timely diagnosis given new and emerging disease-modifying treatments
may enhance confidence in disclosing a diagnosis (Clevenger, Schlenger, Gunter, & Brewster
Glasgow, 2023).
Since 2011, and the passage of the Affordable Care Act (ACA), Medicare has fully covered
an Annual Wellness Visit (AWV) in primary care, which requires the detection of cognitive
impairment. The uptake of this new benefit has been slow, with only 8% of seniors having an
AWV in 2011, rising to approximately 32% by 2018. (Thunell, Jacobson, & Joe, 2022). A 2019

19
survey of current Medicare beneficiaries reported 67% of Medicare Advantage enrollees and 47%
of traditional Medicare fee-for-service enrollees as having an AWV. However, not all AWVs
included the required detection of cognitive impairment, and only 30% of Medicare Advantage
and 23% of fee-for-service received a structured assessment (Jacobson, Thunell, & Zissimopoulos,
Cognitive Assessment At Medicare's Annual Wellness Visit in Fee-For-Service And Medicare
Advantage Plans, 2020). The uptake of AWV is even lower in rural areas and among non-white
populations, where rates of undiagnosed dementia are likely higher (Thunell, Jacobson, & Joe,
2022). Barriers to the use of AWV and cognitive assessments need to be identified and studied.
The Centers for Medicare and Medicaid Services (CMS) has not recommended a specific
assessment tool, given that no single test satisfies all needs in the detection of cognitive impairment
(Cordell, et al., 2013). The Alzheimer’s Association has published recommendations for the direct
cognitive assessment component of the AWV, but the extent of its adoption by primary care
physicians is not known (Cordell, et al., 2013). The lack of specific guidance for conducting
cognitive assessment and the investment of time by primary care physicians to become trained on
the use and implementation of current tools have been identified as barriers to their adoption and
the uptake of cognitive assessment as part of the AWV (Thunell, Jacobson, & Joe, 2022).
Innovative computerized cognitive testing in primary care has been studied as a way to address
some of these barriers. A study using the Computer Assessment of Mild Cognitive Impairment
(CAMCI) has identified advantages and challenges in such an approach. This tool has not been
widely adopted in primary care (Millett, et al., 2018).
The adoption of innovative diagnostic tools in primary care will play a critical role in the
timely and accurate diagnosis of AD. There is an urgent need to understand the challenges and

20
barriers that may prevent the dissemination, implementation, and wide-scale adoption of these
tools.
Ethical Concerns and Issues
As innovative biomarker-based diagnostic tools move into clinical practice, physicians will
encounter several new ethical challenges and issues. These include who should be tested, dealing
with patients who may or may not want to be tested, the interpretation or significance of test results,
and their subsequent disclosure to patients. This will particularly be the case if asymptomatic or
pre-clinical AD comes under the clinical criteria for the disease, as suggested in 2018 and in 2023
by a working group convened by the National Institute on Aging (NIA) and the Alzheimer’s
Association (AA). The draft of the NIA-AA suggested guidelines, asymptomatic individuals who
test positive for biomarker abnormalities could be diagnosed with pre-clinical AD. This would
create ethical and professional challenges for physicians. Asymptomatic individuals with
biomarker abnormalities have a higher risk of an eventual diagnosis of MCI or symptomatic AD.
However, not all will, and more research is being conducted to learn about progression rates and
characteristics (Roberts, et al., 2018).
A diagnosis of pre-clinical AD may create myriad issues for individuals, including but not
limited to psychological impacts, stigma and feelings of shame, social isolation or exclusion, and
discrimination in employment and insurability (particularly for long-term care and life insurance).
Research into the general public’s attitude to an early diagnosis of AD has shown that a majority
would be willing to be examined and informed if they have the disease. A strong majority also
prefer their primary care physician to provide them with such information and diagnosis (Luck, et
al., 2012).

21
Researchers have started to examine these issues. In clinical research settings, a positive
diagnosis of preclinical stage disease with appropriate pre- and post-disclosure counseling was
found to be correlated to a low risk of psychological harm to the individual (Burns, et al., 2017).
Van der Schaar, et. al. (2022) conducted a systematic review of the literature on disclosing AD
biomarker results to individuals without MCI or dementia. They identified 26 considerations,
which were grouped into three categories and will be discussed in Chapter II, and the review of
the literature (van der Schaar, et al., 2022).
Socioeconomic Costs
The magnitude and complexity of the socioeconomic costs of Alzheimer’s disease and
related dementias are significant, rapidly increasing, and affecting individuals, families, healthcare
systems, and society at large. These costs are disproportionately incurred by women, racial and
ethnic groups, and individuals with lower education and socioeconomic status (Aranda, et al.,
2021).
Alzheimer’s is considered one of the most expensive diseases in the United States. In 2023,
the total payments for health care services, long-term care services, and hospice for people age 65
years and older with dementia were estimated to be $345 billion. Approximately 64 percent of
these costs were borne by Medicare and Medicaid, 25% by patients and/or their families as outof-pocket expenses, and 11% by other payors, such as private insurance and health maintenance
organizations (HMO). Additionally, indirect costs arise from productivity losses, as both patients
and their caregivers often face challenges in maintaining employment or pursuing daily activities.
The value of unpaid caregiving was estimated to be $340 billion in 2022 (Alzheimer's Association
Report, 2023). By 2050, the number of individuals over age 70 with Alzheimer’s disease is

22
projected to be 9.1 million, with total costs exceeding $1.5 trillion (Zissimopoulos, Crimmins, &
St. Clair, 2015).
There are strong correlations between Alzheimer’s disease severity, delayed diagnosis,
misdiagnosis, and the lack of diagnosis and increases in the direct and indirect costs of care.
Inaccurate diagnoses, including misdiagnosing dementia subtype, not only lead to more inpatient
days, emergency department visits, and outpatient visits, but also hinder the ability of patients and
their families to make informed decisions about care, financial planning, and legal matters. The
emotional toll of uncertainty and the prolonged search for a correct diagnosis can contribute to
heightened stress and anxiety, impacting the overall quality of life for those affected. (Aranda, et
al., 2021; El-Hayek, et al., 2019).
The emergence of new biomarker-based diagnostic tools holds promise for addressing
these challenges. These tools have the potential to enhance the accuracy of Alzheimer's diagnoses,
allowing for earlier and more precise identification of the disease. Timely and accurate diagnoses
in the earliest stages of the disease continuum will enable individuals to benefit from the new
disease-modifying treatments and potentially delay the onset of AD by months or years. Delaying
the onset of AD by 5 years would result in a 41% lower prevalence among those aged 70 + and a
40% reduction in societal costs (Zissimopoulos, Crimmins, & St. Clair, 2015).
As innovative disease-modifying treatments have become available, the need to accurately
diagnose AD in the early phases, including MCI is now imperative. Recent policy changes by
Medicare and the lifting of the long-standing restrictions on the reimbursement for PET made these
tests more accessible to physicians for appropriate use in diagnosis. However, a PET scan can cost
approximately $3,000 each time a patient needs it, making it uneconomical as the primary frontline
screening tool. Even if Medicare reimburses each PET scan at $2,000, and 1 million individuals

23
were screened annually, the cost would be $2 billion. The use of blood-based biomarkers in
primary care as a screening tool to identify amyloid-positive candidates for further evaluation with
PET scans and appropriate referrals to specialists could reduce annual costs by $400 to $700
million (Laske, 2015; Mattke, Cho, Bittner, Hlavka, & Hanson, 2020).
An understanding of the overall financial impacts of the new disease-modifying treatments
and their implications for the adoption of innovative diagnostic tools represents a gap in our
knowledge. Lecanemab’s proposed price of $26,500 per year has raised equitable access concerns.
Medicare broadly covers this drug, but high out-of-pocket expenses could limit access for patients
who might benefit from this drug. At this price point, it is inconceivable that the entire target
population could be treated, as the aggregate medication expenditure would be $120 billion
annually, exceeding the entirety of Medicare Part D expenditures. As of 2023, Lecanemab was
only available as an intravenous infusion and in specialized centers, which creates other equitable
access issues, and additional costs. An injectible version is in clinical trials, which may help lower
costs and address some equitable access issues (National Council on Aging, 2024; Burke, Kerber,
Langa, Albin, & Kotagal, 2023). The high cost of disease-modifying treatments, reimbursement
policies, and coverage decisions by third-party payors, and equitable access issues may influence
the adoption of innovative diagnostic tools in primary care.
The compounding effects of the direct and indirect costs underscore the urgent need for
effective diagnostic tools and interventions and their successful adoption and implementation in
primary care to mitigate the economic impact of Alzheimer's disease.
Public Policies
Improving the timely and accurate detection and diagnosis of Alzheimer’s disease and its
subsequent management and treatment has been the subject of several important public policies

24
and regulatory actions during the past fifteen years. In 2010, the National Alzheimer’s Project Act
(NAPA) was passed by Congress and signed into law in 2011. NAPA marked a pivotal moment
in the fight against Alzheimer's disease, establishing a comprehensive national strategy to address
the growing impact of this devastating condition. NAPA mandated the creation of a National Plan
to accelerate research, enhance care, and improve services for individuals living with Alzheimer's
and related dementias. Importantly, it significantly boosted research funding from the National
Institutes of Health (NIH), leading to a substantial increase in resources allocated for Alzheimer's
research. This surge in funding has enabled groundbreaking studies into the underlying
mechanisms of the disease, the development of novel therapeutics, and advancements in early
detection and diagnosis. NAPA's implementation has also driven clinical improvements by
promoting the integration of dementia care into primary healthcare, encouraging the use of
standardized care practices, and fostering the development of new care models. These initiatives
have collectively enhanced the quality of care and support for patients and their families,
highlighting the act's enduring impact on both research and clinical practice in the realm of
Alzheimer's disease (Khachaturian, Khachaturian, & Thies, 2012).
The Affordable Care Act (ACA), signed into law in 2010, included several provisions with
implications for individuals with Alzheimer's and dementia. The ACA mandated that certain
preventive services be provided without cost-sharing requirements. For Medicare beneficiaries, a
preventative primary care visit referred to as an Annual Wellness Visit (AWV), requires a
structured cognitive assessment (Jacobson, Thunell, & Zissimopoulos, Cognitive Assessment At
Medicare's Annual Wellness Visit in Fee-For-Service And Medicare Advantage Plans, 2020).
However, given the lack of a single, universally accepted screening tool for the detection of
cognitive impairment, the Centers for Medicare and Medicaid Services (CMS) did not provide

25
specific guidance or recommendations to primary care physicians. The Alzheimer’s Association
convened a working group of experts to develop AWV cognitive assessment recommendations
and guidelines for primary care physicians. The Alzheimer’s Association Medicare Annual
Wellness Visit Algorithm for Assessment of Cognition was based on then-current validated tools
and commonly used rule-out assessments (Cordell, et al., 2013). No studies have been done to
determine the adoption of these recommendations and guidelines in primary care.
The ACA provided health insurance coverage for uninsured individuals through an
expansion of Medicaid eligibility and sliding-scale subsidies for individual insurance programs. In
states participating in the expansion of Medicaid eligibility, a larger number of low-income
individuals, including those with Alzheimer's and other chronic conditions have been able to obtain
coverage. This was an important development for individuals anywhere in the Alzheimer’s disease
continuum, with socioeconomic status, including persistent low wages, associated with higher
dementia risk, lower cognitive performance, and faster memory decline. This expansion resulted
in better healthcare access, utilization, and outcomes for this population. As of January 2017,
Medicaid was the largest source of health coverage, with seventy-seven million beneficiaries
(Mazurenko, Balio, Agarwal, Carroll, & Menachemi, 2018).
The ACA prohibited health insurance companies from denying coverage or charging
higher premiums to individuals based solely on their health status or pre-existing medical
conditions. (Claxton, Fox, Damico, Levitt, & Pollitz, 2016). This protection extends to
asymptomatic conditions like early-stage Alzheimer's disease detected through biomarkers before
cognitive symptoms appear. Health insurers cannot refuse to sell coverage or renew policies for
individuals diagnosed with asymptomatic Alzheimer's through biomarker testing like PET scans
or cerebrospinal fluid analysis. They are also prohibited from charging higher premiums, imposing

26
higher cost-sharing, or excluding coverage for pre-existing conditions like asymptomatic
Alzheimer's or MCI (Porsteinsson, Isaacson, Knox, Sabbagh, & Rubino, 2021). The ACA
eliminated the ability of insurers to impose pre-existing condition exclusions that could previously
deny coverage for Alzheimer's or related care for a period of time after enrollment (Claxton, Fox,
Damico, Levitt, & Pollitz, 2016).
In 2021, the U.S. Food and Drug Administration (FDA) provided accelerated approval for
aducanumab (Aduhelm), the first disease-modifying therapeutic for Alzheimer’s disease. A second
anti-amyloid antibody intravenous (IV) infusion therapy, lecanemab (Leqembi), was given
traditional FDA approval in 2023. These approvals were specifically for early Alzheimer's disease,
including people living with mild cognitive impairment (MCI) or mild dementia due to
Alzheimer's disease who have confirmation of elevated beta-amyloid in the brain.
In 2022, CMS announced that Medicare will cover Aduhelm for beneficiaries enrolled in
a clinical trial approved by CMS or supported by the National Institutes of Health (NIH). The
medication would need to be administered in a hospital outpatient setting. In 2023, CMS
announced that Medicare will cover Leqembi contingent on patients being enrolled in registries
that provide evidence of their safety and efficacy as they are used in clinical practice.
In 2023, the CMS ended the once-per-lifetime reimbursement limitation on the use of PET
scans to measure amyloid in the brain. Previously, the only other CMS-authorized and reimbursed
method to measure amyloid in the brain was through an invasive cerebrospinal fluid test, which
requires a lumbar puncture. Amyloid confirmation is required for Medicare and Medicaid
reimbursement of Leqembi. The broader coverage for PET will enable early and accurate
diagnosis, which is crucial for initiating treatment with disease-modifying therapies at the earliest
possible stage when they are most likely to be effective in slowing or halting the progression of

27
the disease. PET scans can also be used to monitor the effectiveness of disease-modifying
treatments by tracking changes in brain metabolism, amyloid burden, and other biomarkers over
time. This information can help physicians adjust treatment plans and dosages as needed, ensuring
optimal management of the disease. PET scans can help differentiate Alzheimer's disease from
other forms of dementia, ensuring that the right patients receive the appropriate treatment.
With the advent of blood-based biomarkers and their imminent introduction into clinical
practice, public policies for providing adequate reimbursement could incentivize their adoption
and use in primary care settings. Payers like Medicare and Medicaid could establish coverage
policies that reimburse primary care physicians for ordering and interpreting these tests as part of
the diagnostic workup. Additionally, establishing common, consistent standards of evidence is
required to demonstrate the clinical utility of blood-based biomarkers. Regulatory agencies can
encourage developing evidence-based clinical practice guidelines that provide recommendations
on when and how to use these tests in primary care populations.
Research Problem
Although our understanding of Alzheimer’s disease has grown substantially in the past 20
years, timely and accurate diagnosis remains an issue. There continues to be a significant number
of AD cases that go undiagnosed or are misdiagnosed, even after the onset of symptoms. For MCI,
the undiagnosed and misdiagnosed rates are even higher (Liss, et al., 2021). Primary care
physicians are expected to remain on the front lines of dementia care, given the shortage of
specialists, the aging population, and the growing number of individuals with cognitive issues and
concerns.
With the recent availability of disease-modifying treatments and additional ones in the
research and clinical trial pipelines, there is increasing optimism that we have turned the corner on

28
this disease. However, these drugs are only effective in patients in the earlier phases of the disease
(i.e. MCI or Mild AD dementia), making timely and accurate diagnosis even more critical. Wellestablished and validated biomarkers for PET imaging and CSF analysis provide tools for
diagnosing in the earlier stages, but financial, logistic, and risk factors are limiting their adoption.
A number of blood-based biomarkers have already been identified and clinically validated, with
additional ones in the research pipeline. These biomarkers have a strong correlation with PET and
CSF biomarkers in terms of accuracy.
The adoption of these innovative biomarker-based diagnostic tools in primary care is
crucial for early detection, diagnosis, and management of AD. Despite the potential benefits of
these innovative tools, our lack of knowledge as to whether, when, and how they will be integrated
into clinical practice and primary care settings, in particular, is a problem. Studies have identified
the general problem of primary care physicians facing several challenges and barriers in
diagnosing Alzheimer’s using current tools, such as a lack of adequate knowledge, lack of time,
and discomfort in disclosing a diagnosis. A knowledge gap exists as to what may motivate primary
care physicians in a variety of clinic settings to integrate these tests into their clinical workflow
and practice, when they may do so, and how.
Early detection and accurate diagnosis of AD are a public health imperative. In 2017, the
Alzheimer’s Association convened the Best Clinical Practices Guideline (CPG) Workgroup to
identify gaps in the clinical evaluation of AD and provide specific guidelines relevant to both
primary care and specialty settings (Atri, et al., 2018). Some publications have offered practical
recommendations for timely and accurate diagnosis of symptomatic AD in primary care
(Porsteinsson, Isaacson, Knox, Sabbagh, & Rubino, 2021) (Liss, et al., 2021). This qualitative

29
study brings for the first time the voice of the primary care physician on this specifc problem and
at a critical juncture.
Research Question
With the recent breakthroughs in innovative biomarker-based diagnostic tools for Alzheimer’s
disease and their imminent availability for clinical settings, there is an urgent need to understand
how, when, where, and by whom these tools will be used. The primary question that this qualitative
study sought to answer was: What are the barriers and challenges that may prevent the effective
and efficient adoption of innovative biomarker-based diagnostic tools for Alzheimer’s disease in
primary care? The following sub-questions guided this study:
1. What are the knowledge gaps and training needs among primary care providers regarding
the appropriate use, interpretation, and clinical utility of biomarker-based diagnostic tests like
amyloid PET, tau PET, CSF, and blood biomarkers for AD diagnosis?
2. How do factors like costs, lack of clear reimbursement models, and insurance coverage
policies impact the accessibility and adoption of AD biomarker testing in primary care settings?
3. What are the workflow and operational barriers (e.g. staffing, equipment, integration into
existing processes) for implementing biomarker-based AD diagnostics in the primary care clinical
environment?
4. How do the non-specificity and prognostic (rather than diagnostic) nature of some AD
biomarkers create challenges for their effective use and interpretation by primary care physicians?
5. What regulatory, ethical, or legal concerns exist around using biomarker tests for early
screening and diagnosis of preclinical/asymptomatic AD in primary care populations?
6. What are the patient-related factors that primary care physicians may encounter in relation
to the use of these biomarker-based tools?

30
Significance of the Study
Primary care providers are generally the first healthcare professionals seen by individuals
concerned about changes in cognition, behavior, or functioning. In the United States, healthcare
reform has resulted in an increase in the role of primary care physicians and their involvement in
the coordination of care of their patients. As such, improving the effectiveness of primary care
around complex and costly diseases such as AD can have a positive impact on health outcomes,
while potentially lowering socioeconomic costs.
The complexity of Alzheimer’s disease and the lack of accurate, non-invasive, costeffective, and widely accessible diagnostic tools have been identified as barriers to timely and
accurate diagnosis. Undiagnosed cases of Alzheimer’s disease and MCI have been a persistent
problem. Innovative biomarker-based diagnostic tools, such as blood tests, which are now
becoming available, can bring about a paradigm shift in AD diagnosis. However, whether, when,
and how they will be adopted in primary care represents a gap in knowledge. To the best of the
researcher’s knowledge, this was the first qualitative grounded theory study underpinned by the
Diffusion of Innovation theory to address this gap.
This study made contributions to practice in several important areas. It provided an
understanding and identified specific barriers that primary care physicians encounter when
considering the adoption of biomarker-based diagnostic tools for Alzheimer's disease. By
elucidating these barriers through semi-structured interviews, this research provided valuable
insights into the challenges faced by physicians in integrating new diagnostic technologies into
their practice.
This study also raised awareness among primary care physicians about the importance of
biomarker-based diagnostic tools for Alzheimer's disease. By highlighting the potential benefits

31
and addressing misconceptions or concerns, it contributed to the education of healthcare providers,
empowering them to make informed decisions about the adoption of these tools in their clinical
practice. The study also raised awareness among researchers about the need for such studies as
innovations for the diagnosis and treatment of Alzheimer’s enter clinical practice.
The findings of this study can inform the development of targeted interventions aimed at
overcoming the identified barriers to the adoption of biomarker-based diagnostic tools in primary
care. By understanding the specific challenges faced by healthcare providers and providing
evidence-based insights into the barriers and facilitators of adoption in primary care, this research
can inform policy discussions, advocacy efforts, resource allocation, and coverage and
reimbursement decisions aimed at promoting the widespread implementation of these innovative
diagnostic technologies.
The results of this study also underscored the importance of interdisciplinary collaboration
between primary care physicians, specialists, researchers, policymakers, and other stakeholders in
addressing the barriers to adopting biomarker-based diagnostic tools for Alzheimer's disease.
Effective collaboration and communication across these different stakeholders is a key factor in a
more coordinated and holistic approach to improving Alzheimer's disease diagnosis and
management.
Overall, this qualitative study contributed to bridging the gap between research and practice
by identifying barriers, raising awareness, informing interventions, promoting collaboration, and
influencing healthcare policy related to the adoption of biomarker-based diagnostic tools for
Alzheimer's disease in primary care. Patients and their families can ultimately benefit from
improved and equitable access to early and accurate AD diagnosis, enabling them to make
informed decisions and access appropriate disease-modifying treatments, and support services.

32
Overview of Research Design
This study employed a qualitative grounded theory approach. Grounded theory is a
systematic methodology that allows for the generation of theory from data, making it well-suited
for investigating complex social processes and phenomena (Creswell & Creswell, 2018). The
study was underpinned by the Diffusion of Innovations theory, which provides a framework for
understanding how new ideas and technologies spread within a social system over time (Rogers,
2003).
The research design involved conducting semi-structured interviews. The interviews aimed
to capture their perspectives, experiences, and insights regarding the barriers and facilitators to the
diffusion and adoption of these innovative diagnostic tools. The study sample was drawn from
primary care physicians in Southern California identified as Internal Medicine and Family
Medicine practitioners. These included those affiliated with academic and non-academic health
systems as well as physicians in private solo or small group practice. The researcher anticipated a
sample of between 20 and 30 participants to achieve data saturation. Twenty-five primary care
physicians were interviewed.
Data collection and analysis followed an iterative process, with data being continuously
analyzed and coded as it was collected. This allowed for the identification of emerging themes and
concepts, which were then used to refine the interview questions and guide further data collection.
The constant comparative method, a hallmark of grounded theory, was employed to compare and
contrast data, identify patterns, and develop theoretical constructs (Charmaz, Constructing
Grounded Theory A Practical Guide through Qualitative Analysis, 2006).
A detailed review of the specific design of the study is provided in Chapter III.

33
Summary
This study sought to understand what barriers and challenges may prevent the effective
and efficient adoption of innovative biomarker-based diagnostic tools for AD in primary care.
Previous studies have shown that primary care physicians are at the forefront of diagnosing AD.
The rates of missed diagnosis, misdiagnosis, and delayed diagnosis are high compared to other
chronic diseases. Innovative biomarker-based diagnostic tools can enable timely and accurate
diagnosis, which is even more important, with the advent of disease-modifying treatments.
These innovative diagnostic tools need to be adopted and implemented effectively by primary
care physicians with a sense of urgency.
Four chapters follow. Chapter II is a review of the literature on the diagnosis of
Alzheimer’s disease, barriers to timely and accurate diagnosis, and innovations in diagnosis and
treatment. The review also identifies the gap in the literature related to whether, when, and how
innovative diagnostic tools will be adopted in primary care and how this study will fill this gap.
Chapter III is a detailed discussion of the research design and how the study was conducted. The
research results are presented in Chapter IV and the interpretation of the findings and
contributions to practice are presented in Chapter V.

34
Chapter II: Literature Review
There is substantial literature on the detection, diagnosis, and management of Alzheimer’s
disease, in large part due to studies that U.S. federal government agencies and those in
industrialized countries have funded and continue to fund. A major thrust of published works has
been related to barriers to the timely and accurate diagnosis of Alzheimer’s, understanding who,
where, when, and how a diagnosis is made, and disparities in the detection, diagnosis, and
management of the disease by gender, race, and ethnicity. While these previous studies offer
valuable insights, our knowledge of barriers to the adoption of innovative biomarker-based
diagnostic tools in clinical practice is limited.
In considering the literature and scholarship pertinent to this study, factors that influence
the diagnosis of Alzheimer’s disease were explored in four specific areas: (i) barriers to timely
detection and diagnosis; (ii) innovative diagnostic tools; (iii) treatments (symptom alleviating and
disease-modifying treatments); and (iv) diffusion of innovation in clinical practice. The review of
the literature in each of these areas influenced the selection of the methodology for this study and
also shaped the semi-structured interview guide.
Barriers to Timely Detection and Diagnosis
Early and timely diagnosis of Alzheimer’s disease has been studied extensively over the
past thirty years. The definitions of early and timely diagnosis have evolved and continue to
evolve, with the understanding of the disease as a continuum and the emergence and validation of
new biomarkers. Dhedhi et al. (2014) noted the emergence of a rationale for distinguishing
between “timely diagnosis” and “early diagnosis,” with the former “meaning at the right time for
the particular patient in the specific circumstances.” Dubois et al. (2016) defined timely diagnosis,
as “the diagnosis made at a time when individuals first become worried enough to seek help and

35
come to the attention of a physician.” However, the two terms tend to be used interchangeably in
the literature, particularly with the perspectives of clinicians differing from those of the research
community. For the purposes of this study, early and timely diagnosis refers to Mild Cognitive
Impairment due to AD.
Over the past thirty years, there has been a growing body of research into barriers to the
timely detection and diagnosis of Alzheimer’s disease in primary care. These studies have
identified contributing factors for high rates of undiagnosed Alzheimer’s, late diagnosis, and
misdiagnosis. These factors fall into three categories: physician-related, patient-related or
caregiver-related, and healthcare system-related (Bradford, Kunik, Schulz, Williams, & Singh,
2009).
Physician-related Factors
Of particular interest to this study were previous studies and literature related to primary
care physicians’ knowledge, perceptions, perspectives, beliefs, ethical considerations, and actual
practices for detecting and diagnosing Alzheimer’s disease or dementia. Numerous qualitative,
quantitive, and mixed methods research studies in the United States, Canada, the United Kingdom,
and other European countries provided important insights.
The lack of adequate knowledge of Alzheimer’s disease and dementia has been identified
as a contributing and persistent factor to delayed diagnosis, misdiagnosis, or lack of diagnosis by
primary care physicians. Barrett et al. (1997) conducted one of the first qualitative studies to assess
knowledge of AD among four groups of healthcare professionals (primary care physicians, nurses,
social workers, and psychologists) in the southeastern United States. Study participants were
randomly selected from lists of professional organizations, who were then mailed a 12-question
survey. Approximately 50% of primary care physicians demonstrated knowledge deficits across

36
several parameters, such as necessary tests as part of an initial evaluation of a patient complaining
of cognitive issues and procedures involved in making a diagnosis of Alzheimer’s disease. The
study’s limitations included the brevity of the survey, which did not provide for an in-depth and
comprehensive assessment of knowledge deficits. The demographic information of the
respondents was also not included in this study, and differences in knowledge based on variables
such as age, years in practice, gender, and type of practice could impact results.
Boise et al. (1999) conducted structured interviews with 78 primary care physicians as part
of 18 focus groups in Oregon and Ohio. The study investigated four possible barriers to the
recognition and diagnosis of dementia: (i) failure to recognize symptoms, (ii) lack of or limited
knowledge, (iii) negative attitude toward diagnosis and disclosure, and (iv) health system
constraints (such as time, access to diagnostic services). The study pointed to the physician’s initial
awareness of suspected dementia as being driven mostly by a patient’s family member. Barriers to
the recognition of dementia symptoms by the physician included the lack of routine screening, the
lack of time, and the brevity of communications with patients during an office visit (short-answer
questions or yes/no questions), which miss the subtlety of dementia. Several barriers to the full
assessment of patients were also identified, most notably the negative attitude of primary care
physicians toward the need for early diagnosis, specialists, and imaging tests. These sentiments
were driven by beliefs about over-diagnosing or providing a formal diagnosis for a disease without
a cure. Furthermore, there was an inclination toward entering dementia as the diagnosis in the
patient chart, versus Alzheimer’s. The stigma associated with Alzheimer’s disease was a key
challenge in disclosing a diagnosis to the patient and their family (Boise, Camicioli, Morgan, Rose,
& Congleton, 1999). This was one of the earliest qualitative studies to directly engage primary
care physicians about approaches, barriers, and challenges to diagnosing dementia. A shortcoming

37
of this study was the lack of an examination of the impact of pharmacological intervention on
diagnosing and disclosing Alzheimer’s. Donepezil (Aricept), a medication for the alleviation of
symptoms of Alzheimer’s had been clinically available since 1996.
Chodosh et al. (2004) leveraged the results from Boise et al. (1999) for a survey of three
hundred sixty-five physicians in an HMO, which provided care for the Women’s Memory Study
in Southern California. They assessed physician and patient factors related to the recognition of
dementia and cognitive impairment. The inclusion of cognitive impairment in this quantitative
study provided valuable insights into physician factors contributing to its low rates of detection as
compared to dementia. The survey’s attitudinal questions were based on the earlier qualitative
study by Boise et al. (1999). The results suggested that cognitive evaluation by a primary care
physician is not a priority in a busy clinical environment. In addition to the lack of time and the
perception that screening tools such as MMSE are time-consuming, concerns about the ability to
correctly interpret screening test results and recognize cognitive impairment and dementia were
identified as barriers. Discomfort with discussing dementia and disclosing a diagnosis was driven
by a sense of powerlessness in light of the lack of effective treatments or a cure (Chodosh, et al.,
2004). The limitations of this included physicians practicing in a managed care setting whose
patients were only women.
While primary care physicians have found disclosure of a dementia diagnosis to be
challenging, their personal opinion on whether a patient should be informed has pointed toward an
obligation and need to disclose. Studies have pointed to primary care physicians needing to be
better trained in communicating a complex disease such as dementia to their patients and family
caregivers (Mormont, et al., 2020; Koch & Illiffe, 2010; Wangler & Jansky, 2021). With our
relatively recent understanding of Alzheimer’s disease as a continuum and the paradigm shifts in

38
its diagnosis and treatment, this is an even more urgent and critical requirement. A qualitative
study by Bernstein-Sideman, et al. (2023) found that primary care physicians recognize their
evolving role as educators to help patients and their families understand the disease and its possible
progression along the continuum (Bernstein-Sideman, et al., 2023).
Identifying dementia-related topics for training and better educating primary care
physicians have also been studied. Foley et al. (2017) conducted a qualitative study and
interviewed primary care physicians, family caregivers, and people with mild cognitive
impairment. Through semi-structured interviews, five distinct educational and training topics were
elicited. These areas included detection and diagnosis practices and methodologies, disclosure
approaches and practices, knowledge about support and care services, the ability to counsel
patients and family members post-diagnosis, and management of disease symptoms, including
behavioral and psychological symptoms. In detection and diagnosis, differentiating between mild
cognitive impairment and dementia was identified as particularly challenging. A majority of
physicians also had a preference for small group workshops and online courses and resources for
the delivery of training (Foley, Boyle, Jennings, & Smithson, 2017). This study provided important
insights into specific educational and training needs of primary care physicians as related to
detecting, diagnosing, and managing Alzheimer’s dementia. The interview data from three
different stakeholders was triangulated and integrated to identify common themes and topics for
training programs to address gaps in knowledge in the diagnostic process.
Online courses related to the identification, diagnosis, and management of dementia have
been found to have a positive impact on primary care clinicians’ perceptions, capabilities, and
confidence. Bentley, et al. (2019) evaluated attitudes and approaches after four online video
training modules lasting three hours on recognizing dementia, diagnosing dementia, progression

39
of dementia, and best practices for managing dementia. Participants’ knowledge, confidence, and
attitudes about dementia were measured before and after using a 12-item questionnaire. Semistructured interviews were then scheduled with each participant at least one month after the
completion of the course, to allow time for the learnings to be consolidated and potentially
implemented in clinical practice. Among physicians, a 10% increase in knowledge of dementia,
and an 18% increase in confidence in detecting and diagnosing were observed. The interviews
corroborated the findings of the survey and provided a voice to the physician's perceptions of the
importance and value of such training in improving the diagnosis and management of dementia in
a primary care setting (Bentley, Kerr, Ginger, & Karagoz, 2019). With the advent of biomarkerbased diagnostic tools and disease-modifying treatments, new and targeted training and continuing
medical education programs can play a significant role in their effective and efficient adoption and
implementation. Our understanding of the awareness and knowledge of these transformative
developments among primary care physicians is a gap in the literature, which needs to be
addressed.
The Annual Wellness Visits available to Medicare beneficiaries since 2011 have included
a structured cognitive assessment requirement. According to one recent survey, as of 2019, only
one-half of Medicare beneficiaries had an annual wellness visit and fewer than one-third had a
structured cognitive assessment (Jacobson, Thunell, & Zissimopoulos, Cognitive Assessment At
Medicare's Annual Wellness Visit in Fee-For-Service And Medicare Advantage Plans, 2020). This
was a missed opportunity for early detection, at the Mild Cognitive Impairment stage of the
disease. The disease-modifying treatments, Aduhelm and Leqembi, which became available in
2022 and 2023 were deemed most effective at this stage of the disease. Understanding and

40
addressing the barriers to the uptake of structured cognitive assessments in primary care may lead
to the timely detection of MCI and more appropriate referrals to specialists for treatment.
Hamer et al. (2023) qualitatively and quantitatively assessed the motivations, process, and
clinical and financial value of the AWV from a primary care physician perspective. This mixedmethods study involved semi-structured interviews of 29 primary care physicians in Colorado,
combined with an analysis of quantitative Medicare claims data. The adoption of AWV by older
primary care physicians was found to be higher, with each decade since medical school graduation
accounting for a 2 percentage point increase in AWV billing. The motivations to adopt AWV
included both patient needs and financial incentives. Physicians recognized the benefits of
dedicated time for prevention and long-term planning topics, which are often overlooked in timeconstrained office visits for chronic disease management. Additionally, physicians with an
established emphasis on prevention were more likely to adopt AWV. In these cases, AWVs were
also viewed as revenue for uncompensated work that the physician was already performing. Most
AWVs were conducted with healthier patients, as identified by lower Hierarchical Condition
Category (HCC) risk scores1
. Healthier patients had fewer comorbidities and less frequent visits,
and hence, more likely to be scheduled for an AWV (Hamer, DeCamp, Bradley, Nease, &
Perraillon, 2023).
Patients’ lack of knowledge about AWV and mismatched expectations were also identified
as barriers to adoption. Clinicians expressed concern about the amount of time that they have to
spend explaining AWV to patients and differentiating it from other visits. This finding points to
the need to factor in patient education for any changes or innovations pertaining to their health
(Hamer, DeCamp, Bradley, Nease, & Perraillon, 2023).
1 HCCs are medical codes used by the Centers for Medicare and Medicaid Services (CMS) as part of a risk
adjustment model to identify individuals with serious acute or chronic conditions.

41
Concerns about workload distribution also emerged as a theme in the interviews, with
AWV viewed as not being the most optimal use of a physician’s time. AWV was viewed as a
prevention and screening visit versus care for a sick patient. Physician assistants, nurse
practitioners, or other members of the healthcare team were viewed as suitable for taking on this
responsibility. At the same time, there was an acknowledgment that patients prefer their physician
to do as much as possible for them (Hamer, DeCamp, Bradley, Nease, & Perraillon, 2023). This
study provided insights into factors such as time constraints and reimbursement when
implementing process or structural changes within a primary care practice.
Understanding the attitudes of primary care physicians toward a diagnosis of MCI is
important with the advent of innovative diagnostic tools and treatments. Sannemann et al. (2020)
surveyed 343 general practitioner physicians across five European countries. The study explored
their perceptions and practices toward early diagnosis. Early diagnosis at the MCI stage was
perceived as being valuable by 74% of the respondents. However, only 44% felt confident in the
current diagnostic tools and procedures, which included the patient’s medical history, Mini-Mental
State Examination (MMSE), and blood tests to rule out reversible causes. Brain imaging was used
by less than half of the respondents, with CT scan usage having a 4-to-1 edge over MRI. The usage
of amyloid-PET was found to be negligible. A significant difference was found between countries
in physicians having confidence in their ability to accurately diagnose MCI. Dutch general
practitioners conveyed the highest confidence and their Swedish counterparts the lowest. For
diagnosing MCI, only 10% pursued diagnosis by themselves, and almost 1/3 referred patients to a
specialist. The remainder of the respondents (approximately 60%) referred patients to specialists
while remaining engaged as part of the care team for patients with an MCI diagnosis (Sannemann,
et al., 2020). This study pointed to the persistent challenges in the accurate and timely diagnosis

42
of MCI and most likely continued underdiagnosis of cases. The study may have benefited from a
mixed-methods approach, with semi-structured interviews providing for hearing directly from the
physicians and gaining insights into these challenges.
In 2019, the Alzheimer’s Association commissioned a survey of primary care physicians,
physicians completing a residency in primary care, and recent primary care residency graduates.
The survey included 1,000 primary care physicians, 202 current primary care residents, and 200
primary care physicians within two years from the end of their residency. This large and
representative sample represented one of the key strengths of this study.
The survey revealed several key issues that may impact the adoption of innovative
diagnostic tools and disease-modifying treatments. While there was near unanimity (99%) among
all survey respondents about the importance of staying current on new developments in diagnosis,
management, and treatment, 63% of primary care physicians feel they don’t have enough time to
keep up, and 53% are only keeping up “a little or “not at all”. The three most important training
and education topics for primary care physicians are disease management and treatment (83%),
screening and testing (69%), and diagnosis (64%). A majority (53%) of physicians are confronted
multiple times per week with questions related to Alzheimer’s from their patients age 65 and older,
or their family members. (Alzheimer's Association Report, 2020). We can reasonably assume a
rise in these numbers in the future, due to several trends. Over time, an increase in public
understanding of Alzheimer’s disease as a continuum with a long asymptomatic phase can be
expected. As innovative diagnostic tools and disease-modifying treatments become widely
available, public awareness and interest can be expected to increase. In the coming years, primary
care physicians will most likely field more questions about Alzheimer’s disease from an increasing
number of their patients.

43
A significant majority (70%), of primary care physicians try to follow new developments
by scanning journals for newly published research or Continuing Medical Education (CME)
offerings. However, only 40% have completed any additional dementia training since their
residency. Furthermore, 40% are also not comfortable with making a diagnosis of Alzheimer’s or
other dementias (Alzheimer's Association Report, 2020). These findings point to inadequate
training and knowledge as a potential challenge and barrier to the adoption of new biomarkerbased diagnostic tools and disease-modifying treatments.
With its large sample size and questions related to a wide range of topics related to the
diagnosis and management of Alzheimer’s disease, the Alzheimer’s Association survey addressed
gaps in our knowledge of physicians' attitudes, confidence levels, and perceived challenges.
However, the lack of a qualitative component to this study was a weakness, and a missed
opportunity to delve deeply into actual knowledge levels or diagnostic practices of primary care
physicians. The survey was also conducted before many of the breakthroughs with blood-based
biomarkers and the availability of disease-modifying treatments, both of which have received
substantial coverage in medical, scientific, and general news media. This study’s use of semistructured interviews addresses these important gaps in our knowledge.
Ethical dilemmas have also been identified as a barrier to the timely diagnosis of
Alzheimer’s disease. The lack of a cure and the limited efficacy of symptom-relieving treatments
have caused some primary care physicians to question the benefit of routine assessment, screening
tests, and a formal diagnosis (Bandini, et al., 2022). Given the stigma associated with dementia
and Alzheimer’s, concerns about potentially causing more harm than good have been expressed.
This is particularly the case with individuals in the early stages of the disease, with mild symptoms
that are not yet interfering with daily activities. The possibility that such individuals may face

44
negative consequences in relation to employment or their independence through the loss of driving
privileges are among the ethical dilemmas cited by physicians (Koch & Illiffe, 2010).
The emergence of biomarker-based diagnostic tools has brought additional ethical
dilemmas. In 2023, the National Institute on Aging-Alzheimer’s Association (NIA-AA) released
a draft of its Revised Clinical Criteria for Alzheimer’s Disease, which was previously issued in
2018 as a guideline in a research framework. In the 2023 draft, the NIA-AA proposed expanding
the guidelines to include usage in clinical care, with biomarker-based diagnosis as the only
criterion for AD diagnosis. Several ethical and societal concerns have been raised in response to
this proposal (American Geriatrics Society, 2023).
Diagnosing Alzheimer’s disease with biomarker abnormalities alone, regardless of clinical
manifestations is expected to create myriad ethical concerns for physicians. There is substantial
research evidence that abnormal biomarker results do not necessarily result in symptomatic
disease. Furthermore, the rate of progression from asymptomatic to symptomatic disease can vary
among patients. The stigma and psychological impact on asymptomatic patients, which may be
accompanied by social and fiscal consequences, are some of the issues that may negatively impact
the adoption of biomarker-based tools by physicians. There is growing research interest in
understanding these issues and their implications for diagnosing Alzheimer’s with biomarkerbased tools. Van der Schaar, et al. (2022) conducted a systematic review of the theoretical literature
to identify considerations regarding a diagnosis of Alzheimer’s in asymptomatic individuals using
biomarkers. From 27 publications, 26 diverse and opposing considerations were identified, and
categorized as clinical, personal, and societal. Ethical dilemmas transcended all three categories
and the theoretical literature tended to focus on adverse impacts of diagnosing Alzheimer’s in the
asymptomatic phase. Studying and eliciting the perceptions, insights, and attitudes of primary care

45
physicians in relation to these ethical dilemmas is needed to prepare for the availability of
biomarker-based diagnostic tools.
Table 1 summarizes the physician-related factors as barriers to the timely and accurate
diagnosis of Alzheimer’s disease and related dementias.
Table 1: Summary of Physician-related Factors as Barriers to Timely and Accurate Diagnosis
Physician-related Factors Relevant Studies
Education and training • Lack of specific training related to
dementia
• Lack of knowledge about the
benefits of early or timely
diagnosis
• Lack of knowledge about early
symptoms
• Inconsistent use of current
diagnostic tools
• Misdiagnosis and diagnostic
uncertainty
• Lack of knowledge about new
detection and diagnosis tools and
treatments.
• Lack of confidence and comfort in
making a diagnosis
• Lack of knowledge about care
resources and coordination
• Lack of training in optimally
disclosing biomarker-based
diagnosis
(Foley, Boyle, Jennings, &
Smithson, 2017; Illiffe,
Manthorpe, & Eden, 2003; Cahill,
Maeve, Walsh, & O'Connell,
2006; Kaduszkiewicz, Bachmann,
& van den Bussche, Telling “the
truth” in dementia—Do attitude
and approach of general
practitioners and specialists
differ?, 2008; Baloch, Moss, Nair,
& Tingle, 2010; Wangler &
Jansky, 2021; Judge, Roberts,
Khandker, Ambegaonkar, &
Black, 2019; Dubois, Padovani,
Scheltens, Rossi, & Dell'Agnello,
2016; Podhorna, Winters, &
Zoebelein, 2020; Rahman-Filipak,
et al., 2023) (Mattke, et al., 2023)
Disclosure • Discomfort: difficult
conversation/topic
• Risks to patients and their family
• Lack of guidelines for disclosure
of
(Koch & Illiffe, 2010; RahmanFilipak, et al., 2023)
Clinical utility and benefit • Lack of effective treatments
• Diagnostic uncertainty with current
tools
• Shortage of specialists; long wait
times
(Hansen, Hughes, Routley, &
Robinson, 2008) (Prins, Hemke,
Pols, & Moll van Charante, 2016;
Dubois, Padovani, Scheltens,
Rossi, & Dell'Agnello, 2016)
(Mattke, et al., 2023)
Time Constraints • 15-20 minute visit not enough time
for procedures and tests related to
cognition
(Koch & Illiffe, 2010; Gransjoen,
Wiig, Bakke Lysdahl, & Morten
Hofmann, 2018; de Levante
Raphael, 2022) (Mattke, et al.,
2023)
Financial Constraints • Inadequate reimbursement (Koch & Illiffe, 2010)
Ethical Dilemmas • Concerns about stigma, attributed
by the patient or their family.
• Impact on patient’s autonomy and
capacity (long-term care insurance
premiums, driver’s licence,
employment
(Koch & Illiffe, 2010; Boise,
Camicioli, Morgan, Rose, &
Congleton, 1999; Dubois,
Padovani, Scheltens, Rossi, &
Dell'Agnello, 2016)

46
• Access to and cost of diagnostic
tests
• Access to and cost of treatments
Patient-related or Caregiver-related Factors
Studies have identified patient-related or caregiver-related factors that also impact timely
and accurate diagnosis. A review of eighteen qualitative and quantitative studies by Bradford et
al. (2009), identified five distinct factors. Patient and caregiver characteristics, such as living in a
rural area, lower levels of education, less severe functional impairment, and being unmarried as
barriers to timely diagnosis. Lack of awareness and accurate information about dementia is also
an issue for both patients and caregivers. Assumptions that cognitive changes are a normal part of
aging, perceptions about limited treatment options, and concerns about the side effects of
treatments are issues that need to be addressed.
Patient and caregiver attitudes toward dementia are also a hindrance. Patients can be in
denial about their own cognitive symptoms and are more likely to suggest or recommend a
cognitive assessment for someone else. Dementia is a feared condition, and the possibility of a
dementia diagnosis is associated with stigmas and is subject to negative emotional reactions by
both patients and caregivers (Hansen, Hughes, Routley, & Robinson, 2008). Communication
issues such as language barriers and reliance on physicians to bring up cognition as a topic were
identified in several studies. For individuals from lower socioeconomic status in both rural and
urban areas, access to physicians and care was also an issue. Specifically, this included the lack of
transportation or the need to travel a long distance for a medical appointment, and concerns over
out-of-pocket costs (Bradford, Kunik, Schulz, Williams, & Singh, 2009).
A qualitative study by Tyrrell et al. (2021) assessed the experiences of eighteen older
individuals with a cognitive assessment and a neurocognitive diagnosis. Two patient-related

47
factors emerged from the interviews: the importance of trust in the physician as being skilled and
competent in assessing dementia and neurocognitive issues; and the importance of making sense
of and understanding the cognitive diagnosis. The latter spoke to issues around the communication
leading up to the disclosure of the diagnosis. Participants characterized these conversations as
lacking in specificity, leaving them unprepared for a dementia diagnosis. A diagnosis of MCI was
interpreted by the patient as a static condition and came as a relief that it is not Alzheimer’s disease.
The former spoke to a trusting patient-physician relationship built over time, empathetic
communication, particularly around a sensitive topic like cognition and what constitutes a
cognitive assessment (Tyrrell, et al., 2021).
A persistent issue is the patient and caregiver's lack of knowledge about the disease, its
progression, and potential treatment options (Parker, Barlow, Hoe, & Aitken, 2020). While there
are many online and print informational and educational resources, qualitative studies have shown
primary care physicians have remained on the frontlines for closing this knowledge gap. Patients
and caregivers need help to understand test results and their implications for different stages of the
disease and its progression. However, primary care physicians’ lack of adequate knowledge and
time is a well-established issue. (Bernstein-Sideman, et al., 2023). With the advent of biomarkerbased innovative diagnostic tools and disease-modifying treatments, an understanding of how this
problem may be exacerbated represents a gap in the literature.
Table 2 summarizes the patient or caregiver-related factors as barriers to the timely and
accurate diagnosis of Alzheimer’s disease and related dementias.
Table 2: Summary of Patient or Caregiver-related Factors as Barriers to Timely Diagnosis
Patient or Caregiver-related Factors as
barriers to timely diagnosis
Relevant Studies
Inability to understand the diagnosis and its
implications
(Cahill, Maeve, Walsh, & O'Connell, 2006)
Reluctance to acknowledge cognitive decline /
being in denial
(Kaduszkiewicz, Bachmann, & van den Bussche, Telling “the truth”
in dementia—Do attitude and approach of general practitioners and

48
specialists differ?, 2008; Fox, et al., 2014; Jacobson, Joe, &
Zissimopoulos, Barrier to seeking care for memory problems: A
vignette study, 2022)
Feeling ashamed of cognitive decline (Kaduszkiewicz, Bachmann, & van den Bussche, Telling “the truth”
in dementia—Do attitude and approach of general practitioners and
specialists differ?, 2008; Brazil, Carter, Galway, Watson, & van der
Steen, 2015; Hansen, Hughes, Routley, & Robinson, 2008; Jacobson,
Joe, & Zissimopoulos, Barrier to seeking care for memory problems:
A vignette study, 2022)
Perceptions about limited treatment options (Bradford, Kunik, Schulz, Williams, & Singh, 2009)
Concerns about the side effects of treatments (Bradford, Kunik, Schulz, Williams, & Singh, 2009)
System-related Factors
Studies have also identified several system-related or settings-related factors as barriers to
optimal diagnosis. The lack of national guidelines for screening and diagnosis, given the various
tools, techniques, and approaches, has been identified as a key hindrance to timely diagnosis
(Baloch, Moss, Nair, & Tingle, 2010; Mansfield, et al., 2019; Mattke, et al., 2023). This has been
mentioned as one of the reasons for the slow adoption of cognitive screenings as part of the Annual
Wellness Visit for Medicare beneficiaries.
Primary care physicians also face constraints within their practice, which have been found
to be a factor in timely diagnosis. Hinton et al. (2007) conducted a qualitative study of forty
primary care physicians in Northern California. The methodology involved conducting semistructured interviews, and systematic coding of the transcriptions using NVivo software to identify
key themes. The study contributed to identifying structural and system barriers to dementia
diagnosis and care in a primary care setting. Providing adequate care to dementia patients does not
fit into the typical 15-minute office visit. Physicians in smaller group or solo practices may have
some scheduling flexibility, but those in large group practices or Health Maintenance
Organizations, tend to feel pressured to practice in a “time-efficient” manner. Once a symptomatic
patient has been diagnosed with dementia, there is a non-negligible increase in time needed for
communication with one or more family members regarding treatment plans to manage symptoms

49
and behavioral problems. Quite often, these problems tend not to be adequately addressed due to
time constraints, until they become severe and a danger to the patient or their caregiver. At this
point, physicians prescribe psychopharmacological drugs, while avoiding psychosocial
approaches, which are viewed as time-consuming. Additionally, primary care physicians viewed
specialists as being able to spend more time with these patients and were identified as better
providers of post-diagnosis care (Hinton, et al., 2007).
Surveys of physicians have identified the lack of definitive biomarkers as an issue for
making an accurate and timely diagnosis (Judge, Roberts, Khandker, Ambegaonkar, & Black,
2019; Stewart, et al., 2014). These studies provided important insights but did not identify barriers
to the adoption of these innovative diagnostic tools and where, when, and how they would be
deployed in primary care settings.
Several quantitative and qualitative studies have also identified the lack of an adequate
number of specialists as a system-related barrier to timely diagnosis. A majority of primary care
physicians in various surveys have expressed concerns about the limited access to specialists,
particularly outside large metropolitan areas and in lower socioeconomic communities. Referrals
from primary care physicians to specialists have ranged from 20 to 40% of cases, with the reason
in a majority of cases being the verification of a diagnosis and long-term co-management of
patients (Stewart, et al., 2014; Cahill, et al., 2008; Fox, et al., 2014; Bernstein-Sideman, et al.,
2023). Given the persistent shortage of specialists, a limited number of studies have examined
primary care physicians’ views and attitudes towards clear guidelines for referrals to specialists.
Further research is needed in this area, but our limited knowledge points to the perceived benefits
of guidelines as a way to optimize and improve referrals to scarce specialist resources. (Prins,
Hemke, Pols, & Moll van Charante, 2016; Bernstein-Sideman, et al., 2023). In this new era of

50
Alzheimer’s diagnosis and treatments, primary physicians will need to know which of their
patients should be referred to a specialist and the optimal timing for such referrals.
Innovative Diagnostic Tools
The field of Alzheimer's disease (AD) diagnostics has witnessed a significant shift in recent
years, with the emergence of innovative biomarker-based tools that hold the potential to
revolutionize the way the disease is detected and managed. Traditional diagnostic approaches,
which heavily relied on clinical evaluations and neuropsychological testing, have been
complemented by a suite of cutting-edge tools that enable the direct measurement and visualization
of the underlying pathological processes associated with AD. With the advent of diseasemodifying treatments, the role of biomarker-based tests has become more critical to identifying
patients in the early stages of disease and progression who are most likely to benefit from them.
With advances in Magnetic Resonance Imaging (MRI) in the early 2000s, imaging of
neurodegenerative cognitive and behavioral disorders became more acceptable in clinical practice.
Consensus guidelines among neurologists strongly support brain imaging as a first-tier approach
during the course of a thorough clinical evaluation. There is a broad preference for magnetic
resonance imaging (MRI) when available and computed tomography (CT) when not, but this has
yet to be formalized as the standard of care. Structural imaging has been found to help rule out
reversible non-neurodegenerative conditions (such as tumors, infectious, and immune-mediated
conditions) with symptoms similar to dementia. These imaging techniques can also identify
patterns of atrophy in the “AD signature” regions of the brain and increase diagnostic accuracy
(Atri, Imaging of neurodegenerative cognitive and behavioral disorders: practical considerations
for dementia clinical practice, 2016).

51
Bernstein et al. (2019) surveyed 100 primary care physicians and 50 neurologists to gain
insights into their attitudes, behaviors, experiences, and practices (including the use of imaging)
with respect to diagnosing mild cognitive impairment and dementia. The inclusion of both primary
care physicians and neurologists identified differences and similarities in diagnosis. 40% of
neurologists surveyed found brain imaging to be highly useful, as compared to 15% of primary
care physicians. 86% of primary care physicians also lacked confidence in interpreting brain
imaging findings, while 70% of neurologists were confident. Among those who ordered brain
imaging, neurologists had a clear preference for MRI (69%), while primary care physicians
preferred CT. Other barriers to the use of brain imaging by primary care physicians include the
high cost/expense of neuroimaging, the neuroimaging results not changing the diagnosis, and the
time it takes to obtain neuroimaging (Bernstein, et al., 2019).
Imaging of amyloid and tau deposition in the brain using PET has been taking place since
the mid-2000s in research settings. PET scans have shown remarkable potential in detecting AD
pathology, but their adoption in routine clinical practice has been limited, particularly in primary
care settings. The lack of disease-modifying treatments and the $3,000 to $5,000 cost associated
with a PET scan have been identified as barriers. However, the emergence of disease-modifying
treatments and recent policy changes by Medicare lifting the long-standing restrictions on the
reimbursement for PET made these tests more accessible to physicians for appropriate use in the
diagnosis (Chapleau, Iaccarino, Soleimani-Meigooni, & Rabinovici, 2022). Nonetheless, PET
scanners are expensive devices and not as readily available outside metropolitan areas as other
imaging modalities. This is a barrier to their deployment at scale for the Alzheimer’s patient
population, thereby creating inequities (Keshavan, 2021).

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Since the 1990s, Cerebrospinal fluid (CSF) biomarker analysis, measuring levels of
amyloid-beta, total tau, and phosphorylated tau, has been employed in specialized dementia clinics
and academic medical centers with expertise in interpreting these results (Schindler & Bateman,
Combining blood-based biomarkers to predict risk for Alzheimer's disease dementia, 2021).
However, the invasive nature of lumbar puncture, the limited specialized facilities and skilled
clinicians for the procedure, inadequate reimbursement from Medicare (Bonomi, Gupta, &
Schindler, 2023), and the lack of disease-modifying treatments have been barriers to the
widespread adoption of CSF biomarkers by primary care physicians. This has driven research
efforts toward developing minimally invasive blood-based biomarker panels that could enable
broader screening and diagnosis in community settings (Molineuvo, et al., 2018; Turner, Stubbs,
Davies, & Albensi, 2020).
Leveraging the validated CSF biomarkers, several blood-based protein biomarker panels
have been identified in the past ten years and are being validated for clinical use. These biomarkers
reflect the underlying pathological processes of AD, including amyloid-beta, tau, neurofilament
light chain, and various other proteins and metabolites. They are reaching accuracy and
performance levels similar to PET and CSF, with additional improvements steadily occurring.
They are expected to enable more accurate detection and diagnosis and be quickly and costeffectively deployed at scale in a primary care setting. Efforts are currently underway to validate
these biomarker panels in more diverse, community-based populations representative of primary
care settings, without which their clinical utility and adoption would be limited (Bateman,
Barthelemy, & Horie, 2020).
Given their potential for deployment at scale in a primary care setting, initial possible roles
are starting to be conceptualized and will need to be studied. Given the many barriers to timely

53
and accurate diagnosis, a starting point for the use of blood-based biomarkers could be to enable
primary care physicians to confirm a diagnosis in conjunction with current approaches (Lam,
Hlavka, & Mattke, 2019). This may reduce the time to a formal diagnosis and may also reduce
referrals to specialists. These tests may also be used as a prognostic tool for patients already
diagnosed with MCI or AD, to determine the risk of progression to AD or worsening of AD in
terms of severity. Another potential role could be as a screening tool for MCI to detect individuals
at risk for AD. In all these scenarios, primary care physicians will need to be trained on the use
and interpretation of blood-based biomarkers, given that some can be used alone and some in
combination with clinical and cognitive evaluation (Angioni, 2022). These studies have identified
ways that innovative biomarker-based tools can improve the diagnostic process in primary care
and the referral of patients to specialists. However, the voices of primary care physicians have not
been heard as to how, when, and where these tools will be adopted and implemented. This study
aimed to start filling this gap in our knowledge.
In specialist settings, blood-based biomarkers are not expected to immediately eliminate
the need for amyloid PET or CSF testing. They could be used as a quick, less expensive, and less
invasive way to determine amyloid positivity, as a pre-requisite for amyloid PET or CSF testing,
which may still needed for the assessment of the disease stage and severity. This could reduce the
number of patients needing a PET scan or CSF testing, given the barriers to their widespread
adoption. For patients proceeding to disease-modifying treatments, blood-based biomarker testing
can be used as a baseline prior to the start of therapy and then as a monitoring tool during therapy
(Peterson, 2022).
Blood-based biomarkers also have the potential to increase diversity and inclusivity in
clinical trials by facilitating more equitable access to screening and referral processes. By being

54
minimally invasive, cost-effective, and easily accessible, blood-based biomarkers can help identify
potential clinical trial participants from diverse racial, ethnic, and socioeconomic backgrounds
who may not have access to or be able to afford more invasive diagnostic procedures like PET
scans or CSF. This can broaden the pool of eligible participants beyond those who can access
specialized medical centers, making clinical trial recruitment more representative of the general
population affected by AD. By addressing barriers to screening and early detection, blood-based
biomarkers can help increase the participation of underrepresented groups in AD clinical trials,
leading to more generalizable and equitable research outcomes (Angioni, 2022)
Other than CSF and blood, research into other biofluids, such as oral, ocular, and olfactory
fluids are in their early stages. Although blood sampling is far less invasive than CSF, these
biofluids, if developed and validated for clinical use, could be another tool for primary care
physicians to detect disease in its early stages. These biomarkers could one day enable primary
care physicians to periodically screen patients deemed to be at higher risk for AD (Lee, Kim, Hong,
& Kim, 2019; Reale, Gonzales-Portillo, & Borlongan, 2019).
Genetic testing, particularly for the apolipoprotein E (APOE) ε4 allele, a known risk factor
for late-onset AD, has been increasingly used in specialized memory clinics and research settings.
With the advent and increasing adoption of direct-to-consumer genetic testing, primary care
physicians are also confronted with patients’ genetic information (Korthauer, et al., 2021).
However, the clinical utility of APOE genotyping alone is limited, as it provides information about
risk rather than a definitive diagnosis. Genetic testing is often used in conjunction with other
biomarkers and clinical assessments to provide a more comprehensive diagnostic evaluation
(Peterson, 2022).

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Despite the promising potential of these innovative diagnostic tools, their effective
implementation in primary care faces several challenges. These include the need for provider
education and training, addressing cost and reimbursement barriers, developing clear clinical
guidelines and decision support tools, and ensuring seamless integration into existing healthcare
workflows. A better understanding of these barriers and the identification of others are crucial to
realizing the benefits of early and accurate AD diagnosis in community-based primary care settings
(Angioni, 2022).
The perspectives and attitudes of patients, caregivers, and healthcare providers towards
these innovative diagnostic tools play a crucial role in their successful adoption. While patients
and caregivers may perceive benefits such as enabling early diagnosis, treatment planning, and
clinical trial access, concerns exist regarding increased distress, stigma, and novelty and
effectiveness of disease-modifying treatments. Addressing these perceptions and developing
appropriate protocols for pre-test counseling and result disclosure is essential for effective
implementation.
Current and Future Treatments
Tacrine was approved for use in the United States in 1993 as therapy forsymptoms of mildto-moderate dementia of the Alzheimer type. In 1996, the United States Food and Drug
Administration (FDA) approved Donepezil (sold under the brand name Aricept) to treat the
symptoms of Alzheimer’s disease. In the next six years, three other symptom-alleviating drugs
were approved by the FDA, including Memantine in 2003. These drugs were not able to change
the course of disease progression and, in controlled studies, showed only modest benefits for
cognition and behavior (Alzheimer's Association, 2024). Studies have shown that one of the
barriers to the timely diagnosis and disclosure of the disease has been the lack of effective

56
treatments. Sannemann et al. (2020) surveyed 343 general practitioner physicians across five
European countries to elicit their views on these drugs and their impact on the diagnosis process.
A majority of physicians surveyed saw low or no benefit from drug treatments, such as Donepezil,
and viewed them as carrying low to medium risk to patients. If a drug to slow down the progression
of AD by 30% to 50% were available, a majority (59%) would make changes to their diagnosis
processes, and another 29% would consider making such changes (Sannemann, et al., 2020). This
study pointed to the importance of effective treatments in primary care physicians' attitudes and
approaches to timely and accurate diagnosis.
In June 2021, the United States Food and Drug Administration (FDA) approved
Aducanumab (sold under the brand name Aduhelm), an anti-amyloid monoclonal antibody, as the
first disease-modifying treatment to slow the progression of Alzheimer’s disease in its early stages.
In July 2023, Lecanemab (sold under the brand name Leqembi) was approved by the FDA as
another disease-modifying treatment for early-stage disease. The appropriate use recommendation
for both drugs was for patients with a confirmed diagnosis of MCI or early Alzheimer’s using PET
or CSF. They were administered by infusion at specialized facilities and, under the supervision of
specialists, given side effects and risks such as brain swelling and bleeding (Cummings, 2023).
There are another 97 disease-modifying treatments currently in the clinical trial pipeline, some of
which may be targeted toward preclinical or asymptomatic Alzheimer’s disease (Tan, 2022).
With coverage of these disease-modifying treatments in the media, primary care physicians
are likely to be on the frontlines to address questions from their patients. Zissimopoulos, et. al.
(2022) conducted a survey of individuals from a nationally representative sample about one year
after the publicity around Aduhelm’s approval by the FDA. They found that the majority of
middle-aged and older adults did not yet have a broad understanding of this treatment, its benefits,

57
and potential risks. This study pointed to the important role of healthcare professionals in educating
their patients about innovations in the diagnosis and treatment of Alzheimer’s disease
(Zissimopoulos, Jacobson, Chen, & Borson, 2022). As patient attitudes and perceptions change,
there could be increased demand for cognitive screenings and evaluations (Tan, 2022). Strategies
for operationalizing cognitive screening during the Medicare Annual Wellness Visit, and
expanding the use of brief cognitive assessments (BCAs) to detect early-stage cognitive
impairment in primary care have been identified and recommended for adoption. These include
providing physicians with effective and efficient BCAs, integration of BCAs into the clinical
workflow, and reimbursement policies to encourage adoption (Mattke, et al., 2023; Cordell, et al.,
2013). Primary care physicians' views regarding these recommendations and potential barriers to
their adoption have not been studied and represent a gap in our knowledge.
An understanding of primary care physicians’ knowledge, opinions, and views on novel
treatments is important in the context of the adoption of new and innovative diagnostic tools. The
specific eligibility criteria for these treatments and the need for specialists to administer them are
expected to create resource allocation issues, given the limited number of specialists. This is
particularly the case for medically underserved and rural areas, where access to specialists with
relevant experience is an issue. As such, some of the burden of eligibility assessment using
biomarker-based tools can be expected to be on primary care physicians. This triaging of patients
in primary care can lead to a more referral decision to specialists for disease-modifying treatments.
The literature was limited in this area, and this study represented the first time that the perceptions
and views of primary care physicians on these new treatments were elicited in the context of timely
diagnosis using innovative tools.
Diffusion of Innovation in Clinical Practice

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Innovations in healthcare, including diagnostic tools, disseminate slowly even when
successfully implemented in one location or clinical setting. The failure to fully use available
science and technologies is harmful to patients in many ways, including but not limited to, errors
and delays in the diagnosis of diseases, suboptimal or inappropriate interventions, and higher
overall costs to patients and the healthcare system (Berwick, 2003).
The diffusion of innovation theory, developed by Everett Rogers in the 1960s, has provided
a valuable framework for understanding the adoption and spread of new practices, technologies,
or ideas within healthcare organizations, including primary care settings (Berwick, 2003; Balas &
Chapman, 2018; Carlfjord, Lindberg, Bendtsen, Nilsen, & Andersson, 2010). According to Rogers
(2003), the diffusion process involves five distinct stages: knowledge, persuasion, decision,
implementation, and confirmation, each influenced by various factors that can either facilitate or
impede the adoption of an innovation (Rogers, 2003).
The knowledge stage involves initial exposure to the innovation and an understanding of
its functions and potential benefits. During the persuasion stage, individuals or organizations form
attitudes and perceptions about the innovation, which can be influenced by factors such as personal
experiences, peer opinions, and the perceived characteristics of the innovation itself. The decision
stage involves weighing the advantages and disadvantages of adopting the innovation, leading to
a choice to either adopt or reject it. If adopted, the implementation stage follows, where the
innovation is put into practice and integrated into existing systems and processes. Finally, the
confirmation stage involves seeking reinforcement for the decision to adopt or reject the
innovation, based on the observed outcomes and experiences (Rogers, 2003; Berwick, 2003).
One of the critical factors influencing the diffusion of innovations in healthcare is the
perceived attributes of the innovation itself. These attributes include relative advantage,

59
compatibility, complexity, trialability, and observability. Relative advantage refers to the degree
to which an innovation is perceived as being better or more advantageous than the existing practice
or technology it is intended to replace. Innovations that offer clear benefits, such as improved
patient outcomes, cost-effectiveness, or increased efficiency, are more likely to be adopted.
Compatibility refers to the extent to which an innovation aligns with the values, beliefs, and
existing practices of the adopting organization or individuals. Innovations that are compatible with
the organizational culture, norms, and workflows are more readily accepted and integrated.
Complexity relates to the perceived difficulty in understanding and using the innovation.
Innovations that are relatively simple to comprehend and implement are more likely to be adopted,
while those perceived as overly complex or challenging may face resistance. Trialability refers to
the ability to experiment with the innovation on a limited basis before committing to full adoption.
Innovations that can be tested and evaluated in a controlled manner are more likely to be adopted,
as potential adopters can assess their suitability and address any concerns or issues before
widespread implementation. Finally, observability refers to the visibility of the innovation's results
and outcomes. Innovations with observable and tangible benefits are more likely to be adopted, as
potential adopters can witness the positive impacts and advantages firsthand (Berwick, 2003; Balas
& Chapman, 2018; Carlfjord, Lindberg, Bendtsen, Nilsen, & Andersson, 2010).
Third-party payers like Medicare and private insurers act as gatekeepers for the adoption
of new drugs, devices, and clinical procedures through their reimbursement policies and practices.
Their decisions on whether to cover an innovation, the amount to reimburse, and the payment
methodology (e.g. fee-for-service vs bundled payments) significantly impact provider incentives
and ability to adopt the new technology or procedure. For surgical innovations, securing
reimbursable billing codes from entities like the American Medical Association is crucial, as

60
utilization increases dramatically once provisional codes are promoted to permanent reimbursable
status - though this process can take years, delaying diffusion (Bruen, et al., 2016).
Aspects of Rogers' diffusion of innovation theory have been studied in the context of
healthcare organizations. Healthcare organizations are complex with various stakeholders,
communication channels, and decision-making processes. Their structure, the presence of opinion
leaders and change agents, and the organization's readiness for change can significantly influence
the adoption and diffusion of innovations. These studies were related to various diffusion of
innovation situations such as innovative diagnostic tools (such as the latest diagnostic imaging
systems), innovative health delivery models or clinical innovations (such as tight glucose control
in critically ill patients). (Dearing & Cox, 2018; Dorr, Cohen, & Adler-Milstein, 2017; Balas &
Chapman, 2018; Luig, Asselin, Sharma, & Campbell-Scherer, 2018). They did provide valuable
insights, applicable to this study, and pointed to the gap in our knowledge in understanding the
barriers to the adoption of innovative diagnostic tools for Alzheimer’s in primary care.
They described five categories of individuals based on their propensity to adopt new ideas
or technologies, as shown in Figure 3. The first to adopt are the Innovators (2.5%), who are
venturesome risk-takers willing to experiment with new innovations. Next are the Early Adopters
(13.5%), who are opinion leaders with higher social status and education, serving as role models
for others. The Early Majority (34%) adopt innovations after some time, being more deliberate in
their decision-making process. The Late Majority (34%) are skeptical and adopt only after the
majority has embraced the innovation. Finally, the Laggards (16%) are the last to adopt, being
resistant to change and bound by tradition. This categorization highlights how different segments
of a population vary in their readiness to adopt innovations, with the slow initial adoption by

61
innovators, followed by a rapid uptake by the early and late majorities, before tapering off with
the laggards (Dearing & Cox, 2018).
Figure 3: Distribution of adopter innovativeness based on time of adoption (Dearing & Cox,
2018)
Given the novelty of biomarker-based diagnostic tools for Alzheimer’s disease there have
been no published studies yet based on diffusion of innovation theory for their successful adoption
and implementation in primary care. Carlfjord, et. al. (2010) conducted a qualitative study based
on implementation theory to identify key factors influencing the adoption of an innovation in
primary healthcare in Sweden. Adoption of innovation was found to be positively influenced by
positive expectations, positive opinions on change and innovation, perceived advantages, an
explicit implementation strategy, and perceptions of the innovation being compatible with existing
routines.
In the context of clinical practice, the diffusion of innovations often involves multiple
levels of adoption, from organizational leaders and policymakers to healthcare professionals and
patients. This multi-level adoption process can present challenges, as different stakeholders may
have varying perceptions, motivations, and barriers to adopting the innovation. Effective
communication, education, and tailored implementation strategies are essential to address these
challenges and facilitate the diffusion process (Cain & Mittman, 2002).
Summary

62
Research studies during the past twenty years have vastly improved our knowledge of how,
when, and by whom Alzheimer’s disease is diagnosed. The important role of primary care
physicians, driven by the trusted and long relationship with their patients, and the shortage of
specialists, is well-established. Primary care physicians have accounted for diagnosing 85% of
dementia cases in the United States. We also have a better understanding of the disparities by race,
ethnicity, and gender in the detection, diagnosis, and management of this disease. Despite this, the
rates of delayed diagnosis, misdiagnosis, and undiagnosed cases remain at unacceptable levels,
particularly when compared to other diseases.
This literature review includes several quantitative and qualitative studies that have
identified the barriers to the timely and accurate diagnosis of Alzheimer’s disease. Although these
studies pre-date the advent of biomarker-based diagnostic tools and the emergence of diseasemodifying treatments, they do provide valuable insights. There is no known research that has
examined the barriers to the adoption and implementation of innovative diagnostic tools in primary
care. This study is an opportunity to fill this knowledge gap. The diffusion of innovative
biomarker-based diagnostic tools for Alzheimer’s disease and their effective and widespread
adoption by primary care physicians will be essential to better health outcomes for patients, their
families, and society at large. By identifying the barriers and challenges that physicians may
encounter, we will gain an understanding of when, how, and by whom these diagnostics will be
adopted and implemented.
A qualitative approach using grounded theory methodology, underpinned by Diffusion of
Innovation theory was used this study and is outlined in Chapter III.

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Chapter III: Research Method
Introduction
This chapter introduces the research methodology for this qualitative grounded theory
study, underpinned by the Diffusion of Innovation theory, to identify the barriers to the
implementation and adoption of new Alzheimer’s disease diagnostic tools in primary care. By
utilizing semi-structured interviews with primary care physicians, this approach allowed for a
deeper understanding of the challenges and issues that may prevent or slow down the
implementation of these tools. The chapter covers the methodology selected and the rationale for
its selection, study participants, data collection, data analysis, procedures followed, and ethical
concerns. The key limitations and challenges of the selected methodology are also presented,
including mitigation approaches.
Research Question
This study seeks to answer the following research question:
What barriers may prevent or limit primary care physicians from implementing and utilizing new
and innovative Alzheimer’s disease diagnostic and progression monitoring tools, such as bloodbased biomarker tests?
Research Design and Rationale
Qualitative Research
The review of the relevant literature identified examples of the use of qualitative,
quantitative, and mixed-method research approaches to study the barriers and challenges to early
and timely diagnosis of Alzheimer’s disease in primary care. All three approaches were evaluated
and considered for this study and research question. This was the first study focusing on the
barriers to the adoption of innovative diagnostic tools in primary care. As observed through the

64
review of the literature, this was a new topic, with major breakthroughs in both the diagnosis and
treatment of Alzheimer’s disease occurring in the past 2-3 years. My hypothesis was that a
significant number or perhaps a majority of primary care physicians may not be well informed
about these developments. This could potentially be a barrier to their willingness to participate in
surveys, which would be required for a quantitive or mixed-methods approach. A survey would
also not be an ideal approach to obtain deeper insights into a potentially new topic. Additionally,
the COVID-19 pandemic placed significant workload and time pressures on primary care
physicians, which could be another challenge to willingness to participate. A qualitative approach
was therefore selected.
Qualitative research uses words as data, collected through interviews, focus groups,
observations, or open-ended questions in surveys to understand the meanings and interpretations
people give to a social or human problem. Creswell & Creswell (2018) have identified several
characteristics of qualitative research. Five characteristics influenced the selection of qualitative
inquiry as the method for this study :
1. Natural setting: Gathering information from participants occurs at a location where
they experience the issue being studied. A natural setting may also enable participants
to spend more time with the researcher (Creswell & Creswell, 2018). This enabled me
to engage primary care physicians in a deeper conversation and capture the
complexities of their experiences, perspectives, and attitudes as factors and potential
barriers.
2. Role of the researcher as the key instrument: Qualitative researchers are in the field,
collecting the data directly through face-to-face interaction with participants.

65
Participants can share their thoughts, feelings, and ideas freely, without the constraints
of scales in a survey or other instruments.
3. Multiple sources of data: Sources of the data in qualitative research can include
interviews, observations, and documents. A research study can have one or all such
sources of data (Creswell & Creswell, 2018). In this study, semi-structured interviews
were useful in exploring complex issues such as thoughts, feelings, and views toward
diagnostic tools, knowledge and training needs, processes, managing the ever-changing
healthcare landscape, and reimbursement challenges related to Alzheimer’s disease.
This provided primary care physicians an opportunity to express themselves in their
own words. Additionally, I was able to make observations of facial expressions,
behavior, and body language, if a particular question or topic of discussion made them
more or less comfortable.
4. Inductive and deductive data analysis: Qualitative researchers review and organize the
open-ended data they have gathered to develop patterns, categories, and themes from
the bottom up. This inductive process enables the researcher to work iteratively to
achieve a comprehensive set of themes. As the process moves forward, the researcher
deductively examines the data and themes to determine if additional information should
be gathered (Creswell & Creswell, 2018). Given the exploratory nature of the research
topic and question, qualitative research provided flexibility in the study design to adapt
and make changes based on emerging findings.
5. Holistic account: By including multiple perspectives and identifying a wide range of
factors, qualitative researchers are able to create a larger picture emerging from the
data. The result is “a model of multiple factors interacting in different ways” (Creswell

66
& Creswell, 2018). Face-to-face interviews were key to understanding the context in
which biomarker-based diagnostic tools may be used for Alzheimer’s disease. These
contextual factors may influence the adoption of these tools.
Philosophical Framework
Philosophical ideas influence the practice of research. In this study, I embraced the tenets
of constructivism. Constructivism provides a robust philosophical foundation for exploring the
barriers to the adoption of innovative diagnostic tools for Alzheimer's disease in primary care.
Within this paradigm, researchers recognize that reality is not an objective, pre-determined entity
but is actively constructed through the subjective experiences and interpretations of individuals
within a specific context. In the context of investigating barriers to the adoption of diagnostic tools,
a constructivist approach invited an exploration of the diverse ways in which primary care
practitioners and stakeholders construct and make sense of the challenges associated with
integrating innovative diagnostic technologies. By acknowledging the subjective nature of these
barriers, the researcher engaged in a nuanced examination of the contextual factors, individual
perceptions, and social dynamics that shape the resistance or acceptance of these tools within
primary care settings.
Adopting a constructivist lens in the study of barriers to innovative diagnostic tool adoption
encouraged an active engagement with primary care professionals and stakeholders in the coconstruction of knowledge. The researcher, in collaboration with participants, sought to unravel
the intricate layers of meaning associated with the challenges of adopting new diagnostic tools for
Alzheimer's disease. This involved dialogue, reflexivity, and a commitment to understanding the
contexts influencing the adoption process. Through a constructivist orientation, the study aimed to
uncover not only the observable barriers but also the underlying subjective interpretations and

67
contextual nuances that contribute to the complex landscape of adopting innovative diagnostic
tools within the primary care setting.
Research Approach: Grounded Theory Methodology
Qualitative researchers have several well-established approaches or designs to consider and
select. These include narrative research, phenomenological research, grounded theory,
ethnography, and case studies (Creswell & Creswell, 2018). A grounded theory approach was used
for this qualitative study.
Grounded theory was introduced in the 1960s and, similar to other qualitative research
methods, uses the researcher as the primary instrument for data collection and analysis. It is well
suited for studying processes and provides the researcher the flexibility to simultaneously collect
and analyze data (Wertz, et al., 2011). Charmaz (2012) proposes that grounded theory enables the
qualitative researcher to move beyond addressing the “what” and “how” questions, by providing
“tools to answer “why” questions from an interpretive stance” (Charmaz, 2012).
In this study, the researcher took an inductive stance and derived meaning from interview
data to form a theory grounded in that data. The theory developed was substantive rather than
formal theory. A substantive theory provides a working theory for a specific context. Substantive
theory is not generalizable as is the case with formal theory, but it is transferrable to other contexts
with similar characteristics (Merriam & Tisdell, 2015). In this study, a substantive and working
theory was developed, specifically for the adoption of innovative Alzheimer’s disease diagnostic
tools in primary care.
The Role of the Researcher
In a grounded theory qualitative study using interviews, the role of the researcher is
dynamic and interactive. The researcher is able to get an insider's view of the participant’s

68
thoughts, feelings, and attitudes in relation to the research question. The researcher plays a crucial
role in co-constructing knowledge with participants and actively engaging in the process of data
collection, analysis, and theory development (Charmaz, 2012).
By adopting a reflexive stance, the researcher can acknowledge their own subjectivity,
biases, and preconceptions. This self-awareness is vital in understanding how the researcher's
background and perspectives might influence the study. Grounded theory encourages researchers
to continuously reflect on their positionality throughout the research process, promoting
transparency and enhancing the rigor of the study (Creswell & Creswell, 2018) (Charmaz, 2012).
The researcher takes on the role of a facilitator in the interview process. Rather than
imposing preconceived notions or hypotheses, the researcher employs a semi-structured interview
approach using open-ended and exploratory questions, allowing participants to share their
experiences and perspectives in their own words. The focus is on creating a collaborative and
trusting environment that fosters honest and rich dialogue. This facilitative role involves active
listening, probing for deeper insights, and adapting the interview guide based on emerging themes,
ensuring flexibility in response to the dynamic nature of the data.
The researcher engages in the iterative process of data analysis, coding, and theory
development. Grounded theory emphasizes constant comparison, where data collected from
interviews are compared with previous data and emerging codes to refine categories and concepts.
The researcher's role involves systematically organizing and categorizing data, identifying
patterns, and iteratively developing a grounded theory that captures the essence of participants'
experiences. Throughout this process, the researcher's active involvement is crucial in uncovering
the underlying meanings and connections within the data, contributing to the construction of a
theory grounded in participants' perspectives.

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The researcher had the skills to carry out this study. During this study, he was employed
by the University of Southern California (USC) as Assistant Vice President for Research Initiatives
within University Advancement. He has extensive experience over the past twelve years in
interviewing university faculty and researchers in relation to their work and innovations, to identify
synergies with internal and external partners and funding sources. No participants, including those
working within Keck Medicine of USC, had a direct relationship with the researcher.
Prior to his position at USC, the researcher was a member of the co-founding team and a
business development executive at a medical imaging informatics company, which developed
workflow optimization solutions to facilitate the interactions between primary care physicians and
radiologists. In this role, he interacted with primary care physicians throughout the United States
to implement innovative medical image visualization and radiologist reporting technologies.
The researcher holds a Bachelor of Science in Chemical Engineering and a Master’s degree
in Business Administration.
Study Participants
This qualitative study engaged primary care physicians practicing in Southern California,
specifically Internal Medicine or Family Practice in an outpatient setting. Geriatricians were
excluded from the study, even though some serve as primary care physicians. This decision was
informed by the recognition of the specialized nature of geriatric medicine, the relatively lower
number of geriatricians compared to Internal Medicine and Family Practice physicians, and the
desire to capture the perspectives of primary care practitioners who are more likely to be involved
in the early stages of diagnosing and managing Alzheimer's disease.
By excluding geriatricians and focusing on Internal Medicine and Family Practice, the
study aimed to shed light on the experiences and challenges faced by primary care physicians who

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are often the first point of contact for patients with concerns related to cognitive health. This
intentional exclusion allowed for a more targeted investigation into the factors influencing the
adoption of innovative diagnostic tools for Alzheimer's disease within the broader primary care
landscape in Southern California.
Inclusion criteria were established to ensure a representative sample, taking into account
the varying dynamics and challenges experienced by primary care physicians in different practice
contexts. The participant pool was intentionally diverse, encompassing professionals from various
practice settings, including academic medical centers, multi-physician practices, and solo
practitioner environments. By including physicians from academic medical centers, where
research and teaching are integral components of their roles, alongside those in private practices,
the study aimed to explore potential variations in the adoption of innovative diagnostic tools for
Alzheimer's disease within different healthcare contexts.
The participant demographics in this study reflected a gender-inclusive approach,
encompassing both male and female primary care physicians. Recognizing the importance of
gender diversity in understanding potential variations in perspectives and approaches to healthcare,
the study applied gender as one of the inclusion criteria. Additionally, a deliberate effort was made
to include participants practicing in lower socioeconomic areas, where patient populations may be
predominantly lower income. This consideration aimed to capture the unique challenges and
contextual factors faced by primary care physicians serving communities with varying
socioeconomic profiles. By incorporating both gender and socioeconomic diversity as key
inclusion criteria, the research sought to identify potential intersections and disparities in the
adoption of innovative diagnostic tools. This inclusivity within the participant pool, guided by

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predefined inclusion criteria, contributed to a more nuanced exploration of the barriers and
facilitators shaping the integration of new Alzheimer’s disease diagnostic tools.
Participants in the study were selected through purposeful sampling, considering their
expertise, practice settings, and gender as key inclusion criteria to ensure a comprehensive
exploration of the research questions. By including primary care physicians from academic
medical centers, multi-physician practices, and solo practitioner settings, as well as maintaining a
gender-inclusive approach based on predefined inclusion criteria, the study aimed to generate
insights that are contextually relevant and applicable across various primary care contexts in
Southern California. The diverse perspectives captured from this participant pool, guided by clear
inclusion criteria, contributed to the richness and depth of the study's findings, providing a holistic
understanding of the barriers and opportunities associated with the adoption of innovative
diagnostic tools for Alzheimer's disease in primary care.
Sampling Plan, Techniques, and Population
A nonprobabilistic or deliberate sampling strategy was selected for this study, as is
common in qualitative research studies. Honigmann (1982) maintains that nonprobabilistic
sampling approaches “are logical as long as the fieldworker expects mainly to use his data not to
answer questions like ‘how much’ and ‘how often’ but to solve qualitative problems, such as
discovering what occurs, the implications of what occurs, and the relationships linking
occurrences” (Honigmann, 1982, p. 130). This enables the researcher to select a sample from
which to obtain the richest information (Merriam & Tisdell, 2015).
A purposive sampling technique was used to recruit the participants for this study. By
purposefully selecting a sample, the researcher is better able to collect information most relevant
to the research question (Merriam & Tisdell, 2015). Primary care physicians in Southern

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California specializing in Internal Medicine or Family Practice were purposefully chosen, based
on the inclusion and exclusion criteria, to capture a range of experiences and viewpoints. This
intentional selection process allowed for a comprehensive exploration of the barriers to the
adoption of innovative diagnostic tools for Alzheimer's disease.
Furthermore, snowball sampling was employed as a supplementary technique to enhance
the diversity of the participant pool. Initial participants, identified through purposive sampling,
were asked to recommend other primary care physicians who might provide valuable insights into
the study's research questions. This iterative process facilitated the inclusion of participants who
might not have been initially identified through purposive sampling alone. Snowball sampling is
particularly valuable in studies where the population of interest may be challenging to access
directly, allowing for the exploration of connections and networks within the professional
community (Merriam & Tisdell, 2015). This was the case with primary care physicians, with the
researcher experiencing challenges in getting responses to emails and phone calls requesting
participation. By combining purposive and snowball sampling techniques, the study aimed to
capture a comprehensive and varied range of perspectives, ensuring a rich and contextually
grounded exploration of the barriers to the adoption of innovative diagnostic tools for Alzheimer's
disease among primary care physicians in Southern California.
The sample size in qualitative studies is generally smaller as compared to quantitative
studies. The sample size is driven by several factors, but primarily by what the researcher deems
as an adequate number of participants to address the research question. Other factors, such as
challenges in recruiting participants, and the availability of resources and time for the study, may
factor into the decision on sample size. Additionally, as the study progresses and the data is being
analyzed, participant inclusion and exclusion criteria may change. Sampling and recruiting

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additional participants should continue until data saturation has been achieved. Data saturation is
the point at which no new analytical information is obtained from new participants and the richness
of the gathered data provides a sense of closure to the study. Moser and Korstjens (2018) estimate
that a grounded theory study should require 20-30 interviews (Moser & Korstjens, 2018). In this
study, the researcher anticipated 20 to 25 participants.
Limitations of Sample Population
While the sample population in this study was carefully selected to provide a diverse and
representative range of primary care physicians in Southern California, certain limitations should
be considered. Firstly, the exclusion of geriatricians from the participant pool may have limited
the generalizability of findings to healthcare providers who specialize in geriatric medicine. Given
that geriatricians often possess unique perspectives on cognitive health and diagnostic practices
for conditions like Alzheimer's disease, their exclusion might result in an incomplete
representation of the broader healthcare landscape. Future research that specifically targets
geriatricians could offer valuable insights into the challenges and facilitators related to innovative
diagnostic tool adoption within this specialized subgroup of physicians.
Secondly, while efforts were made to include primary care physicians from various practice
settings, including academic medical centers, multi-physician practices, and solo practitioner
environments, the findings may not fully capture the experiences of those working in extremely
resource-constrained or rural settings. Physicians practicing in such environments may face
distinct challenges, such as limited access to advanced diagnostic technologies or unique socioeconomic factors influencing patient care. The absence of these perspectives in the sample may
have created a potential limitation in the study's ability to comprehensively address the full

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spectrum of barriers and opportunities related to the adoption of innovative diagnostic tools for
Alzheimer's disease in primary care.
Finally, although the study applied a gender-inclusive approach and aimed to include
physicians serving lower socioeconomic areas, the representation of certain demographic factors
within the sample might still be uneven. Variability in the gender ratio or socioeconomic status of
the participants could affect the transferability of findings to specific subgroups within the primary
care physician population. It is essential to interpret the results within the context of the sample
composition and acknowledge the potential limitations associated with any underrepresentation or
overrepresentation of certain demographic characteristics.
Data Collection
The data collection for this study involved face-to-face interviews conducted via Zoom
video, providing a platform that facilitated remote but real-time dialogue and personal interactions
with primary care physicians. The choice of Zoom video interviews was driven by practical
considerations, given the COVID-19 pandemic restrictions and challenges with scheduling inperson interviews at physician’s workplaces. This virtual format allowed for the efficient
engagement of participants while ensuring the safety and convenience of both the participant and
the researcher. All participants had prior experience with using the Zoom video platform. Every
participant kept their video camera turned on throughout the interview, thereby providing the
researcher with the essential visual and non-verbal cues necessary for an in-depth qualitative
interview. While the online format presented advantages in terms of accessibility and flexibility,
it is important to acknowledge the potential influence of digital communication on the depth of
rapport and non-verbal communication compared to in-person interactions.

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The primary instruments for data collection were the researcher and the semi-structured
interview questions tailored to elicit detailed insights from primary care participants. The
researcher prepared a set of questions to serve as a guide and to draw out the participants' thoughts,
insights, and experiences related to the study and research question. Charmaz (2006) suggests that
“well-planned open-ended questions and ready probes” (p. 29) should be designed to elicit specific
and relevant experiences related to the research question. By asking questions slowly, the
participant’s reflections may be obtained (Charmaz, Constructing Grounded Theory A Practical
Guide through Qualitative Analysis, 2006).
The researcher, equipped with expertise in qualitative research methods and a background
in healthcare, played a central role in guiding the interview process. The semi-structured interview
format allowed for flexibility, permitting the exploration of emergent themes while ensuring that
key topics related to the study's objectives were consistently addressed (Merriam & Tisdell, 2015).
The interview guide included a mix of more and less structured questions and prompts to delve
into participants' experiences, perceptions, and challenges associated with diagnosing Alzheimer’s
disease and the adoption of innovative diagnostic tools. By placing the primary care physician in
the role of key informant, the interview captured their contextualized perspectives, facilitating a
deeper understanding of the barriers and facilitators encountered in their day-to-day practice. The
combination of the researcher's expertise and the carefully crafted interview questions served as
robust instruments to extract rich and relevant data, contributing to the depth of the qualitative
analysis in this study.
Interview Process
Interviews conducted with 25 primary care physicians were the primary source of research
data for this study. The one-on-one semi-structured interviews were all conducted via Zoom video

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and recorded for verbatim transcription, with the acknowledgment and concurrence of the
participants. The participants were informed that their identity would only be known to the
researcher and not linked to their responses.
At the outset of each interview, the researcher reminded the participants of the research
topic and reiterated that their participation was voluntary. Additionally, the participants had the
option of skipping any question that they were not comfortable answering. The researcher also
discussed USC IRB’s review and approval as an exempt research study, with the signing of an
informed consent form being optional and not required. None of the participants requested written
informed consent, and the researcher received verbal consent from each participant before asking
the first question.
It was common for the participants to address multiple of these questions, without
additional prompting. With the completion of each interview, the questions were reassessed and
modified as necessary to better elicit rich information from the participants. The guiding questions
are presented in Appendix B.
Transcription Process
Each interview was recorded using Zoom’s video and audio recording capability and
QuickTime Player’s audio recording capability. The Zoom and QuickTime Player software were
both downloaded to the researcher’s computer. The electronic recording files were kept on the
researcher’s laptop, which was only available to the researcher. The laptop was at all times secured
with a password for access. A backup copy was created and kept on the researcher’s Microsoft
OneDrive password and dual-authenticated USC account. The contents of this account were only
available and visible to the researcher.

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All recordings were transcribed verbatim. The researcher used Microsoft Word’s
transcription capability and then reviewed each transcription while listening to the audio file. The
transcriptions did not have any identifying information, in order to maintain privacy. The
transcripts were emailed to respective participants for their review and edits. Eleven participants
responded that they did not have any changes. The remaining participants did not respond to the
initial or follow-up email.
Data Analysis
In a qualitative study, simultaneous data collection and analysis is a fundamental principle.
The key reasons include the opportunity to refine the interview guide based on emerging patterns
and themes, thereby contributing to richer data. The iterative nature of data collection and analysis
can also help the researcher identify the point of data saturation more efficiently (Merriam &
Tisdell, 2015).
In this study, as soon as an interview was completed, it was transcribed and initial analysis
was conducted. The process began with a careful reading of the transcript to identify initial
impressions and patterns. Annotations were made using the comments feature in Microsoft Word,
identifying key points, themes, and patterns relevant to answering the research question. This
process, called coding, is a fundamental step in qualitative data analysis. Coding is the process of
categorizing and labeling segments of the data to identify patterns, themes, and relationships
(Wertz, et al., 2011). Grounded theory coding involves analytically questioning the data rather
than describing it. Charmaz (2012) suggests questions such as “What is this data a study of?”;
“What do the data suggest?”; and “When, how, and with what consequences are participants
acting?” (Charmaz, 2012).

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Thematic analysis was applied to systematically identify, analyze, and report patterns
(themes) within the data. Initially, open coding, also called inductive coding, was applied to the
raw data. This involved line-by-line examination and identification of concepts, actions, and
interactions responsive to the research question. Initial line-by-line coding is well-suited for data
from interviews (Charmaz, Teaching Theory Construction With Initial Grounded Theory Tools:
A Reflection on Lessons and Learning, 2015). The researcher approached the data with an open
mind and an expansive view, providing both rigor and neutrality to the analysis. Through open
coding, the data was broken down into discrete units, enabling coding for processes, actions,
meanings, and themes. Charmaz (2006, p. 3) posits “coding means that we attach labels to
segments of data that depict what each segment is about. Coding distills data, sorts them, and gives
us a handle for making comparisons with other segments of data.” Open coding also serves as a
tool for reflexivity, allowing the researcher to explore personal biases, assumptions, and
preconceptions that may influence the analysis (Charmaz, Constructing Grounded Theory A
Practical Guide through Qualitative Analysis, 2006) (Merriam & Tisdell, 2015). In this study, the
researcher’s self-critical and reflexive stance during open coding guarded against beliefs and
biases from his previous experience in interacting with primary care physicians and radiologists in
relation to implementing innovative medical imaging tools.
In conjunction with initial coding, memo writing played a pivotal role in the data analysis
process. For each interview and transcript, a memo was written that captured the researcher’s
thoughts, insights, and ideas related to the codes and the research question. As data collection
progressed, the memos documented emerging patterns, tentative theories, and connections
between codes. These memos provided a valuable narrative that contributed to the development of
theoretical ideas and helped maintain a transparent record of the analytical journey. This ongoing

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process of coding and memo writing fostered an in-depth understanding of the data and guided the
researcher toward the development of a grounded theory that captures the complexities of primary
care physicians’ experiences with diagnosing Alzheimer’s disease and the implications for the
adoption of innovative diagnostic tools.
This initial coding and memo-writing phase laid the groundwork for an intensive analysis
of the data once all the interviews were completed (Merriam & Tisdell, 2015). The constant
comparison method, a hallmark of grounded theory, guided the ongoing comparison of data across
all interviews, to enhance the depth and richness of the emerging coding scheme. (Charmaz, 2012).
In this phase, identified codes and categories were refined, and more precise patterns and
relationships were elucidated. As this iterative process evolved, a codebook began to take shape
organically. The codebook is essentially a table that outlines the defined codes, their definitions,
and illustrative examples, providing a systematic framework for the next and final phase of data
analysis.
The constant dialogue between coding, memo writing, ongoing reflection, and a rigorous
and consistent analysis ensured that the theory generated from the data was grounded or rooted in
the participants’ experiences. As a result, this study provides a nuanced understanding of the
barriers and facilitators influencing the adoption of innovative diagnostic tools in primary care
settings.
Procedures Followed
The researcher submitted this study to the University of Southern California Institutional
Review Board (USC IRB) in May 2021 and received approval in July 2021. This study was
determined to be exempt from 45 CFR 46 according to §46.104(d) as category (2). As research

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was considered exempt according to §46.104(d), this project was not subject to requirements for
continuing review. The approval letter is shown in Appendix C.
The outreach to potential participants started with primary care physicians at Keck
Medicine of USC. The researcher emailed (Appendix A) the potential participants to request a
Zoom meeting to discuss barriers to the adoption of new diagnostic tools for Alzheimer’s disease.
In the introductory email, the researcher identified himself as a USC doctoral candidate and
referred to the IRB approval. A second email was sent to potential participants who expressed an
interest in participating in the study to verify that they met the inclusion criteria for the study (as
noted in the Study Participants section). Per USC IRB, a signed consent form was not required
for exempt studies. USC follows the principles of the Belmont Report, which requires all potential
participants to be informed of the research study, their rights as a participant, and the
confidentiality of their data. This was done in the initial email as well as at the start of each
interview.
All participants were interviewed via Zoom video meeting technology. The researcher set
up each Zoom meeting and sent the login information to each participant via email and a Microsoft
Outlook Calendar invitation. As the meeting host, the researcher had control over the Zoom
meeting and was able to verify that no one else had joined. The researcher was alone in a room
with a closed door, as was each participant. At the beginning of each interview, the researcher
reviewed with the participant the nature of the study, the voluntary nature of their participation,
their right to withdraw from the study at any time, and the fact that no patient or confidential
information would be requested or should be provided. The researcher also confirmed with the
participants that they were not receiving compensation for their time. The researcher asked for and
received verbal approval from each participant to record the Zoom meeting.

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Ethical Considerations
Ethical considerations in this qualitative study were carefully addressed, with the study
having received approval from the Institutional Review Board (IRB) at the University of Southern
California. The IRB determined that the research design posed minimal risks to human subjects,
leading to the classification of the study as exempt. This designation reflected the thoughtful
consideration given to participant well-being, ensuring that the research procedures and methods
were aligned with ethical standards. The exempt status indicated that the study does not involve
procedures likely to cause harm or distress to participants. However, despite the exempt
classification, ethical principles guided every phase of the research, ensuring the protection of
participants' rights, confidentiality, and overall well-being.
Given the minimal risks associated with the study, the IRB approved the use of verbal
consent as an acceptable and sufficient form of obtaining participants' agreement to participate.
Upon agreeing to participate, every participant was provided an opportunity to receive and review
a written consent form. All participants expressed a desire to proceed with only a verbal consent
before their interview. Throughout the study, a standardized verbal consent script was employed,
which provided participants with clear information about the study's purpose, procedures, potential
risks, and their rights. This approach aimed to foster transparency and maintain a respectful and
ethical relationship between the researcher and the participants.
Verbal consent discussions were documented by the researcher, capturing key elements of
the consent process. Participants were informed that they could withdraw from the study at any
point without consequence. The use of verbal consent was carefully explained to participants,
ensuring they were comfortable with the process. Ethical considerations also extended to the
handling of data, with strict confidentiality measures in place to safeguard participants' privacy.

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The research adhered to ethical guidelines, respecting the autonomy and well-being of the
participants while they contributed valuable insights to the study's research questions.
Trustworthiness
Reliability and validity are important concepts in qualitative research. Reliability refers to
the stability and consistency of the data collection and analysis process. It can be enhanced by
consistency in the recording of interviews and their verbatim transcription to ensure that the data
are accurately represented and interpreted (Merriam & Tisdell, 2015). In the context of grounded
theory, reliability is achieved through the consistent application of coding procedures and data
analysis techniques, ensuring that the findings are stable and not unduly influenced by subjective
factors (Creswell & Creswell, 2018).
Validity in qualitative research, particularly in grounded theory, is often referred to as
trustworthiness. Trustworthiness encompasses several criteria, including credibility, authenticity,
transferability, dependability, and confirmability. Credibility pertains to the accuracy and
plausibility of the findings, which can be achieved through strategies such as triangulation using
multiple sources of data, and member checks. Triangulation using multiple sources of data can be
achieved by comparing data collected from participants with different perspectives. Member check
is a validation strategy involving soliciting feedback on the preliminary findings from some of the
participants. This approach allows the researcher to validate their interpretation of the words and
experiences of the participants and address any misinterpretations or biases. Authenticity involves
ensuring that different voices and perspectives are represented in the research, while transferability
relates to the applicability of the findings to other contexts. Dependability is about establishing the
consistency and stability of the research over time, and confirmability involves addressing

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researcher biases and ensuring that the findings are supported by the data (Merriam & Tisdell,
2015).
Ensuring the trustworthiness of this grounded theory qualitative research study was
paramount. To bolster credibility, a detailed and thoughtful approach to data collection and
analysis was adopted. The plan was consistently followed through every step of the research
journey, including coding and memo writing. The use of verbatim participant quotes, and detailed
contextual descriptions in the presentation of findings contributed to the credibility of the study,
providing transparency and allowing readers to assess the alignment between the data and
interpretations. During both data collection and analysis, regular memos served as mechanisms for
the researcher to take a reflexive stance, enhancing the rigor of the study by fostering an ongoing
critical examination of assumptions and biases.
For dependability, a clear and well-documented sampling plan was implemented, guided
by predefined inclusion and exclusion criteria. The sampling strategy, which employed both
purposive and snowball sampling techniques, was outlined to ensure replicability and
transparency. Member checking, a validation technique (Merriam & Tisdell, 2015), was applied
by sharing preliminary findings with ten participants to gather their feedback. Dependability was
further strengthened by maintaining a reflexive stance, acknowledging and addressing any
potential biases of the researcher.
In terms of transferability, the detailed and contextually rich descriptions of the study
participants, settings, and research procedures were crucial. While the study was conducted by a
single researcher, the thorough presentation of the methodology, including the sampling strategy,
data collection methods, and coding procedures, enables readers to evaluate the relevance and
applicability of the findings to their own contexts. The inclusion of demographic details and

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characteristics of the participants provides readers with the necessary information to gauge the
extent to which the study's findings may be transferable to similar populations or settings. In
summary, despite being a solo researcher, a transparent and systematic approach to the research
process contributes to the overall trustworthiness of this grounded theory study.
In this study, the researcher recorded, transcribed, and manually coded all interviews. The
consistency of this approach helped ensure a closer connection with the data and, hence, participant
thoughts and intent.
In summary, reliability in grounded theory research refers to the stability and consistency
of the data analysis process, while validity is often conceptualized as trustworthiness, which
encompasses various criteria to ensure the rigor and credibility of the findings.
Limitations
While the grounded theory methodology employed in this qualitative study allows for an
in-depth exploration of participants' experiences, potential limitations are acknowledged,
particularly in the realm of sampling bias. Despite efforts to employ purposive sampling for
diversity, the exclusion of geriatricians and the focus on Internal Medicine and Family Practice
may limit the generalizability of findings. To mitigate this limitation, future research could
intentionally include geriatricians and broaden the scope to encompass a more diverse range of
primary care specialties. Additionally, transparency about the study's focus and acknowledgment
of potential subgroup variations in the findings can contribute to a nuanced interpretation of results.
The potential influence of participant attitudes and perceptions introduces another layer of
bias. To mitigate selection bias, efforts were made to enhance participant diversity and actively
recruit individuals with a range of experiences, ensuring a more representative sample. The

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researcher also used member-checking techniques during the analysis phase, presenting
preliminary findings to ten participants and seeking their input to validate interpretations.
In future research, a mixed-method approach could be employed to augment the findings
of this qualitative study. While the grounded theory methodology has provided rich insights into
the experiences and perspectives of primary care physicians regarding the adoption of innovative
diagnostic tools for Alzheimer's disease, integrating quantitative surveys may offer a broader
understanding and allow for the exploration of patterns and relationships at a larger scale. Surveys
can be designed to gather structured data on the prevalence of certain attitudes, behaviors, or
challenges related to the adoption of diagnostic tools, providing quantitative measures that
complement the qualitative depth obtained through interviews. The combination of surveys and
interviews in a mixed-method design may offer a more comprehensive view of the barriers and
facilitators in primary care settings, allowing for triangulation of findings and strengthening the
overall robustness of a study. By employing both qualitative and quantitative approaches,
researchers can capitalize on the strengths of each method to provide a more holistic understanding
of the complex factors influencing the adoption of innovative diagnostic tools for Alzheimer's
disease among primary care physicians.
In summary, while limitations exist, addressing them through thoughtful study design,
transparent reporting, and methodological adjustments can enhance the trustworthiness of the
research findings and contribute to a more nuanced understanding of the barriers to the adoption
of innovative diagnostic tools in primary care.
Summary
The objective of this chapter was to outline the research method used to answer the research
question. The methodology employed in this study aligns with a constructivist philosophical

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framework. Embracing a qualitative research approach, the study adopted a grounded theory
methodology supplemented by thematic analysis. The research design emphasized an in-depth
exploration of primary care physicians' experiences that may influence the adoption of innovative
diagnostic tools for Alzheimer's disease. Participants in the study were selected through purposeful
sampling, considering their expertise, practice settings, and gender as key inclusion criteria to
ensure a comprehensive exploration of the research questions.
Ethical considerations were paramount, with the study receiving an exempt designation
from the Institutional Review Board (IRB) due to the minimal risks posed to participants. Verbal
consent was deemed appropriate for this study, ensuring a respectful and flexible approach to
obtaining participants' agreement. The data analysis process involved systematic coding, thematic
analysis, and memo writing, facilitated by NVivo software. The utilization of a codebook derived
from the coding and memoing process ensured a structured and transparent foundation for data
analysis. This methodological approach positioned the study to uncover nuanced insights into the
barriers to the adoption of innovative diagnostic tools in primary care, contributing to the broader
understanding of practices and decision-making processes.
The goal of Chapter IV is to provide the study results and demonstrate that the methodology
outlined in this chapter was followed.

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Chapter IV: Results
This chapter contains the results of a qualitative grounded theory study, underpinned by
Diffusion of Innovation theory to identify the barriers to the implementation and adoption of new
Alzheimer’s disease diagnostic tools in primary care.
This chapter provides an overview of the methodology used in the study and discusses how
the data was gathered and analyzed. In addition, the chapter includes sample demographics, using
tables and charts to complement the summary. The process used to analyze the transcripts of the
24 interviews of primary care physicians, to identify codes and themes is described.
Overview of Methodology
The methodological framework for this study was grounded theory, a qualitative research
approach aimed at generating theory through the systematic collection and analysis of data.
Grounded theory was particularly suitable for identifying the complex, context-dependent barriers
to the adoption of biomarker-based diagnostic tools for Alzheimer’s Disease in primary care
settings. By engaging in an iterative process of data collection and analysis, grounded theory
allowed for the emergence of themes and concepts directly from the participants’ experiences and
perspectives. This approach facilitated an in-depth understanding of the underlying reasons and
mechanisms that hinder the adoption of these innovative diagnostic tools. The study employed
semi-structured interviews with 24 primary care physicians in Southern California, providing a
rich, diverse data set that was continuously analyzed using open, axial, and selective coding to
build a robust theoretical model.
To frame the analysis and interpretation of the findings, the study was underpinned by the
Diffusion of Innovation (DOI) theory (Rogers, 2003). DOI theory, proposed by Everett Rogers,
examines how innovations are adopted and spread within a social system. The theory’s key

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components, including relative advantage, compatibility, complexity, trialability, and
observability, provided a valuable lens for understanding the adoption dynamics of biomarkerbased diagnostic tools among primary care physicians.
Blood-based biomarkers for Alzheimer's Disease present a significant relative advantage
over traditional diagnostic methods, such as MMSE and MoCA, and newer ones such as PET and
CSF. The ease of administration, non-invasiveness, and potential for earlier detection make them
appealing to both physicians and patients. Unlike current tools that require more time or are
invasive, a simple blood test could streamline the diagnostic process, offering a more efficient and
accessible solution in primary care settings. However, for this advantage to be fully realized, the
biomarkers must be demonstrated to be as reliable and accurate as traditional methods.
The compatibility of blood-based biomarkers with existing medical practices varies. While
the test itself aligns with the routine procedures in primary care, the integration of this new tool
requires adjustments in diagnostic workflows and possibly additional training for physicians.
Given the complexity of Alzheimer’s disease, the assessment of the results by the physician and
review with the patient can require more knowledge, time and sensitivity, as compared with test
results for other chronic conditions. In settings and practices with more resources, such as large
hospital systems or academic institutions, the adoption of blood-based biomarkers may be more
seamless due to better infrastructure. However, in smaller or resource-constrained settings, the
need for workflow redesign and additional resources could hinder compatibility and delay
adoption.
From the perspective of primary care physicians, the complexity of blood-based
biomarkers lies not in the administration of the test, which can be expected to be straightforward,
but in the interpretation and integration of the results into clinical decision-making. If the test

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results are not easily understood or if they require specialized knowledge to interpret, this
complexity could act as a barrier to adoption. Additionally, concerns about false positives or
negatives add to the perceived complexity of relying on this new tool.
The trialability of blood-based biomarkers for Alzheimer's Disease may be somewhat
limited. In many cases, primary care physicians may not have immediate access to these tests for
experimental use or may be reluctant to trial them without strong evidence of their efficacy.
Additionally, the potential consequences of a misdiagnosis or an inaccurate result could make
physicians cautious about adopting the test on a trial basis without significant validation studies.
Finally, observability refers to the extent to which the benefits of the innovation are visible
to others. For blood-based biomarkers, their observability might initially be low, as the outcomes
of early adoption (such as improved diagnosis rates or patient outcomes) may not be immediately
apparent. However, as more physicians begin to use the test and positive results are shared within
the medical community, the observability of the benefits could increase, thereby encouraging
wider adoption.
By integrating DOI theory with grounded theory methodology, the study was able to
elucidate not only the barriers identified but also how these barriers relate to broader processes of
innovation diffusion within the healthcare context. This dual methodological approach enabled a
comprehensive exploration of the factors influencing the uptake of new diagnostic tools, offering
insights into both the immediate and systemic challenges faced by primary care physicians in their
adoption.
Study Participants
To explore the barriers to adopting biomarker-based diagnostic tools for Alzheimer's
Disease in primary care, semi-structured interviews were conducted with 24 primary care

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physicians in Southern California. Table 3 summarizes the characteristics of the study participants.
Fifteen participants, or 62% of the participants, were male, and nine, or 38%, were female. This
breakdown correlates with the latest California State Physician Workforce Report (2019), showing
62% male and 38% female (California Health Care Foundation, 2021).
The number of years in practice varied among study participants. Nine of the twenty four
participants (38%) had been practicing for 25 to 35 years. The next largest sample population was
34%, who had been in practice for 15 to 25 years. Five participants (21%) had between 5 and 15
years in practice, as shown in Figure 4.
The practice type also varied among study participants. Twelve of the twenty four
participants (50%) practiced within a large health system, such as an academic medical center, or
HMO. The next largest sample population was 29%, consisting of solo practitioners. Five
participants (21%) were part of a group practice of 5 or less physicians in a community settings.
Including primary care physicians from various practice settings—solo practice, group
practice in community settings, and practices within academic medical centers—was a key design
and approach for this study. This diversity of practice environments allowed for a more
comprehensive understanding of the barriers to adopting biomarker-based diagnostic tools for
Alzheimer's Disease. Each setting comes with unique challenges and resources, which can
significantly influence how innovations are integrated into practice. By capturing perspectives
from different types of practices, this study was able to identify not only common barriers but also
setting-specific factors that may impact the adoption process.

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Figure 4: Breakdown of study participant’s years in practice
Years in Practice
5-15 yrs 15-25 yrs 25-35 yrs 35-40 years

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Table 3: Characteristics of Study Participants
Participant 1 Male 20-25 Academic Outpatient
Participant 2 Female 20-25 Solo practice
Participant 3 Male 30-35 Solo practice
Participant 4 Male 35-40 Solo practice
Participant 5 Male 35-40 Academic Outpatient
Participant 6 Male 5-10 Community Healthcare
Participant 7 Male 30-35 Community Healthcare
Participant 8 Female 5-10 Academic Outpatient
Participant 9 Male 10-15 Academic Outpatient
Participant 10 Male 30-35 Solo affiliated with large H
Participant 11 Male 25-30 Community Healthcare
Participant 12 Male 15-20 Community Healthcare
Participant 13 Male 30-35 Multi-physician HMO
Participant 14 Female 25-30 Solo practice
Participant 15 Female 20-25 Solo Concierge
Participant 16 Female 10-15 Academic Outpatient
Participant 17 Female 15-20 Academic Outpatient
Participant 18 Male 15-20 Academic Outpatient
Participant 19 Female 25-30 Academic Outpatient
Participant 20 Female 10-15 Academic Outpatient
Participant 21 Male 10-15 Academic Outpatient
Participant 22 Female 10-15 Academic Outpatient
Participant 23 Male 30-35 Solo practice
Participant 24 Male 10-15 Community Healthcare
Participant Male/Female Years in Practice Practice Type

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Data and Analysis
Coding Process
The researcher conducted simultaneous data collection and analysis. As soon as an
interview was completed, it was transcribed, and an initial analysis was conducted. The process
began with a careful reading of the transcript to identify initial impressions and patterns.
Annotations were made using the comments feature in Microsoft Word, identifying key points,
themes, and patterns relevant to answering the research question. The iterative nature of data
collection and analysis provided the researcher the opportunity to refine the interview guide based
on emerging patterns and themes, thereby contributing to richer data. As an example, there was a
clear pattern in the first eight interviews of the importance of the availability of disease-modifying
treatments as a key driver in a physician’s pursuit of timely diagnosis. The interview guide was
adjusted to spend more time and delve deeper into the connection between treatment and diagnosis.
The iterative nature of data collection and analysis also helped identify the point of data saturation,
which occurred by the twentieth interview. The last four interviews had already been scheduled,
and I proceeded with conducting them.
The coding process for this qualitative study was guided by grounded theory methodology,
which emphasizes the development of a theory grounded in systematically gathered and analyzed
data. The coding process involved three stages, including open coding, axial coding, and selective
coding, from which code categories and themes were identified. This iterative process ensured that
the emerging categories were valid and deeply rooted in the participants' experiences and
perspectives, providing a comprehensive understanding of the barriers to the adoption of
biomarker-based diagnostic tools.

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Figure 5 shows the data coding and process used. The first stage, open coding, involved a
detailed line-by-line analysis of all 24 interview transcripts. During this stage, I identified and
labeled segments of text that represented distinct concepts or ideas. Each segment was assigned a
code that encapsulated the essence of the data. For example, responses related to the challenges of
diagnosing and disclosing Alzheimer's Disease were initially coded as “Diagnosis Complexity”
and “Disclosure Sensitivity.” Similarly, mentions of financial concerns were coded as “Inadequate
Reimbursement” and “Cost Burden.” This phase resulted in the generation of 40 initial codes that
captured various aspects of the participants' beliefs, attitudes, experiences, and perceptions as
possible barriers to the adoption of biomarker-based diagnostic tools. The list of 40 codes that
emerged from Open Coding of the 24 interview transcripts is found in Appendix D.
Figure 5: Data Coding and Analysis Process
Open Coding: Each
transcription was
coded line-by-line
manually
Axial Coding:
Grouping of open
codes into
categories, using
constant
comparative method
Selective Coding:
Grouping categories
into themes

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In the axial coding phase, I analyzed the relationships between the open codes to identify
patterns and connections. This process involved clustering related codes into broader categories,
which helped to organize the data into a more coherent structure. For instance, codes related to the
challenges of integrating new technologies into clinical practice, such as “Time Constraints,”
“Workflow Disruption,” and “Integration Concerns,” were grouped under the category of “Time
and Workflow Constraints.” Additionally, codes related to patient-related barriers, such as “Patient
Skepticism” and “Patient Education,” were clustered into a new category, “Patient-Related
Barriers.” During this phase, I also identified the significant impact of the lack of diseasemodifying treatments on the perceived value of new diagnostic tools, leading to the creation of the
“Treatment Limitations and Impact on Adoption” category, which encapsulates concerns about
the limited treatment options for Alzheimer's Disease, controversies surrounding their efficacy,
and their influence on the adoption of innovative diagnostic tools. The resulting eight categories—
(a) Clinical and Diagnostic Challenges, (b) Time and Workflow Constraints, (c) Resource and
Infrastructure Limitations, (d) Financial and Reimbursement Issues, (e) Patient-Related Barriers,
(f) Knowledge and Training Deficits, (g) Professional and Ethical Concerns, and (h) Treatment
Limitations and Challenges—offered a structured representation of the challenges expressed by
the primary care physicians in the interviews. The inclusion of the category addressing treatment
limitations and challenges highlights a critical barrier that influences the adoption of new
diagnostic technologies, as mentioned by every participant. These categories provided a nuanced
understanding of the multi-faceted barriers faced by primary care physicians in adopting new
diagnostic tools for Alzheimer’s Disease.
Table 4 lists the eight categories, their associated codes, and the description of each
category, based on statements from participants.

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Table 4: List of eight categories and associated codes
Category Associated Codes Description
Clinical and Diagnostic Challenges • Diagnosis Complexity
• Disclosure Sensitivity
• Test Reliability
• Diagnostic Accuracy
• Clinical Uncertainty
• Diagnostic Confidence
Challenges related to the complexities
and sensitivities involved in diagnosing
Alzheimer's Disease, especially with
new biomarker-based tools. It highlights
concerns about test reliability and
accuracy, as well as the difficulties in
understanding and communicating
diagnostic results to patients.
Time and Workflow Constraints • Time Constraints
• Workflow Disruption
• Integration Concerns
Issues related to the limited time
available in primary care settings and the
challenges of integrating new diagnostic
technologies into existing workflows. It
reflects how time pressures and potential
disruptions can hinder the adoption of
new tools.
Resource and Infrastructure Limitations • Resource Shortage
• Support Infrastructure
• Institutional Barriers
• Practice Resistance
Resources and infrastructure to support
the implementation of new diagnostic
technologies. It includes challenges
related to inadequate facilities, tools, and
institutional support for adopting new
innovations
Financial and Reimbursement Issues • Inadequate Reimbursement
• Reimbursement Complexity
• Cost Burden (patient out-ofpocket)
• Financial Incentives
Economic factors as barriers to the
adoption of innovative diagnostic tools.
Financial barriers including costs,
reimbursement policies, and the lack of
financial incentives.
Patient-Related Barriers • Patient Skepticism
• Patient Education
• Patient Trust Issues
• Patient Compliance Issues
• Psychological Impact
• Cultural Attitudes
Patient denial or skepticism of their
condition, psychological impact/stigma
associated with a diagnosis, patients
view of risks associated with diseasemodifying treatments, patient
willingness to adhere to treatment plan.
Lack of knowledge and training • Knowledge Deficit
• Training Gaps
• Lack of Awareness
Gaps in knowledge and among most of
the primary care physicians regarding
recent advances such as AD as a
continuum, biomarker-based diagnostic
tools and disease-modifying treatments.
It highlights the need for more education
and awareness to facilitate the adoption
of innovative diagnostic tools.
Ethical and Professional Concerns • Ethical Concerns
• Professional Skepticism
• Evidence-based Hesitation
• Perceived Risks
Professional and ethical concerns related
diagnosing patients with minimal or no
symptoms. It includes issues of
skepticism among healthcare
professionals, ethical considerations,
and the perceived risks associated with
innovative diagnostic tools.
Treatment Limitations and Impact on
Adoption
• Controversial Disease-Modifying
Treatments
• Perceived Treatment Efficacy
• Clinical Relevance Concerns
Implications of the lack of diseasemodifying treatments for Alzheimer’s
Disease. It captures concerns about the
limited therapeutic options available and
how this impacts the perceived utility
and relevance of adopting new
diagnostic technologies. The absence of
effective treatments makes the adoption

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of new diagnostic tools less compelling,
as they may not lead to improved patient
outcomes in the absence of actionable
therapeutic interventions.
The final phase of this iterative process involved refining the categories and distilling the
experiences, beliefs, and attitudes expressed by the primary care physicians into an organized
framework of categories, from which I identified six themes that addressed the research question.
The six themes that emerged from this analysis included: (a) Clinical and Diagnostic Complexities
Are Major Problems. Can Innovative Diagnostics Reduce Them?, (b) Innovative Diagnostic Tools
Can Face Systemic Constraints and Operational Barriers in Primary Care, (c) Economic and
Financial Challenges Are A Major Concern In Primary Care and Impact Decisions on New Tools
and Diagnostics, (d) Patient Perceptions, Beliefs, and Attitudes Can be Barriers to the Adoption of
New Diagnostic Tools, (e) Addressing Gaps in Physician Knowledge Related to Current
Understanding of Alzheimer’s Disease and Innovative Diagnostics is Critical, and (f) The
availability, effectiveness, and risks of disease-modifying treatments can impact the adoption of
innovative biomarker-based diagnostics. This comprehensive approach ensured that the findings
were grounded in the data and provided valuable insights into the barriers to adopting biomarkerbased diagnostic tools in primary care.
Summary of Themes and Results
The analysis of the semi-structured interviews with 24 primary care physicians in Southern
California identified six major themes that capture the potential barriers to the adoption of
biomarker-based innovative diagnostic tools for Alzheimer's Disease. These themes reflect the
multifaceted challenges primary care physicians may face as advanced diagnostic methods enter
routine clinical practice. The themes encompass a range of issues, from diagnostic and operational
complexities to economic constraints, patient-related barriers, and educational needs.

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The first theme relates to clinical and diagnostic complexities being a persistent problem.
The data suggested that if new biomarker-based diagnostics add new complexities or do not reduce
complexities and diagnostic uncertainties in clear and meaningful ways, there will be resistance
on the part of primary care physicians. Physicians expressed significant concerns about the timeconsuming nature of existing methods like MMSE and MOCA and the ethical and practical
challenges of communicating complex diagnostic information to patients.
Systemic and Operational Barriers form the second theme, focusing on the structural and
operational issues that may hinder the adoption of new diagnostic tools. Time constraints,
workflow disruptions, and inadequate resources were repeatedly mentioned as significant
impediments. These systemic barriers may affect the integration of new diagnostic tools into
already strained primary care environments.
Economic factors are encapsulated in the third theme, Economic and Financial Constraints,
which addresses issues like inadequate reimbursement, high out-of-pocket costs for patients, and
the lack of financial incentives. Physicians highlighted the financial burdens associated with
adopting new diagnostic technologies, which are often prohibitive for smaller or solo practices.
Patient-related barriers comprised the fourth theme, emphasizing the role of patient
skepticism, lack of understanding, compliance challenges, and cultural attitudes as factors that may
hinder the adoption of new diagnostic methods. These barriers underscore the importance of
patient education and cooperation in the successful implementation of innovative diagnostic tools.
The fifth theme, Knowledge Gaps, and Educational Needs, identifies the critical need for
comprehensive training and ongoing education for primary care physicians. The lack of knowledge
and training regarding advances in our understanding of AD as a continuum, new biomarker-based

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diagnostic tools, and disease-modifying treatments emerged as a significant barrier, necessitating
enhanced educational programs.
Finally, the sixth theme, Treatment Limitations and Challenges, addresses the impact of
the limited availability of disease-modifying treatments, the publicized controversies of FDA
approvals, high prices, and risks from side effects. Physicians expressed frustration over the lack
of effective treatments, which diminishes the perceived value of early diagnosis using advanced
diagnostic tools. This theme highlights the need for breakthroughs in treatment options to
complement diagnostic advancements.
These six themes provide a comprehensive overview of the potential barriers to adopting
biomarker-based innovative diagnostic tools for Alzheimer's Disease in primary care. Each theme,
discussed in detail in the subsequent sections, offers insights into the specific challenges and
potential areas for improvement as these advanced diagnostics become ready for clinical practice.
Theme 1: Clinical and Diagnostic Complexities Are Major Problems. Can Innovative
Diagnostics Reduce Them?
This theme and associated five sub-themes capture the complexities and challenges
associated with diagnosing Alzheimer's Disease using current diagnostic tools and how these may
influence decisions around the adoption of new diagnostics. The data suggested that primary care
physicians face difficulties with existing diagnostic methods like MMSE and MOCA, finding them
time-consuming and hard to apply uniformly. Differences were observed based on both practice
type and years in practice. No differences were observed between male and female physicians.
Most physicians practicing within a large health system relied on nursing staff or a physican
assistant for administering the test, and they interpreted the results and reviewed it with the patient
and/or their family member. Most physicians in smaller practices, including solo practices

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administered the test, and expressed greater dissatisfaction with the amount of time (5 to 10
minutes) they had to spend, and the challenges in applying it uniformly across all patients.
An innovative diagnostic tool, such as a blood test for Alzheimer's, may face similar
challenges but holds promise for earlier adoption in outpatient clinics affiliated with academic
institutions or large hospital systems, including HMOs. These institutions have more resources
and capabilities to integrate new tests or tools and address any workflow redesign requirements.
Furthermore, for such a diagnostic tool to be useful, it must be highly accurate, sensitive, validated,
and repeatable, with rates of false positives or false negatives consistent with blood tests for other
chronic conditions.
Sub-Themes and Illustrative Quotes
Dissatisfaction with Current Diagnostic Tools
Every participant expressed awareness and familiarity with the cognitive assessment
component of the Annual Wellness Visit for Medicare beneficiaries. 18 participants discussed
conducting a formal cognitive assessment using MMSE, Mini-Cog, or MOCA. Expressions of
comfort emanating from the use of these tools were tempered by views that they are timeconsuming and difficult to administer consistently across diverse patient populations. Most of the
participants affiliated with an academic outpatient practice only assessed and reviewed the results
of the MMSE or MOCA with their patients. In these cases, nursing staff conducted the actual test
and provided the results to the physician. Nearly all participants expressed dissatisfaction with
MMSE and MOCA, seeing them as inefficient and challenging to administer consistently.
“I generally have 15-20 minutes per patient, and I find the MMSE and MOCA timeconsuming, and tough to use consistently with every patient.” (Participant 8, Female, 5-10
years in practice, practicing in academic outpatient setting)

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“I find the MMSE lengthy and too time-consuming to score and discuss with the patient
and their family members. I tend to use the Clock Drawing Test and if I see an issue, I refer
the patient to a neurologist.” (Participant 13, Male. 30-35 years in practice, HMO
outpatient setting)
All participants were asked to reflect on diagnosing AD in the MCI stage versus later and
more severe stages. Discomfort with and lack of confidence in being able to diagnose MCI was
mentioned by most participants. The lack of a single, accurate, efficient, and cost-effective
diagnostic tool was mentioned by nearly all participants as a source of frustration. While there
was broad awareness of diagnostic tools such as PET imaging and CSF, specialists were viewed
as having the knowledge and time to order these tests and review results with patients. The results
and reports from these tests were viewed as being complex and requiring adequate training and
time to review and discuss with patients. One participant discussed how making an MCI diagnosis
makes him nervous.
“I have generally avoided making a diagnosis of MCI, as I don’t have the tools, knowledge,
or confidence needed for this. I try to refer patients who are beginning to show symptoms
to specialists, but unfortunately, that doesn’t always work, as the wait times can be long.
If I am to make an MCI diagnosis on a regular basis, I need an accurate, reliable, and
quick test.” (Participant 10, Male, 30-35 years in practice, solo practice)
While there was broad consensus on the importance of diagnosing any medical condition
early, there were expressions of concern about the value of early diagnosis of AD in the absence
of disease-modifying treatments.
“I am confident in my ability to diagnose patients with severe dementia. The problem is
MCI and I don’t think MMSE or such tests work well for that. I am aware of PET and CSF,

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but have not ordered them for any patient. If I suspect MCI, I make a referral to a
neurologist, and they may order the PET or CSF after an evaluation. I don’t have the time
or knowledge to be involved at that stage. I am also concerned about telling a patient they
have MCI if there is nothing we can do to change the trajectory of the disease.” (Participant
1, Male. 20-25 years in practice, practicing in academic outpatient setting)
Complexity of Biomarker-Based Diagnostics
Most participants lacked awareness of biomarker-based diagnostics, which is discussed in
Theme 5, below. Once informed about the progress being made in this area, nearly all expressed
concerns about such tools introducing further complexities to Alzheimer’s diagnosis. There was
broad belief that these tools would require a deeper understanding of Alzheimer’s pathology and
a sophisticated interpretation of results, which many participants felt they lacked knowledge of.
For a blood test to be clinically helpful and useful, it must be highly accurate, sensitive,
and validated, with rates of false positives or false negatives consistent with blood tests for other
chronic conditions. There should also be evidenced-based parameters about the clinical value and
utility of the test. Any ambiguities around the clinical meaning and interpretation of a positive or
negative test and the implications for disease progression would take the physician’s time and be
a barrier to adoption for nearly all the participants. Most participants expressed openness to using
such a test for screening versus diagnosis purposes, and to help prioritize and streamline referrals
to specialists. These issues are crucial barriers to the adoption of innovative diagnostic tools in
primary care and are captured in the following quotes from several participants.
“I would need to thoroughly understand these new biomarkers before I would order a test.
If they are clinically useful and don’t make things even more complicated than they are, I

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am open to using them.” (Participant 12, Male, 15-20 years in practice, small group
practice)
“I am concerned about the complexity of interpreting biomarker results and fully
understanding what they mean for the patient. Perhaps this blood test can be used to screen
patients and identify those that we need to refer to specialists.” (Participant 17, Female,
15-20 years in practice, practicing in academic outpatient setting)
“With MMSE, I know what to expect. But biomarker-based diagnostics may bring a level
of uncertainty that makes me hesitant.” (Participant 19, Female, 25-30 years in practice,
practicing in academic outpatient setting)
Need for High Accuracy and Reliability
For new diagnostic tools to be useful, they must be highly accurate, sensitive, validated,
and repeatable. Accuracy and reliability are crucial because these characteristics determine the
utility and acceptance of a diagnostic tool in clinical practice. Physicians emphasized that for a
blood test to be adopted, it must meet stringent criteria for accuracy, ensuring that results are
dependable and clinically actionable. This includes sensitivity (the ability to correctly identify
those with the disease) and specificity (the ability to correctly identify those without the disease),
as well as overall validation through extensive research and clinical trials. The diagnostic tool
should also show consistency in different settings and among various populations to ensure its
widespread applicability. Moreover, the rates of false positives (indicating the disease when it’s
not present) and false negatives (failing to detect the disease when it is present) must align with
those of other chronic condition tests to avoid causing undue alarm or false reassurance among
patients and their families.

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“The test needs to be as reliable as blood tests for other chronic conditions, with low rates
of false positives and false negatives.” (Participant 2, Female, 15-25 years in practice, solo
practice)
“If the test isn’t highly accurate and validated, it’s not going to be useful for us or our
patients.” (Participant 16, Female, 5-15 years in practice, practicing in academic
outpatient setting)
Ethical and Communication Concerns
The ethical implications of using new biomarker-based tools and the sensitivity required
in communicating a diagnosis of Alzheimer’s Disease are significant concerns. Physicians are
wary of the potential psychological impact on patients and the difficulties in explaining complex
diagnostic results. A test that detects Alzheimer’s in the asymptomatic and even the mild phase
of the disease faces ethical challenges, and the sensitivity required in communicating a diagnosis
adds to the difficulties. This is particularly the case if there are no cures or treatments, and there
is little, if anything, that a patient can do.
“I think it is unethical to give someone who doesn’t have any symptoms a diagnosis of
Alzheimer’s based on a new biomarker test. I can’t imagine how such a person would
feel, and I certainly know that I wouldn’t want to be in that situation.” (Participant 11,
Male, 25-30 years in practice, Community Healthcare small group practice)
“I worry that communicating a diagnosis that’s based on complex biomarker data will be
difficult. Patients will want to understand what the test result means, and unless I am
trained, it will be difficult for me to explain it. In that case, I think I should send patients

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to specialists.” (Participant 19, Female, 25-30 years in practice, practicing in academic
outpatient setting)
Potential for Adoption in Resource-rich Settings
Differences were observed between smaller and resource-constrained settings and larger
resource-rich settings. Resource shortages or constraints were mentioned by almost all
participants. Partcipants in solo practice or smaller group practices viewed themselves as facing
high levels of constraints and pressures. Participants in larger setting mentioned resource
contraints but acknowledged the contributions of staff and personnel to the clinical workflow. In
resource-constrained settings, the primary care physician tends to be burdened with more tasks
on a per-patient basis and does not have the time to devote attention to new tools or techniques
and altering the established workflow. These physicians tend to be followers versus early
adopters of innovation. There is a preference for others to use such tools first and work out bugs
or issues before investing time and resources in changes in their existing workflows. These
facilities have more resources, including non-clinical administrative staff looking for ways to
improve efficiency, lower costs, and improve margins, and tend to be early adopters of
innovation. Innovative diagnostic tools like a blood test for Alzheimer’s, may be adopted earlier
in resource-rich settings such as outpatient clinics in academic institutions or large hospital
systems. In these settings, decisions about the adoption of new medical technologies and
diagnostic tools are generally made jointly between clinicians and the administrative leadership
and disseminated throughout the organization.
“In our practice (outpatient clinic part of an academic setting), we have the staff and
expertise to evaluate innovations such as new diagnostics and come up with a plan and
guidelines to integrate them into clinical practice. It’s typically a collaboration between

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physicians and the administration.” (Participant 1, Male. 20-25 years in practice,
practicing in academic outpatient setting)
“I can only order tests that are in our system and approved. Also, I must typically follow
established guidelines, particularly if a test is considered costly, such as MRI or CT. For
a blood test for Alzheimer’s, the administrative leadership of (a large national HMO) will
take the lead in assessing, approving, and establishing guidelines for its use. If it can
improve patient outcomes and lower costs, I think it would get into our system quickly.”
(Participant 13, Male, 30-35 years in practice, large national HMO)
Theme 2: Innovative Diagnostic Tools Can Face Systemic Constraints and Operational
Barriers in Primary Care
This theme addresses the systemic and operational barriers to the adoption and integration
of new biomarker-based diagnostic tools, such as the PrecivityTM AD blood test by C2N
Diagnostics, into primary care settings. It includes constraints such as time pressures, workflow
disruptions, and inadequate resources, all of which contribute to the reluctance to adopt new
diagnostic tools. A significant majority of participants expressed concerns about these operational
issues and how they can directly impact the day-to-day functioning of their practice.
Primary care physicians often face time limitations, with 15 to 20 minutes allocated for a
typical office visit. Medicare patients with multiple chronic conditions typically have two to three
visits per year and take more of the physician’s time. Patients with cognitive concerns or
symptoms, take even more time. It is common for such patients to have a family member
accompanying them to office visits, which places an additional time burden on the physician.
Physicians cited the lack of time as a key reason for not doing a formal cognitive evaluation
and test. Adding a blood test for Alzheimer’s to their clinical workflow was perceived as creating

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extra work for them and their staff. An often-cited concern was the time that would need to be
allocated to address myriad patient questions about the test result, given concerns and sensitivities
about Alzheimer’s disease. A strong majority of physicians expressed a preference toward
referring patients interested in an Alzheimer’s blood test to specialists, whom they viewed as
having the time, training, and resources for a neurocognitive evaluation.
“I simply don’t have the time to add more steps to my already packed schedule, especially
taking questions from patients about results from a new test.” (Participant 4, Male, 35-40
years in practice, solo practice)
“Time is a huge factor. We’re always rushing, and my concern is that this new blood test
will just add to the workload.” (Participant 14, Female, 25-30 years in practice, solo
practice)
Workflow Disruption
The introduction of a new diagnostic tool was viewed as disrupting established workflows
and causing inefficiencies. This was particularly the case in smaller and resource-limited practices.
Existing workflows are designed around current diagnostic methods, and introducing a new tool
is viewed as potentially leading to significant changes in procedures, roles, and responsibilities.
The PrecivityTM AD blood test ordering process was explained to each participant. Almost all
participants found it cumbersome and disruptive to their workflow. Almost all physicians in solo
practice stated that this disruption would prevent them from proactively offering the test to their
patients. Physicians expressed other concerns, such as the test not being available through the
existing laboratory and testing channels, and the lack of coverage by private insurance payers,
Medicare or Medicaid. The data pointed to the need for streamlined processes for any new test to
minimize disruption.

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“Changing our workflow for a new diagnostic tool for a complex disease like Alzheimer’s
is easier said than done. If this Precivity AD blood test is not available through existing
channels such as Quest, I would not order it. A new test has to fit right into my practice,
without extra steps.” (Participant 3, Male, 30-35 years in practice, solo practice)
“We have a system for dementia patients that is not ideal or perfect, but I think it is
working for the most part. I know it is important to always try to improve things, but I am
concerned about introducing new tools without thinking about if it is practical.”
(Participant 23, Male, 30-35 years in practice, solo practice)
Resource and Infrastructure Limitations
The data suggested that limited resources and infrastructure, including financial, technical,
and human resources can be a barrier to the adoption of new diagnostic technologies. These
limitations are significant barriers in smaller solo practices compared to larger practices, including
outpatient clinics within academic settings. Most participants associated with smaller practices
stated that they lack the necessary staff and financial resources to be the first adopters of new
diagnostic tools. The costs associated with training their staff to understand and implement new
diagnostic tools and tests were a top concern. Conversely, physicians in academic settings or larger
hospital systems acknowledge access to resources and infrastructure, which could help deal with
challenges related to integrating new tools into established workflows and protocols.
“We don’t have the resources to support this new (AD) blood test. It seems like we all
would need to get trained and learn more about it.” (Participant 23, Male, 30-35 years in
practice, solo practice)
“We don’t do blood draw in our practice, and we don’t have the resources to work with
multiple different labs each time a new test comes along. If the test is not available through

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Quest, most likely, we would not order it for our patients.” (Participant 12, Male, 15-20
years in practice, Community Healthcare small group practice)
“The reality of my practice is that we’re stretched thin. Adding a complicated diagnostic
tool just isn’t feasible with our current resources.” (Participant 3, Male, 30-35 years in
practice, solo practice)
Theme 3: Economic and Financial Challenges Are A Major Concern In Primary Care and
Impact Decisions on New Tools and Diagnostics
This theme captures the economic barriers that hinder the adoption of biomarker-based
diagnostic tools. It encompasses concerns about inadequate reimbursement, the high costs
associated with new diagnostic tests, and the lack of financial incentives for primary care
physicians to adopt these tools. Financial challenges are a critical barrier because they directly
affect the feasibility and sustainability of implementing new diagnostic tools and methods in
primary care.
The data suggested three distinct barriers to the adoption of new diagnostic tools related to
reimbursement. These included adequate reimbursement for the test, adequate reimbursement for
the physician to interpret and discuss results with the patient, and adequate reimbursement for
disease-modifying treatments (DMTs). The limitations of existing DMTs as a barrier to the
adoption of innovative diagnostic tools represent Theme 6 and are discussed in detail later in this
chapter. The widespread adoption of new diagnostic tools would be significantly aided by public
policies addressing these reimbursement barriers.
All participants expressed concerns about the lack of coverage by private insurance payers,
Medicare, and Medicaid for the PrecivityTM AD blood test. A majority indicated that the lack of
coverage would make them uncomfortable in bringing the test to the attention of their patients,

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given the $1,250 cost. Less than half of the participants envisioned discussing the availability of
the test in the absence of third-party coverage if it cost no more than $250.
“If Medicare is not covering this (Precivity AD) test, I would hesitate to mention it to my
patients. The $1,250 cost would be a financial burden to most patients and it’s unclear
how they can benefit from it. If the out-of-pocket was around $100, maybe I would
recommend it, however, it’s unclear to me what we can do for patients who test positive.
Regardless, if Medicare starts covering it, that would be a strong signal for me, and, I am
sure, for others.” (Participant 3, Male, 30-35 years in practice, solo practice)
Adequate reimbursement for the physician to review, interpret, and discuss the test results
with the patient was also specifically cited by all participants as a significant concern. There was
broad consensus that an investment of time and resources for training and education was needed
to be able to understand the test results and accurately convey the meaning to patients. The
complexity of Alzheimer’s as compared to other chronic conditions was specifically mentioned by
many participants in the context of a primary care physician being financially incentivized to adopt
innovative diagnostic tools. Participant 23 best illustrated this point.
“I want to take great care of all my patients, but if I am not able to bill for interpreting and
reviewing the AD blood test result, then it becomes a financial burden on me. At some
point, if it becomes too much, I may have to refer patients to a specialist for the AD blood
test and cognitive evaluation.” (Participant 23, Male, 30-35 years in practice, solo practice)
The participant’s beliefs and attitudes toward third-party payer coverage of diseasemodifying treatments (DMTs) suggested that it is a factor in the decision to adopt new diagnostic
tools for timely diagnosis. All participants were aware of the new DMTs and viewed them as
expensive, even though covered by Medicare. None of the participants were aware of any of their

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patients taking Aduhelm or Leqembi. Only 3 participants knew the cost of these drugs and the
approximate out-of-pocket for Medicare patients. The availability of more FDA-approved and
Medicare-covered DMTs at lower costs was mentioned by most participants as influencing their
willingness to use innovative diagnostic tools and diagnose patients in the early stages of the
disease.
“The financial aspects cannot be ignored. Without adequate reimbursement for the
diagnostic, professional, and treatment components, it’s unrealistic to expect that new
diagnostic tools will be quickly adopted by all clinicians. I think it all has to be there for it
to happen.” (Participant 2, Female, 20-25 years in practice, solo practice)
Theme 4: Patient Perceptions, Beliefs, and Attitudes Can be Barriers to the Adoption of New
Diagnostic Tools.
Participants indicated that patient perceptions, beliefs, and behaviors can impact how and
when new diagnostic tools or methods are used. These included issues such as patient skepticism
toward being diagnosed with Alzheimer’s, a lack of understanding of the diagnostic process and
benefits of a timely diagnosis, and compliance challenges. Patient-related issues were recognized
as barriers, with patient cooperation and trust viewed as essential for the successful implementation
of new diagnostic methods. Participants believed that they remain at the forefront of informing
patients about new diagnostic and therapeutic developments, which is time-consuming and causes
delays for other scheduled patients.
Patient Skepticism
The data suggests that primary care physicians often encounter significant skepticism from
patients with cognitive issues. This skepticism is rooted in various factors, including fear, denial,
and a lack of understanding about dementia and Alzheimer’s. Patients and their families may be

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reluctant to accept a diagnosis due to the perceived hopelessness associated with Alzheimer's, as
a disease with no cure and its progressive, debilitating nature. Primary care physicians have
witnessed denial, where patients dismissed early symptoms or attributed them to normal aging
rather than possibly a serious neurological condition. Just under half of the participants have been
asked by patients about genetics and the risks of developing Alzheimer’s, and have expressed
interest in genetic testing.
The ability to diagnose Alzheimer's disease in its earlier stages—particularly in the
asymptomatic stage—faces additional layers of skepticism. Patients may question the validity and
reliability of these new methods, especially if they feel well, exhibit minimal or no symptoms, and
are still generally active. This skepticism is often exacerbated by the stigmas associated with
Alzheimer's. Most participants stated that many patients or their family members have expressed
fear of being labeled with Alzheimer’s. Most participants stated that they would need to take
patient’s or family members’ concerns about the implications of an early diagnosis, such as the
impact on their autonomy, relationships, and mental health into consideration when deciding on
recommending one of the new tests.
Most participants viewed addressing this skepticism as a delicate balance of providing
accurate information, offering reassurance, and empathizing with patients' fears and concerns.
Physicians recognized the need for clearly communicating the benefits of early diagnosis,
emphasizing how it can lead to better planning and potentially more effective management of the
disease. Participants recognized the need to educate patients about the reliability and significance
of new diagnostic tools and help them understand how these innovations can offer a more accurate
and earlier detection. Moreover, combating the stigma associated with Alzheimer's requires
sensitive conversations that focus on the patient's overall well-being, the importance of early

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diagnosis, and the options for managing the disease. Most participants felt that they had a trusting
relationship with their patients and could address these concerns to help reduce skepticism and
encourage patients to embrace new diagnostic advancements. The lack of time and adequate
reimbursement (discussed in Themes 2 and 3) were also mentioned frequently in this context.
“I think it was a couple of years ago that patients started asking me about imaging tests
for AD, and now, in the past year, I started getting asked about the blood test and the new
drugs. There is definitely curiosity and interest, but very few patients have wanted to follow
through and get tested. Most people seem skeptical about these new tests and the drugs
and often question me about risks and potential downsides.” (Participant 17, Female, 15-
20 years in practice, practicing in academic outpatient setting)
“No one wants to hear that they are being diagnosed with Alzheimer’s, and I see the fear
in patients with even the slightest symptoms. Being in an academic setting, I know that we
are turning the corner with this horrible disease. Communicating the hope to patients in a
way that they can understand it is key. I don’t think it is happening to the extent that it
should, and you can see it in the doubts among patients.” (Participant 19, Female, 25-30
years in practice, practicing in academic outpatient setting)
Lack of Understanding
The data suggested that a significant barrier to the adoption of new diagnostic tools in
primary care is patients' general lack of understanding and limited knowledge about chronic
diseases, including Alzheimer’s. This lack of understanding can lead to misconceptions and
underestimation of the importance of early detection and ongoing management. Three participants
cited Type 2 diabetes as an example, where a lack of understanding and knowledge leads to
avoidable late detection, diagnosis, and interventions. In the case of Alzheimer’s, some patients

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may not grasp that Alzheimer's is a progressive neurological disease that starts with mild
symptoms and gradually worsens over time. They might think that forgetfulness or cognitive
lapses are just part of normal aging and not indicative of a serious underlying condition.
Additionally, patients may have a limited understanding of Alzheimer's, believing that a definitive
diagnosis cannot be made until severe symptoms manifest, which contributes to their skepticism
about early detection and intervention.
This gap in understanding affects patients' attitudes towards new diagnostic tools. The
coverage of new diagnostic tools, such as a blood test and the new DMTs, in the mainstream media,
was viewed as helping to spur discussions with patients on these issues and concerns. All
participants had patients who brought up either the AD blood test, the DMTs, or both during an
office visit. Several participants shared that most of their patients may be willing to undergo an
AD blood test if they were adequately informed about the meaning and significance of the result,
and how it differs from traditional methods. Almost half of the participants believed that they
lacked adequate knowledge to address patient questions on these topics. Several participants
affiliated with academic medical settings were aware of online informational and educational
resources and printed pamphlets from their institution and others, such as the Alzheimer’s
Association. Encouraging patients to use these resources was seen as one way to address the gap
in patient knowledge.
“A lot of my patients just don’t understand what Alzheimer’s really is. They think it’s just
a part of getting old, and they don’t see the point of early testing. If the new blood test is
reliable and accurate, then we need to make sure that patients understand how it can help
them in dealing with the disease. At (academic medical institution), we have a lot of

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relevant information in print and online, which we share with patients.” (Participant 5,
Male, 35-40 years in practice, practicing in academic outpatient setting)
“The more my patients understand and are knowledgeable about a disease like
Alzheimer’s, the better the outcomes. I think that would also apply to the adoption of new
diagnostics.” (Participant 12)
Patient Follow-through
The data suggested that patient compliance with prescribed diagnostic testing, such as
blood tests or imaging, is, at times, a challenge. Patients who feel healthy, lack symptoms, and
perceive their condition as not being serious tend to de-prioritize these tests. This is particularly
observed for tests requiring a separate appointment, several hours of fasting, and an empty
stomach. The data suggested physicians, or their staff have to repeatedly remind and encourage
such patients to complete these essential diagnostic steps, emphasizing their importance in
understanding and managing their health conditions. This lack of compliance may be problematic
for testing early-stage Alzheimer’s when the symptoms are mild.
“I have patients who simply avoid going to the lab or imaging center. They’re worried
about the cost or the time it takes, or they’re just scared of what we might find. It’s a big
hurdle in getting the full picture of their health.” (Participant 11, Male, 25-30 years in
practice, community healthcare small group practice)
Theme 5: Addressing Gaps in Physician Knowledge Related to Current Understanding of
Alzheimer’s Disease and Innovative Diagnostics is Critical
This theme focuses on the significant knowledge gaps among primary care physicians as
it relates to innovative biomarker-based diagnostic tools. Many physicians are unaware of the latest
advancements in diagnostic technologies, highlighting the need for increased awareness and

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education to promote adoption. Beyond PET and CSF, most participants were not aware of other
innovative biomarker-based tests. Only ten participants were aware of the recent advances in blood
tests and had read about them in Medscape.com (an online portal for physicians and healthcare
professionals), heard about them from a colleague, or in the mainstream media. These ten
participants could only provide high-level general information about blood tests for AD, such as
measuring levels of amyloid and tau proteins. All participants were asked about the
PrecivityADTM blood test by C2N Diagnostics, and none had heard about it.
There was also a significant knowledge deficit about the Alzheimer’s disease continuum,
the asymptomatic phase of the disease, and distinctions between early and timely diagnosis. None
of the participants were aware of the long asymptomatic phase of the disease, and that detection
and diagnosis at that stage is now possible. All participants characterized late-onset Alzheimer’s
as an old person’s disease instead of as a middle-aged disease, with symptoms manifesting in later
years and old age. All participants expressed a lack of confidence in their ability to discuss and
explain AD pathology. None of the participants were aware of the criteria proposed by the
Alzheimer’s Association workgroup to diagnose and stage AD biologically. Several participants
acknowledged the gap in training and expressed confidence in being able to gain the knowledge
needed for using these new tools.
“I wasn’t even aware of some of these new diagnostic tools until recently. I think I need to
become more knowledgeable about the advances in Alzheimer’s, but it’s a matter of finding
the time.” (Participant 3, Male, 30-35 years in practice, solo practice)
“I don’t feel confident using these new diagnostic tools because I lack the knowledge. I am
hoping that it’s something that a CME course or two can fix.” (Participant 10)

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Theme 6: The availability, effectiveness, and risks of disease-modifying treatments can
impact the adoption of innovative biomarker-based diagnostics
This theme focuses on the concerns expressed by the participants about the availability of
effective DMTs and their impact on the timely diagnosis of Alzheimer’s. All participants were
aware of drugs, such as Donepezil, as symptomatic treatments, and most had prescribed it to
patients. Most participants viewed these drugs as a short-term intervention with limited efficacy
for most patients. All participants were aware of and expressed frustration about the many setbacks
in the research for treatments for Alzheimer’s. This state of drug intervention options and the lack
of DMTs were cited by most participants as reasons for viewing a formal diagnosis as not having
clinical value, even though it could help patients and their families with planning and caregiving
matters.
Most participants viewed the availability of effective and safe DMTs as an important
motivating factor for a timely diagnosis, using new and more accurate diagnostic technologies. All
participants were aware of Aduhelm and Leqembi as the first DMTs for Alzheimer’s but did not
know of any patients undergoing treatment. This was particularly noteworthy as seven of
participants were interviewed at a time when the health system they were associated with was
offering Leqembi. The controversies about their efficacy, risks, side effects, and the FDA approval
process, particularly for Aduhelm, were mentioned by all participants and cited as factors that
could give pause to both patients and physicians.
The data suggested that most primary care physicians look to specialists for their views
and opinions on new treatments and drugs for complex diseases such as Alzheimer’s. Several
participants had discussed these DMTs with specialists and noted the lack of consensus around
their efficacy and benefits, but broad concerns around risks and side effects. Almost all participants

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expressed trust in the specialists to whom they make referrals. Their assessments of DMTs were
viewed as being a key factor in how these new drugs would be perceived in primary care and
communicated to patients. This finding highlights an interesting dynamic, given that 85% of
Alzheimer’s cases are diagnosed in primary care and less than a third of those patients see a
specialist (Drabo, et al., 2019). This reliance on specialists for guidance on the latest innovations,
such as disease-modifying treatments like Leqembi underscores the gap in knowledge and
confidence that some primary care physicians feel in staying current with rapidly evolving
treatment options and diagnostic advancements. It also points to the need for better integration of
specialist insights into primary care, ensuring that primary care physicians are equipped with the
necessary information and support to make informed decisions about adopting new tools and
treatments, thus bridging the gap between initial diagnosis and advanced care.
Most participants expressed hope and optimism about improvements in outcomes for
Alzheimer’s patients, but this was tempered by views that drugs with greater efficacy and with
fewer and less severe side effects are needed. The risk profile of the current DMTs precludes them
from being prescribed by primary care physicians, a point that was emphasized by nearly all
participants. The data suggested that the shortage of specialists may create bottlenecks if more
patients are diagnosed with MCI in primary care with new diagnostic tools and referred to
specialists for further evaluation and treatment. This predicament could make the adoption of new
diagnostic tools in primary care a less urgent and compelling proposition. There are serious
concerns about creating a backlog of disappointed primary care patients who would not be able to
see a specialist in a timely manner, despite the results of their biomarker-based test(s).
All participants expected to remain involved in detecting and diagnosing Alzheimer’s, but
the limitations of current DMTs and the lack of safe and effective treatments that can prescribed

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by both specialists and primary care physicians represented a significant issue and concern. These
concerns can slow the adoption of biomarker-based diagnostic tools in primary care.
“Even if we were able to diagnose earlier, it seems to me that we’re limited by the treatments we
have, which are not very effective when you also consider the risks to the patient.” (Participant 7,
Male, 30-35 years in practice, community healthcare small group practice)
“The efficacy of current treatments is a big concern. This makes new diagnostic tools seem less
clinically useful, on top of other concerns I have about new tests.” (Participant 11, Male, 25-30
years in practice, community healthcare small group practice)
“I know several specialists who are concerned about being able to manage the side effects of the
new meds. Patients can be at risk of brain swelling and bleeding. Who are the specialists willing
to prescribe these meds? There are way too many unknowns.” (Participant 4, Male, 35-40 years
in practice, solo practice)
These six themes captured comprehensively the barriers identified in this study, each
supported by detailed descriptions, sub-themes (where applicable), and illustrative and direct
quotes from interviews with participants. They captured the multifaceted challenges that primary
care physicians face in adopting biomarker-based diagnostic tools for Alzheimer’s Disease.
The six themes were also examined in relation to the demographics of the participants.
Table 5 summarizes these findings. The analysis revealed that certain demographic factors, such
as gender, years in practice, and type of practice factor into the barriers to adopting innovative
biomarker-based diagnostics for Alzheimer's disease.
Male and female physicians exhibited different priorities and concerns across the themes.
Male physicians tended to emphasize practical and clinical outcomes, particularly focusing on the
accuracy and reliability of new diagnostic tools. They were more inclined to adopt such tools if

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they felt confident in their utility and alignment with available treatments. In contrast, female
physicians were more concerned with patient communication, ethical considerations, and the
psychological impact of diagnosing a disease that currently has limited treatment options.
The analysis also highlighted how experience shaped perspectives on the adoption of new
diagnostic tools. Physicians earlier in their professional careers (5-15 years in practice) were
generally more optimistic and open to incorporating innovative diagnostics into their practice,
viewing them as a potential improvement in patient care. However, those with more experience
(25-40 years) exhibited greater caution, drawing from past experiences where new tools did not
significantly alter clinical outcomes. This group emphasized the need for robust clinical evidence
and clear guidelines before adopting new technologies, reflecting a more measured approach to
innovation.
The type of practice also played a critical role in shaping physicians’ views. Those in solo
practices were particularly concerned with the cost-effectiveness and practicality of adopting new
diagnostic tools, given their limited resources. Small group practices, while also cautious, were
more open to adoption if they could access specialist support and clear clinical guidelines. In
contrast, physicians within large health systems or HMOs were more willing to integrate new
diagnostics, provided these tools were part of a broader care framework that included collaboration
with specialists and access to disease-modifying treatments. These findings suggest that systemic
and operational factors significantly influence the adoption of innovative diagnostics.
In summary, the examination of the six themes in relation to participant demographics
revealed nuanced differences in how primary care physicians perceive and respond to innovative
biomarker-based diagnostics for Alzheimer's disease. These findings highlight the importance of
considering demographic factors when developing strategies to promote the adoption of new

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diagnostic tools in primary care settings. By addressing the specific concerns and needs of different
physician groups, efforts to integrate these innovations into clinical practice can be more
effectively tailored and implemented.
Table 5: The Relationship Between the Six Themes and Participant Characteristics
Theme Gender Breakdown Years in Practice Practice Type
Clinical and Diagnostic
Complexities Are Major
Problems. Can Innovative
Diagnostics Reduce Them?
Male participants
tended to focus more on
the technical aspects of
diagnostics, while
female participants
often emphasized the
patient communication
challenges involved in
disclosing diagnoses.
Physicians with 25-35
years in practice were
more likely to discuss
diagnostic complexities,
Physicians in solo practice
were more likely to
express frustrations at the
complexities of AD
diagnosis and to take a
cautious stance toward
integrating new
diagnostics.
Innovative Diagnostic Tools
Can Face Systemic
Constraints and Operational
Barriers in Primary Care
Both genders discussed
systemic barriers, with
male physicians more
often cited
administrative burdens,
while female physicians
highlighted the impact
of these barriers on
patient care.
Those with 15-25 years
of experience frequently
mentioned systemic
barriers, reflecting a
middle-career position
where they may be
more involved in
administrative and
operational decisionmaking.
Solo and small group
practitioners frequently
highlighted operational
barriers, including
administrative burdens
and limited support staff,
which make adopting new
tools more difficult.
Economic and Financial
Challenges Are a Major
Concern In Primary Care
and Impact Decisions on
New Tools and Diagnostics
Both genders discussed
economic and financial
challenges in primary
care and its impact.
Male participants
discussed in more
detail.
Significant issue for
older participants (25 or
more years of practice).
Reimbursements from
third-party not keeping
up with expenses and
workload. Slightly less
so for younger
participants.
Less of an issue for
participants in large health
systems, Significant issue
for solo practitioners.
Patient Perceptions, Beliefs,
and Attitudes Can be
Barriers to the Adoption of
New Diagnostic Tools
No major differences
observed.
Younger physicians (5-
15 years in practice)
often highlighted
patient-related
challenges, perhaps
reflecting their more
recent training and
emphasis on patientcentered care.
Mentioned across all
practice types, but
physicians in solo
practices emphasized the
difficulty of addressing
patient challenges with
limited resources and busy
staff.
Addressing Gaps in
Physician Knowledge
Related to Current
Understanding of
Alzheimer’s Disease and
Innovative Diagnostics is
Critical
No major differences
observed
Those with 35-40 years
of experience had little
familiarity with the
latest innovations.
Younger participants
were better informed of
latest advances in AD
diagnosis and exuded
more confidence in
their abilities to detect
and diagnose.
All practice types cited the
need for more training in
AD. Large health systems
were noted as having
access to continuing
education and specialist
consultations, while solo
practitioners noted the
difficulty in staying
updated with the latest

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advancements due to time
and resource constraints.
The availability,
effectiveness, and risks of
disease-modifying
treatments can impact the
adoption of innovative
biomarker-based diagnostics
Female participants had
a slightly stronger
commitment to
providing a diagnosis
even if treatments are
still limited. Male
participants tended to be
more focused on the
practical and clinical
outcomes, male
physicians stressed the
importance of having
effective treatments
before fully embracing
new diagnostic tools.
They were concerned
that without effective
disease-modifying
treatments, the utility of
these diagnostics might
be limited.
Younger participants
(5-15 years in practice)
were more optimistic
and willing to adopt
new diagnostic tools,
hoping that these will
soon align with
emerging treatments.
Those with 15-25 years
in practice balanced
optimism with caution,
wanting more evidence
and clearer guidance on
integrating diagnostics
with current treatments.
Most older participants
expressed skepticism
based on what they had
heard about treatments
and would take a more
cautious approach.
Solo practitioners
expressed the most
concerns about safety/side
effects and efficacy of
new treatments. Many in
large health systems had
discussed DMTs with a
specialist colleague.
Summary
This chapter provided the results of this ground theory study underpinned by the Diffusion
of Innovation Theory. The results revealed several key barriers to the adoption of biomarker-based
innovative diagnostic tools for Alzheimer's disease in primary care settings. The data highlighted
significant challenges related to clinical and diagnostic complexities. These included concerns
about the accuracy, reliability, and integration of new tools like blood-based biomarkers into
existing workflows. The need for these tools to be highly validated and reproducible, along with
the complexity of incorporating them into routine practice, was consistently emphasized.
Physicians also pointed to the challenges associated with the time-consuming nature of current
diagnostic methods, such as the Mini-Mental State Examination (MMSE) or the Montreal
Cognitive Assessment (MoCA), and expressed skepticism about the ease of adoption for newer
tools.

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Systemic and operational barriers were another significant theme identified in the study.
Physicians practicing in various settings, from solo practices to large health systems, highlighted
differences in resource availability, infrastructure, and the ability to implement new diagnostic
tools. Those in smaller or resource-limited practices expressed concerns about the financial and
logistical burden of adopting innovative diagnostics. In contrast, those in larger, more integrated
systems were more open to adoption, provided that these tools aligned with available diseasemodifying treatments and adequate attention and consideration would be given to redesigning
workflows. The demographics of participants, including their years of practice and type of practice
setting, played a significant role in shaping these perspectives, with more experienced and large
health system-integrated physicians often demonstrating greater readiness to engage with new
diagnostic tools.
Physician knowledge and confidence emerged as crucial factors affecting the adoption of
biomarker-based diagnostics. Most participants acknowledged gaps in their understanding of
Alzheimer's disease and the role of biomarkers in early diagnosis. This lack of knowledge often
translated into hesitancy to adopt new tools, as physicians felt uncertain about their utility and
potential impact on patient care. Interestingly, this issue was more pronounced among physicians
in smaller or solo practices, who may have fewer opportunities for ongoing education and
professional development, beyond CMEs, compared to those in academic or large healthcare
settings. The study's findings suggest that addressing these knowledge gaps through targeted
education and training could be key to overcoming this barrier.
Patient-related barriers, including skepticism, lack of understanding, and compliance
issues, were identified as impediments to the adoption of new diagnostic tools. Physicians reported
that patients often struggled to grasp the implications of an Alzheimer's diagnosis, particularly

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when symptoms were mild or absent. This was compounded by stigmas surrounding the disease
and a general reluctance to engage with early diagnostic efforts. Furthermore, patients' failure to
follow through with lab tests or imaging, as prescribed by the physician, can further complicate
the adoption or effective use of innovative diagnostics. The demographic diversity of the
physicians, particularly in terms of the patient populations they serve, influenced these
observations, with those working in lower-income or less-educated communities reporting greater
challenges in patient compliance and understanding.
Chapter V provides a discussion and interpretation of these findings, contributions to
practice, and recommendations for future research.

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Chapter V: Discussion
The purpose of this study was to identify the barriers to the adoption of innovative
biomarker-based diagnostic tools for Alzheimer’s disease in primary care. In the last decade,
researchers have turned the corner in understanding Alzheimer’s disease. We are now moving
from a clinical definition of the disease to a biological one, which includes a 20 to 25-year
asymptomatic phase and potential progression along a continuum that for some patients traverses
through MCI and ultimately severe AD. Researchers have validated an array of biomarkers,
including blood tests for screening and diagnosis. Patients are already receiving infusions of the
first disease-modifying treatments, and many more are in the clinical trial pipeline. These DMTs
work best in patients with MCI or early-stage disease, making timely and accurate diagnosis an
urgent imperative. In order for this paradigm shift to lead to better outcomes for patients, these
breakthroughs need to transition from the research realm to clinical practice. While specialists
have had and will continue to have an important role, primary care physicians are on the frontlines.
The efficient and effective adoption and implemention of these innovative diagnostic tools in
primary care will be a turning points for millions of patients and better outcomes for them and
their family members.
To the best of the researcher’s knowledge, this study was the first time that the voices of
primary care physicians were heard directly in relation to these breakthrough, with a particular
focus on diagnosis and the adoption of innovative diagnostic tools. This chapter includes a
discussion of major findings and concludes with a discussion of the contributions to practice,
limitations of the study, and areas for future research.
The theory for identifying the barriers to the adoption of innovative biomarker-based
diagnostic tools for Alzheimer’s in primary care is comprised of six themes: (a) clinical and

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diagnostic complexities are major problems. Can innovative diagnostics reduce them?, (b)
innovative diagnostic tools can face systemic constraints and operational barriers in primary care,
(c) Economic and Financial Challenges Are A Major Concern In Primary Care and Impact
Decisions on New Tools and Diagnostics, (d) Patient Perceptions, Beliefs, and Attitudes Can be
Barriers to the Adoption of New Diagnostic Tools, (e) Addressing Gaps in Physician Knowledge
Related to Current Understanding of Alzheimer’s Disease and Innovative Diagnostics is Critical,
and (f) The availability, effectiveness, and risks of disease-modifying treatments can impact the
adoption of innovative biomarker-based diagnostics. Some factors relate to primary care
physicians, including their perceptions of patient response to new diagnostic tools, some to the
systemic issues, and some to the complexities of the disease and limitations of emerging
treatments. Together, these factors represent barriers to the adoption of new biomarker-based
diagnostic tools in primary care.
Interpretation of the Findings
While there were variations in years of experience, practice type, and practice location or
environment for each participant, each of the six themes emerged as barriers to the adoption of
new biomarker-based diagnostic tools. Each theme is discussed in detail and in the context of the
literature, in the following sections.
Clinical and Diagnostic Complexities Are Major Problems. Can Innovative Diagnostics
Reduce Them?
Clinical and diagnostic complexities pose significant barriers to the timely diagnosis of
Alzheimer’s disease in primary care using current diagnostic tools and to the adoption of
innovative biomarker-based tools. One of the key issues identified in this study is the challenge
posed by current diagnostic tools, such as the Mini-Mental State Examination (MMSE) and the

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Montreal Cognitive Assessment (MoCA), which are often seen as time-consuming and difficult to
standardize across diverse patient populations. This finding is consistent with prior research, which
has highlighted the limitations of these traditional cognitive assessment tools in terms of their
sensitivity, specificity, and practical application in primary care settings. Studies have shown that
while these tools are valuable for detecting and diagnosing late-stage disease, they are not always
effective for diagnosing MCI, where symptoms may be mild or ambiguous ( (Sabbagh, Boada,
Borson, & Chilukuri, 2020; Liu, Jun, Becker, Wallick, & Mattke, 2023; Mattke, et al., 2023)
The complexity of diagnosing Alzheimer’s Disease is the key driver of the need for new
diagnostic tools to demonstrate high accuracy, sensitivity, and reliability. This study found that
primary care physicians expressed a willingness to prescribe biomarker-based diagnostic tests that
are as accurate and repeatable as those used for other chronic conditions, such as diabetes or
cardiovascular disease. The rates of false positives and false negatives would need to be consistent
with established standards for these and other conditions. Participants emphasized that the
guidelines for assessing test results need to be clearly established and easy to follow. Complexities
and ambiguities in the interpretation and clinical implications of test results emerged as a serious
concern in this study and represent a barrier to the adoption of these new tools. These findings
align with the broader literature, which underscores the necessity for biomarker-based tests to
achieve a high level of diagnostic accuracy before they can be widely adopted in clinical practice
(Jack, Wiste, & Therneau, 2019). The need for validation and standardization of these tools is a
recurring theme in the literature, as inconsistent results can undermine both physician and patient
confidence in new tests (Hampel, et al., 2018).
Furthermore, innovative diagnostic tools may be adopted earlier in outpatient clinics within
academic institutions with more personnel, resources, and capabilities. This finding resonates with

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the literature on healthcare innovation diffusion. Larger healthcare systems, such as academic
medical centers and HMOs, are often at the forefront of adopting new tools and technologies due
to their ability to absorb the costs and infrastructure changes associated with these innovations
(Balas & Chapman, Road Map For Diffusion Of Innovation In Health Care, 2018). This
phenomenon is well-documented in the diffusion of medical innovations, where resource-rich
environments facilitate early adoption, while smaller practices may lag behind due to personnel,
financial, and logistical constraints.
According to the Diffusion of Innovation theory, innovations must also be perceived as
having a relative advantage over existing methods to be adopted. The numerous issues with the
current diagnostic tools and the comparative advantages of biomarker-based diagnostics indicate
a need for clear communication about the benefits and improved outcomes associated with new
tools. Additionally, the theory emphasizes the importance of trialability and observability,
suggesting that providing physicians with opportunities to experiment with and observe the
benefits of new diagnostics could facilitate adoption. The integration of such tools in wellresourced institutions aligns with the theory's elements of compatibility and infrastructure support.
In conclusion, the clinical and diagnostic complexities identified in this study reflect
broader challenges in the adoption of innovative diagnostic tools for Alzheimer’s Disease. The
need for accurate, reliable, and minimally disruptive diagnostic methods is critical for their
successful integration into primary care. These findings are consistent with the existing literature,
which emphasizes the importance of addressing these complexities to facilitate the widespread
adoption of new tools and technologies in healthcare.
Innovative Diagnostic Tools Can Face Systemic Constraints and Operational Barriers in
Primary Care

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Systemic and operational barriers represent another significant challenge to the adoption
of innovative diagnostic tools in primary care. This study identified several key operational issues,
including resource and infrastructure limitations, especially among physicians in a solo practice
compared to those in larger, resource-rich environments such as academic medical centers. In an
HMO system, physicians are limited to tests and procedures that have been approved by the
organization and generally follow established guidelines for when and on which patients they can
be used. This finding is consistent with the literature, which highlights the disparities in resource
availability across different healthcare settings. Smaller practices often face financial constraints,
limited access to advanced diagnostic tools, and a lack of support staff, all of which can hinder the
adoption of new technologies (Chaudoir, Dugan, & Barr, 2013).
Resource limitations are particularly pronounced when it comes to the adoption of complex
diagnostic tools that require additional training, equipment, or support infrastructure. Participants
expressed awareness of PET scans and CSF testing as new tools for diagnosing Alzheimer’s but
cited logistic and operational challenges as reasons for not using them for their patients. PET scans
were viewed as generally found in large hospitals and not as readily available and accessible within
stand-alone imaging centers. The interpretation and review of PET scan results with patients were
viewed as requiring significant training beyond the capabilities of most primary care physicians.
A blood test, such as the PrecivityTM AD, which requires the company’s specialized kit for sample
collection, and the need for the sample to be shipped back for analysis, was viewed as too
cumbersome and taxing of limited resources.
Another critical consideration is the impact of workflow disruption caused by the
introduction of new diagnostic tools. This study revealed that primary care physicians are
concerned about how these innovations might disrupt their established workflows, leading to

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inefficiencies and resistance. This disruption can lead to initial inefficiencies as staff adapt to the
new processes and can create resistance among those who are accustomed to the old methods.
Workflow redesign involves training, adjustments in scheduling, and sometimes reallocation of
resources, all of which can be daunting for practices already operating at full capacity.
These concerns were observed in all practice types and settings but were more pronounced
amongst solo practices. This concern is supported by the Diffusion of Innovation theory, which
suggests that the complexity of an innovation and its perceived impact on existing workflows can
significantly influence its adoption (Rogers, 2003). Research has shown that healthcare providers
are more likely to adopt new technologies that integrate seamlessly into their practice without
requiring significant changes to their routines (Chaudoir, Dugan, & Barr, 2013).
Overall, this study’s findings on systemic and operational barriers to the adoption of
innovative diagnostic tools for Alzheimer’s Disease are well-supported by existing research. The
Diffusion of Innovation theory posits that innovations must be compatible with existing values,
past experiences, and needs of potential adopters. Workflow disruption is a systemic and
operational barrier, which highlights the importance of ensuring that new diagnostic tools align
with the current workflows and resources of primary care settings. The identification of ways that
new diagnostic tools can bring about efficiencies in workflow combined with simplifying
integration and providing adequate training and support could facilitate adoption.
Addressing these barriers requires a multi-faceted approach, including improving resource
allocation, enhancing infrastructure, and ensuring that physician and staff workflows are adapted
to support the use of advanced diagnostic technologies. By addressing these systemic challenges,
primary care settings can create a more conducive environment for the adoption of biomarker-

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based diagnostic tools to accurately diagnose Alzheimer’s in a timely manner and lead to improved
patient outcomes.
Economic and Financial Challenges Are a Major Concern In Primary Care and Impact
Decisions on New Tools and Diagnostics
This study found that reimbursement and economic factors represent a crucial barrier to
the adoption of innovative diagnostic tools for Alzheimer’s Disease. Inadequate reimbursement
for innovative diagnostic tests was a major concern for all the participants, who viewed primary
care as operating under thin financial margins. When reimbursement rates do not adequately cover
the direct and indirect costs associated with new diagnostic tests, providers are less likely to adopt
these technologies. An understanding of the financial viability of implementing new diagnostic
tools is critical. Reimbursement policies need to include not only the test but also the professional
services of the physician to assess and interpret the results and review them with the patient. This
theme is strongly supported by the literature, where reimbursement is consistently identified as a
key determinant of technology adoption in healthcare. This issue was found to be particularly
acute in primary care, where the financial viability of adopting new technologies was often closely
tied to reimbursement policies (Varabyova, Blanckart, Greer, & Shreyogg, 2017).
The literature also highlights the importance of aligning reimbursement policies with the
value that new diagnostic tools bring to patient care. Participants expressed a willingness to adopt
innovative diagnostics if they perceived that these tools offer significant benefits to their patients
and if these benefits are recognized and rewarded by third-party payers. This concept is supported
by the broader research on value-based care, which emphasizes the need for reimbursement
policies that reflect the clinical and economic value of new technologies (Porter, Larsson, & Lee,
2016). In the case of Alzheimer’s diagnostics, this means ensuring that reimbursement rates for

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tests and physician professional services are commensurate with the potential benefits of early
detection and intervention.
Another critical aspect of this theme is the role of health insurance coverage in shaping
patient access to innovative diagnostic tools. This study found that even when a new diagnostic
test, such as PrecivityTM AD, is available, the $1,250 out-of-pocket cost due to the lack of or
inadequate third-party coverage creates an affordability challenge for a majority of patients. This
finding is consistent with the literature, which highlights the impact of insurance coverage on
healthcare access and the adoption of new technologies (Namin, Vahdat, DiGennero, Amid, &
Jalali, 2020). Inadequate coverage can lead to disparities in access to innovative diagnostics, with
patients in lower socioeconomic groups disproportionately affected. Ensuring equitable access to
these tools requires policies that mandate comprehensive insurance coverage for new diagnostic
technologies.
In conclusion, the theme of reimbursement and economic factors is a critical consideration
in the adoption of innovative diagnostic tools for Alzheimer’s Disease. These findings are wellsupported by existing literature, which emphasizes the importance of aligning reimbursement
policies with the value of new technologies and ensuring that economic barriers do not prevent
their adoption. Addressing these issues is essential for facilitating the widespread use of innovative
diagnostics in primary care and improving the early detection and management of Alzheimer’s
Disease.
Patient Perceptions, Beliefs, and Attitudes Can be Barriers to the Adoption of New
Diagnostic Tools.
Patient-related challenges, including skepticism and denial, lack of understanding, and
non-compliance with prescribed diagnostics in a timely manner, represent significant barriers to

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the adoption of innovative diagnostic tools for Alzheimer’s Disease. This study identified these
challenges as critical factors that influence how effectively new diagnostic technologies can be
integrated into primary care. These findings are consistent with a body of literature that highlights
the importance of patient engagement and education in the successful implementation of new
healthcare innovations (Baines, Edwards, & Stevens, 2022).
Patient skepticism, particularly regarding the diagnosis of Alzheimer’s Disease, is a welldocumented phenomenon. Many patients are reluctant to undergo diagnostic testing for
Alzheimer’s, especially in its early stages, due to fears associated with the disease’s stigma and
the lack of available treatments (Vernooij-Dassen, Moniz-Cook, Woods, De Lepeleire, &
Leuschner, 2005; Jacobson, Joe, & Zissimopoulos, Barrier to seeking care for memory problems:
A vignette study, 2022). This study’s finding that patients are often skeptical of new diagnostic
tools aligns with this literature, which suggests that patients may perceive these tools as
unnecessary or invasive, especially when symptoms are mild or absent. Addressing this skepticism
requires effective communication strategies that emphasize the benefits of early diagnosis and the
role of innovative diagnostics in managing the disease.
The literature also supports this study’s conclusion that a lack of patient understanding of
Alzheimer’s Disease and its progression can hinder the adoption of new diagnostic tools. Many
patients have limited knowledge about the nature of Alzheimer’s, the importance of early
detection, and new disease-modifying treatments, which can lead to resistance when physicians
recommend new diagnostic tests (Zissimopoulos, Jacobson, Chen, & Borson, 2022; Sullivan,
Muscat, & Mulgrew, 2007). This gap in understanding is particularly pronounced in populations
with lower health literacy, where misconceptions about the disease and its treatment are more
common.

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The lack of patient understanding and knowledge of Alzheimer’s disease is a public health
issue as well as a barrier to the adoption of innovative diagnostic tools. Addressing it requires
coordinated efforts at national, state, and local levels. Nationally, public health campaigns led by
organizations like the CDC and the Alzheimer's Association can raise awareness about the disease,
focusing on early signs, the importance of early diagnosis, and the benefits of innovative diagnostic
tools. These multi-lingual campaigns should leverage multiple platforms, including social media,
video platforms such as YouTube, television, and print, to reach diverse populations. Additionally,
integrating Alzheimer’s education into national healthcare policies and medical guidelines can
ensure that both patients and healthcare providers have access to up-to-date information. Federal
funding could also support research and educational programs aimed at improving public
knowledge about Alzheimer's and reducing stigma.
At the state and local levels, partnerships between public health departments, community
organizations, and healthcare providers can help tailor educational initiatives to specific
populations. As an example, the Los Angeles County Department of Public Health’s launched its
Healthy Brain LA initiative in 2020. The initiative has developed and implemented local outreach
programs, such as workshops, seminars, and health fairs, to educate communities within the county
about Alzheimer's in culturally relevant ways.
States can also work with healthcare systems to integrate Alzheimer’s education into
routine primary care visits, ensuring that patients receive accurate information during their
interactions with healthcare providers. In large health systems, including academic medical centers
and HMOs such as Kaiser, educational interventions that target these knowledge gaps, such as
patient information sessions, online resources, or personalized counseling, have been developed
to improve patient acceptance of diagnostic procedures.

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Most primary care physicians lack the time needed for educating their patients about
Alzheimer’s and the critical role of early diagnosis and intervention. It is therefore imperative that
national, state and local public health campaigns be developed and maintained. Most participants
in this study observed that when patients are well-informed about the nature of a condition and the
potential advantages of timely and accurate diagnosis, they are more likely to accept and engage
with these innovations. This public health educational effort may also help address patient
skepticism and the stigmas associated with an Alzheimer’s diagnosis. By improving patients'
understanding, physicians can facilitate a more receptive attitude toward new diagnostic
approaches, ultimately enhancing the quality of care and patient outcomes.
Non-compliance with physician-recommended diagnostic tests, such as blood tests or
imaging, is another significant patient-related challenge identified in this study. This issue is wellsupported by the literature, which notes that patient non-compliance with prescribed diagnostic
tests is a pervasive problem in healthcare, particularly for chronic diseases (Memon, Shaikh,
Soomro, & Shaikh, 2017). Factors contributing to non-compliance include fear of or discomfort
with the procedure, inconvenience, high out-of-pocket cost(s), and a lack of perceived benefit. The
findings suggest that these factors are particularly relevant in the context of Alzheimer’s
diagnostics, where patients may be hesitant to undergo tests such as a PET scan or CSF, which
they perceive as costly and invasive. A blood test with minimal or no out-of-pocket costs and
available through existing labs may improve compliance, particularly if patients’ fears and
concerns are addressed and a clear explanation of the benefits is communicated.
In conclusion, patient-related challenges play a critical role in shaping the adoption of
innovative diagnostic tools for Alzheimer’s Disease. The findings from this study is consistent
with the broader literature, which emphasizes the importance of patient education, communication,

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and engagement in overcoming these barriers. By addressing these challenges, healthcare
providers can enhance patient acceptance of new diagnostic technologies and improve the early
detection and management of Alzheimer’s Disease.
Addressing Gaps in Physician Knowledge Related to Current Understanding of Alzheimer’s
Disease and Innovative Diagnostics is Critical
This study found that knowledge gaps and educational needs among primary care
physicians are critical barriers to the adoption of innovative diagnostic tools for Alzheimer’s
Disease. The advancements in understanding Alzheimer’s disease as a continuum, discovery and
validation of biomarkers and biomarker-based diagnostic tools, and disease-modifying treatments,
have not been fully transferred to primary care physicians. Developing strategies to address these
gaps in knowledge needs to be an urgent priority. None of the participants were aware of AD as a
continuum, with an asymptomatic phase that could be as long as 25 years. The depth of knowledge
around new biomarker-based diagnostic tools and DMTs showed the need for more training and
transfer of knowledge from research to clinical practice. Without this knowledge and the adoption
of innovative diagnostic tools, primary care physicians may continue to find it challenging to
diagnose patients accurately and in a timely manner. The risks and side effects notwithstanding,
DMTs are deemed most effective and appropriate for patients diagnosed in the MCI stage.
This study found that primary care physicians in solo practice often feel inadequately
prepared to incorporate new diagnostic tools as compared to colleagues in larger practices,
academic medical centers, and HMOs. Physicians who are part of a larger practice or an academic
medical center may benefit from a more robust network, whereby interactions with peers,
including specialists, may increase their knowledge and confidence in these innovations. This
finding is supported by research showing that physicians are less likely to adopt new technologies

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if they are unsure of how to use them or lack confidence in their clinical utility (Grol, Bosch,
Hulscher, Eccles, & Wensing, 2007).
The literature also emphasizes the importance of practical, hands-on training in the
adoption of new diagnostic tools. The findings suggest that primary care physicians in academic
medical centers benefit from opportunities to engage with research-active clinician scientists and
become exposed to these tools in a learning environment. Some of the interactions can be formal
through organized seminars or collaborations on a funded research project. These physicians can
gain valuable practical experience without the pressure of immediate clinical application. These
approaches can be effective methods for enhancing physician knowledge and competency in new
diagnostic techniques. With the rapid pace of scientific advances in Alzheimer’s diagnosis and
treatment, primary care physicians in academic medical centers may be best positioned to be early
adopters of innovations.
Another important aspect of addressing knowledge gaps is creating a culture of
collaboration and knowledge-sharing among healthcare professionals. This study indicates that
primary care physicians who are late adopters of innovations, often rely on their peers and
specialists for guidance when adopting new diagnostic tools. This finding is consistent with the
literature, which highlights the value of interprofessional collaboration in promoting the diffusion
of innovations in healthcare (Reeves, Pelone, Harrison, & Zwarenstein, 2017). By creating
networks of knowledge exchange and support, healthcare systems can facilitate the spread of best
practices and ensure that physicians have access to the latest information and expertise.
The issues of education and training are well-documented in the literature, where it is noted
that ongoing medical education is essential to ensure that physicians are equipped with the
knowledge necessary to effectively diagnose and manage complex conditions like Alzheimer’s

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(Boustani, Baker, & Campbell, 2010). Continuous medical education (CME) programs that focus
on emerging diagnostic tools and their application in clinical practice are crucial for bridging the
knowledge gap. Studies have shown that targeted CME programs can significantly improve
physicians’ knowledge and confidence, leading to better integration of new diagnostic
technologies in clinical settings (Bloom, 2005).
Physicians in California are required to complete 50 hours of approved CME credits per
the biennial licensure cycle (AMA EdHub, 2024). A review of the American Medical
Association’s EdHub website shows that as of the first half of 2024, there were very few online
training modules related to AD biomarkers, innovative diagnostic tools, or disease-modifying
treatments. The number of modules for 2022 and 2023 was even lower. A search of the website of
the California chapter of the American College of Physicians, whose membership consists mainly
of primary care physicians did not find any online resources or CME courses related to these topics.
Given the substantial progress in Alzheimer’s research in the late 2010s and early 2020s, these are
an indication of the lag in transferring knowledge from research to the clinic.
In summary, addressing the knowledge gaps and educational needs of primary care
physicians is essential for the successful adoption of innovative diagnostic tools for Alzheimer’s
Disease. The findings from this study are consistent with existing literature, which underscores the
importance of ongoing education, practical training, and collaborative learning in overcoming
these barriers. By investing in these areas, healthcare systems can better equip physicians to
embrace new technologies and improve the early detection and management of Alzheimer’s
Disease.
The availability, effectiveness, and risks of disease-modifying treatments can impact the
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This study identified the availability, and limitations of therapeutics and disease-modifying
treatments represent another major barrier to the adoption of innovative diagnostic tools for
Alzheimer’s Disease, as identified in this study. This theme is widely supported in the literature,
where the lack of therapeutic options is often cited as a key reason for the reluctance to pursue
early diagnosis (Cummings, 2023). Without effective treatments, many physicians questioned the
utility of diagnosing Alzheimer’s Disease at an early stage, as it may not have significantly altered
the patient’s clinical management or outcomes.
Before the advent of DMTs, participants cited the lack of such treatments as having
impacted both physician and patient attitudes toward testing using current tools. At that time, the
focus of physicians, patients, and their family caregivers was on symptomatic treatments using
drugs such as Donepezil (Aricept). This approach is supported by the literature, which suggests
that in the absence of curative treatments, the primary goal of care often shifts to managing
symptoms and maintaining quality of life (Livingston, et al., 2020). As a result, innovative
diagnostic tools that do not directly contribute to symptom management may be perceived as less
valuable in clinical practice.
This study found that primary care physicians were particularly concerned about the ethical
implications of diagnosing the disease when there was no cure. This concern is echoed in the
literature, where the concept of “therapeutic nihilism” is frequently discussed in the context of
Alzheimer’s Disease (Leuzy, 2012). The advent of DMTs has not eliminated these concerns, but
this study found general optimism tempered by concerns related to short and long-term side effects,
safety, risks, efficacy, and high out-of-pocket costs for the patients. The highly publicized
controversies around the FDA’s approval of the first two DMTs brought these issues to the
forefront. Participants felt that the current DMTs should not be prescribed in primary care and that

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patients undergoing treatment should be under the care of specialists. Currently, the Appropriate
Use Recommendation for Leqembi does not specify that patients have to be under the care of a
specialist to receive this drug, but clinician and institutional preparedness to manage serious side
effects such as amyloid-related imaging abnormalities (ARIA) is critical (Cummings, et al., 2023).
Improving the ability to diagnose MCI in primary care was seen as leading to better patient
outcomes, by identifying and prioritizing referrals to specialists. There was broad recognition that
biomarker-based diagnostic tools can play a key role in this process if barriers to their adoption
are addressed.
There remains a hesitancy on the part of primary care physicians to diagnose asymptomatic
Alzheimer’s, given the lack of treatments for that stage of the disease. The knowledge that a
definitive diagnosis will not lead to a meaningful treatment can lead to a sense of futility, both for
the physician and the patient, which in turn discourages the adoption of tests for asymptomatic
disease.
The literature also highlights the impact of treatment limitations on patient motivation to
seek diagnostic testing. This study’s findings suggest that patients are often reluctant to undergo
testing for Alzheimer’s Disease if they believe that there are no effective treatments. This lack of
motivation is consistent with the broader research, which shows that patients are more likely to
comply with diagnostic testing and follow-up care when they believe that effective treatments are
available (Cohen-Mansfield et al., 2006). The absence of or limitations of such treatments for
Alzheimer’s Disease contributes to a widespread perception that early diagnosis offers little
practical benefit, which in turn limits the adoption of innovative diagnostic tools.
The diffusion and adoption of Leqembi has not been studied extensively and represents a
gap in our understanding. A recent assessment of the ramp up of Leqembi within Alzheimer’s

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programs in the United States, shows several challenges. As demand for the drug increased, wait
times have grown longer. Other challenges and barriers include staffing issues, access to imaging,
payer restrictions and administrative burden of entering patients in the CMS registry and following
up with patients. Wait times for patients to be evaluated at a memory clinic have increased from
245 days in October 2022 to 490 days in early 2024 (Shaw G. , 2024).
The findings of this study diverged from existing literature in a couple of areas. One was
in the degree of reliance that primary care physicians place on specialists for guidance regarding
innovative diagnostic tools and disease-modifying treatments. While the literature suggests that
primary care physicians are increasingly taking on the role of managing chronic conditions such
as Alzheimer’s Disease independently due to workforce shortages and the growing prevalence of
the disease , this study revealed that many primary care physicians still look to specialists for
insights and guidance on the latest diagnostic advancements and treatment options. This reliance
suggests a gap in confidence or resources within primary care settings, which to some extent
contrasts with the growing emphasis in the literature on primary care as the frontline for
Alzheimer’s management.
While previous studies emphasize the importance of primary care physicians adopting new
diagnostic technologies to improve early detection rates, the findings here suggest that barriers
such as resource constraints and workflow disruptions are more pronounced in certain practice
settings than the literature typically acknowledges. For instance, physicians in smaller, solo
practices expressed more significant challenges in integrating new diagnostic tools compared to
those in larger or academic settings, a nuance that is often underexplored in broader discussions
about technology adoption in primary care. This indicates that while innovation is being

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championed, its practical application in diverse clinical settings may be more limited than
previously suggested.
Additionally, the widespread concern and skepticism about the reliability of biomarkerbased tests, among the participants in this study, adds another layer of complexity to the adoption
of these innovative tools. While existing studies often report enthusiasm for new diagnostic
technologies clinician researchers, this study suggest that primary care physicians may approach
these innovations with more caution, reflecting concerns about test accuracy, the potential for false
positives or negatives, and the overall trustworthiness of emerging tools. This indicates a gap
between the optimistic portrayal of diagnostic innovations in research and the cautious, real-world
attitudes of frontline physicians.
In summary, the theme of therapeutic and treatment limitations represents a significant
barrier to the adoption of innovative diagnostic tools for Alzheimer’s Disease. The findings from
this study align with existing literature, which highlights the impact of the lack of effective
treatments on both physician and patient attitudes toward diagnosis. Addressing these therapeutic
limitations, either through the development of new treatments or by reframing the goals of early
diagnosis, is essential for improving the adoption of new diagnostic technologies in primary care.
Implications for Practice
With 85% of Alzheimer’s disease cases diagnosed by a primary care physician and the
shortage of specialists, primary care physicians will remain at the forefront of detection and
diagnosis (Drabo, et al., 2019). This study gave voice to primary care physicians on the important
and emerging topic of innovative biomarker-based diagnostic tools. The successful
implementation of these tools has the potential to significantly reduce rates of delayed diagnosis,
missed diagnosis, and misdiagnosis, which have been persistent and significant issues. These

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issues are even more serious for MCI. With the advent of DMTs, which currently are most effective
for patients with MCI and early Alzheimer’s disease, accurate and timely diagnosis is critical for
better patient outcomes. To the best of the researcher’s knowledge, this was the first qualitative
grounded theory study underpinned by the Diffusion of Innovation theory to identify the barriers
to the adoption of innovative diagnostic tools for Alzheimer’s in primary care.
This study made contributions to practice in several important areas. It provided an
understanding and identified specific barriers that primary care physicians encounter when
considering the adoption of biomarker-based diagnostic tools for Alzheimer's disease. By
elucidating these barriers through semi-structured interviews, this research provided valuable
insights into the challenges faced by physicians in integrating new diagnostic technologies into
their practice.
The findings from this study have significant implications for primary care, where the
adoption of innovative diagnostic tools like biomarker-based tests for Alzheimer’s Disease is
crucial, but also challenging. One of the most immediate implications is the need for enhanced
training and education for primary care physicians. The knowledge gaps identified in this research
highlight that many physicians may not be fully aware of the biological definition of Alzheimer’s,
mixed pathologies, which account for most of the diagnosed cases, and the different stages of the
disease across a continuum that starts in middle-age. The results also imply that many physicians
need to be trained about the capabilities, limitations, and proper utilization of these new diagnostic
tools. Increased awareness of current DMTs, and the ones in the clinical trial pipeline can also be
beneficial, given the increasing coverage in the media and interest from patients and their families.
Addressing these gaps through targeted continuing medical education (CME) programs,

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workshops, and accessible resources could facilitate more informed decision-making and
potentially increase the adoption of such innovations in clinical practice.
Another practical implication is the necessity of streamlining the integration of new
diagnostic tools into existing workflows. The study found that many physicians are concerned
about the disruption these tools may cause to their established processes, particularly in smaller
practices with limited resources. Healthcare systems and institutions must prioritize developing
and implementing strategies that minimize workflow disruptions. This could involve the use of
clinical decision support systems (CDSS) that seamlessly integrate with electronic medical records
(EMRs), making it easier for physicians to order and interpret these new tests without adding
significant time or complexity to their routines. The integration of CDSS tools into electronic
medical record (EMR) systems has become common in large medical enterprises, such as
academic medical centers and HMOs (Alexluk, et. al. 2024). In smaller practices, their consistent
and effective use may help in addressing workflow disruption concerns from the use of innovative
biomarker-based diagnostic tools.
Resource allocation is also a critical issue that needs to be addressed. The study revealed
substantial disparities between larger, well-funded institutions and smaller, resource-limited
practices in their capacity to adopt new diagnostic tools. This suggests a need for policymakers
and healthcare leaders to consider resource allocation models that ensure all primary care settings
have the necessary infrastructure, such as access to advanced laboratory services and financial
support for adopting new technologies. This might include subsidies, grants, or incentives for
smaller practices to level the playing field and enable broader adoption of these innovations.
Finally, patient education and engagement emerge as vital components of practice that
must be enhanced to facilitate the adoption of new diagnostic tools. The study found that patient

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skepticism and lack of understanding are significant barriers to the uptake of biomarker-based
tests. Primary care physicians can play a crucial role in addressing these barriers by developing
clear communication strategies that demystify these new tools, explain their benefits, and alleviate
patient concerns. This could involve creating patient education materials, holding informational
sessions, and fostering a more collaborative approach to care where patients are actively involved
in the decision-making process regarding their diagnostic options.
By adopting innovative diagnostic tools for Alzheimer’s, primary care physicians and
healthcare institutions can accurately detect and diagnose the disease in a timely manner,
ultimately leading to better outcomes for patients.
Limitations and Recommendations for Future Research
Qualitative research using semi-structured interviews with primary care physicians
allowed for a deeper understanding of the challenges and issues that may prevent or slow down
the adoption of innovative diagnostic tools for Alzheimer’s disease. Future research can build on
the findings of this study, by using a mixed-methods methodology. By coupling quantitative and
qualitative research, and having a larger sample size, the findings of the study could be generalized
across a larger segment of primary care physicians. Using a survey for the quantitative portion of
the study would enable statistical analysis of the data, offering more substantive evidence for the
results of this study.
This study was geographically limited to Southern California, which may not fully
represent the experiences and perspectives of primary care physicians across different regions of
the United States. Future research should aim to include a broader geographic scope, incorporating
participants from various states and regions, including rural areas where access to resources may
be more limited, and the adoption of new technologies might face different challenges. This would

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provide a more comprehensive understanding of the barriers to the adoption of innovative
diagnostic tools across diverse healthcare settings.
Another limitation of this study is the underrepresentation of primary care physicians
practicing in low-income communities. These communities often face unique challenges, such as
limited access to healthcare resources, higher patient loads, and greater socioeconomic barriers
that could influence the adoption of new diagnostic tools. Future research should prioritize
recruiting participants from these areas to ensure that the findings reflect the experiences and
challenges of a wider range of primary care settings. This would also help to identify specific
strategies that might be necessary to support the adoption of innovative diagnostic tools in
underserved communities.
Additionally, while the semi-structured interviews provided rich qualitative data, the
sample size of 24 physicians, though sufficient for grounded theory research, may limit the
generalizability of the findings. Future research could benefit from incorporating a larger sample
size, possibly using surveys to capture data from a sample population of primary care physicians.
This would allow for more robust statistical analyses and could strengthen the validity of the
study’s conclusions. A mixed-methods approach, combining qualitative interviews with
quantitative data collection, could provide a more holistic understanding of the issues at hand and
allow for the triangulation of data, thereby enhancing the credibility of the findings.
Furthermore, future research could explore longitudinal studies to assess how the adoption
of innovative diagnostic tools evolves over time. Understanding the long-term impacts of
introducing these tools in primary care settings, including their influence on patient outcomes,
physician workflows, and healthcare costs, would be invaluable. Such studies could also

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investigate how ongoing training, changes in healthcare policies, and advancements in technology
influence the sustained adoption and utilization of these tools.
Future research could also explore whether the adoption of blood-based biomarkers for
Alzheimer’s disease are similar to or differ from those for other diseases. One example of a new
diagnostic tool that did not fully live up to its initial promise is the prostate-specific antigen (PSA)
test for prostate cancer screening. When the PSA test was first introduced, it was widely adopted
in primary care with the expectation that it would significantly increase early detection and reduce
prostate cancer mortality. However, over time, evidence emerged that the test produced a high
number of false positives, leading to unnecessary biopsies and treatments. Additionally, many of
the cancers detected by PSA screening were slow-growing and unlikely to cause harm during a
man's lifetime, leading to concerns about overdiagnosis and overtreatment.
As a result, the initial enthusiasm for PSA screening has been tempered, and
recommendations for its use have become more conservative. While the PSA test can still be a
useful tool in certain cases, its limitations highlight the importance of balancing the benefits and
risks of new diagnostic tools, and the need for comprehensive evidence before widespread
adoption. This example illustrates how new diagnostic tools, even when promising, can face
challenges in fulfilling their expected potential, leading to a more cautious and measured approach
in clinical practice.
Finally, it would be beneficial for future research to explore the perspectives of other
stakeholders in the healthcare system, such as patients, specialists, healthcare administrators, and
policymakers. Their views on the barriers and facilitators of adopting innovative diagnostic tools
could provide a more comprehensive picture and help identify multifaceted strategies to overcome
the challenges identified in this study. Engaging a broader range of voices would ensure that any

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interventions or recommendations are grounded in the realities of the entire healthcare ecosystem,
ultimately supporting more effective implementation of these tools in primary care settings.
Conclusions
The persistent high rates of misdiagnosis, late diagnosis, or lack of diagnosis for
Alzheimer’s disease have been public health issues and concerns. Timely and accurate diagnosis
of this disease could have resulted in better outcomes for many patients and their families even in
the absence of disease-modifying treatments. Research breakthroughs in the biological definition
of Alzheimer’s, biomarkers for timely and accurate diagnosis, and the advent of disease modifying
treatments in clinical practice have dawned a new era. The transition of these innovations into
clinical practice, particularly through adoption and implementation in primary care will have a
profound positive impact on millions of individuals.
The findings of this study have illuminated the complex and multifaceted barriers that
primary care physicians face in adopting biomarker-based diagnostic tools for Alzheimer’s
Disease. Through semi-structured interviews with 24 physicians in Southern California, this
research has underscored the challenges at the intersection of disease complexity, clinical practice,
healthcare systems, patient dynamics, and technological innovation. These insights offer a deeper
understanding of the factors hindering the integration of promising diagnostic advancements for
Alzheimer’s disease into routine primary care, highlighting the need for targeted interventions to
address these challenges.
One of the most significant conclusions drawn from this study is the critical role of
physician knowledge and training in the adoption process. Many primary care physicians reported
a lack of adequate understanding of innovative diagnostic tools, which leads to hesitation in
incorporating them into practice. This gap in knowledge, combined with concerns over the

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accuracy and reliability of new tests, suggests that continuing education and accessible resources
are paramount. Addressing these educational needs could empower physicians to feel more
confident in adopting and advocating for these tools, ultimately improving patient outcomes.
Furthermore, the study has identified systemic and operational barriers as substantial
impediments to the adoption of new diagnostic tools. Resource limitations, particularly in smaller
practices and underfunded areas, pose significant challenges. Physicians practicing in these
settings may struggle to integrate new technologies due to a lack of infrastructure, financial
constraints, and the potential disruption to established workflows. These findings emphasize the
need for healthcare policies and initiatives that support resource allocation, infrastructure
development, and streamlined integration processes to ensure that all primary care settings,
regardless of size or funding, can benefit from the latest advancements in Alzheimer’s diagnostics.
Patient-related factors also emerged as critical barriers to the adoption of innovative
diagnostic tools. The study revealed that patient skepticism, lack of understanding of chronic
diseases, and non-compliance with diagnostic procedures significantly hinder the effectiveness of
new tools in primary care. This highlights the importance of developing patient education
initiatives and communication strategies that address these issues, fostering a more informed and
engaged patient population. Physicians must be equipped with the tools and resources necessary
to guide their patients through the complexities of Alzheimer’s diagnostics, helping them to
overcome their fears and misconceptions.
The application of the Diffusion of Innovation theory throughout this study has provided a
valuable framework for understanding the adoption process of new diagnostic tools in primary
care. The theory’s focus on the characteristics of innovation, the social system, communication
channels, and time has been instrumental in analyzing the barriers identified in this research. It is

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clear that for these tools to be successfully integrated into primary care, they must be perceived as
highly reliable, compatible with existing workflows, and beneficial to both physicians and patients.
Future efforts to promote the adoption of such innovations must consider these factors, ensuring
that the tools are not only scientifically valid but also practically viable in diverse healthcare
settings.
In conclusion, this study contributed to the growing body of knowledge on the adoption of
innovative diagnostic tools in primary care, with a specific focus on Alzheimer’s Disease. The
insights gained from this research provided a foundation for future studies and interventions aimed
at overcoming the barriers identified. By addressing the gaps in physician knowledge, improving
resource allocation, enhancing patient education, and considering the practical application of the
Diffusion of Innovation theory, healthcare systems can better support the adoption of these critical
tools. Ultimately, this will lead to earlier and more accurate diagnoses of Alzheimer’s Disease,
offering patients and their families the opportunity for better management and improved quality
of life.

151
References
Alzheimer’s Association Workgroup . (2024, May 31). Clinical Criteria for Staging and
Diagnosis. Retrieved from Alzheimer's Association:
https://alz.org/media/Documents/scientific-conferences/Clinical-Criteria-for-Staging-andDiagnosis-for-Public-Comment-Draft-2.pdf
Alzheimer's Association. (2019). 2019 Alzheimer's Disease Facts and Figures. Washington, DC:
Alzheimer's Association.
Alzheimer's Association. (2024, April 5). Medications for Memory, Cognition and DementiaRelated Behaviors. Retrieved from Alzheimer's Association website:
https://www.alz.org/alzheimers-dementia/treatments/medications-for-memory
Alzheimer's Association Report. (2020). Alzheimer's Association Facts and Figures. Alzheimer's
& Dementia, 391-460.
Alzheimer's Association Report. (2022). 2022 Alzheimers disease facts and figures. Alzheimer's
& Dementia, 700-798.
Alzheimer's Association Report. (2023). Alzheimer's Association Report: 2023 Alzheimer's
disease facts and figures. Alzheimer's & Dementia, 1598-1695.
AMA EdHub. (2024, August 10). Retrieved from American Medical Association:
https://edhub.ama-assn.org/collections/5777/neurology
American Geriatrics Society. (2023, November 16). The American Geriatrics Society. Retrieved
from The American Geriatrics Society: chromeextension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.americangeriatrics.org/sites/
default/files/inlinefiles/AGS%20Comments%20on%20Revised%20Criteria%20for%20Diagnosis%20and%
20Staging%20of%20AD%20%2811%2016%2023%29.pdf
Amjad, H., Roth, D. L., Sheehan, O. C., Lyketsos, C. G., Wolff, J. L., & Samus, Q. M. (2018).
Underdiagnosis of Dementia: an Observational Study of Patterns in Diagnosis and
Awareness in US Older Adults. Journal of General Internal Medicine, 1131-1137.
Angioni, D. D. (2022). Blood Biomarkers from Research Use to Clinical Practice: What Must Be
Done? A Report from the EU/US CTAD Task Force. The Journal of Prevention of
Alzheimer’s Disease, 569-579.
Aranda, M. P., Kremer, I. N., Hinton, L., Zissimopoulos, J., Whitmer, R. A., Hummel, C. H., . . .
Fabius, C. (2021). Impact of dementia: Health Disparities, population trends, care
interventions, and economic costs. Journal of the American Geriatrics Society, 1774-
1783.

152
Atri, A. (2016). Imaging of neurodegenerative cognitive and behavioral disorders: practical
considerations for dementia clinical practice. In J. Masdeu, & R. G. Gonzalez, Handbook
of Clinical Neurology (pp. 971-984). Elsevier.
Atri, A., Knopman, D. S., Norman, M., Karlawish, J., Sano, M., Onyike, C. U., . . . Dickerson, B.
C. (2018). Best Clinical Practice Guidelines for the Evaluation of Neurodegenerative
Cognitive Behavioral Symptoms. Alzheimer's & Dementia, P1559-P1559.
Baines, R., Edwards, K., & Stevens, S. (2022). Meaningful patient and public involvement in
digital healthinnovation, implementation and evaluation: A systematicreview. Health
Expectations, 1232-1245.
Balas, E. A., & Chapman, W. W. (2018). Road Map for Diffusion of Innovation in Health Care.
Health Affairs, 198-204.
Balas, E. A., & Chapman, W. W. (2018). Road Map For Diffusion Of Innovation In Health Care.
Health Affairs, 198-204.
Baloch, S., Moss, S. B., Nair, R., & Tingle, L. (2010). Practice Patterns in the Evaluation and
Management of Dementia by Primary Care Residents, Primary Care Physicians, and
Geriatricians. Proceedings of Baylor University Medical Center, 121-125.
Bandini, J. I., Schulson, L. B., Ahluwalia, S., Harrison, J., Chen, E. K., Lai, J.-S., & Edelen, M.
(2022). Patient, Family Caregiver, and Provider Perceptions on Self-Assessment
Screening for Cognitive Impairment in Primary Care: Findings From a Qualitative Study.
Gerontology and Geriatric Medicine.
Barnes, L. L., Leurgans, S., Aggarwal, N. T., Shah, R. C., Arvanitakis, Z., & James, B. D.
(2015). Mixed pathology is more likely in black than white decedents with Alzheimer
dementia. Neurology, 528-534.
Barthélemy, N., Salvado, G., Schindler, S., He, Y., Janelidze, S., Collij, L. E., . . . Hansson, O.
(2024). Highly accurate blood test for Alzheimer’s disease is similar or superior to
clinical cerebrospinal fluid tests. Nature Medicine. Retrieved from Nature Medicine:
https://doi.org/10.1038/s41591-024-02869-z
Bateman, R. J., Barthelemy, N. R., & Horie, K. (2020). Another step forward in blood-based
diagnostics for Alzheimer's disease. Nature Medicine, 313-319.
Baumgart, M., Snyder, H. M., Carrillo, M. C., Fazio, S., Kim, H., & Johns, H. (2015). Summary
of the evidence on modifiable risk factors for cognitive decline and dementia: A
population-based perspective. Alzheimer's & Dementia, 718-726.
Bentley, M. W., Kerr, R., Ginger, M., & Karagoz, J. (2019). Behavioural change in primary care
professionals undertaking online education in dementia care in general practice.
Australian Journal of Primary Health, 244-249.

153
Bernhardt, B. A., Zayac, C., Gordon, E. S., Wawak, L., Pyeritz, R. E., & Gollust, S. E. (2012).
Incorporating direct-yo-consumer genomic information into patient care: attuitude and
experiences of primary care physicians. Personalized Medicine, 683-692.
Bernstein, A., Rogers, K. M., Possin, K. L., Steele, N., Ritchie, C., Kramer, J., . . . Rankin, K.
(2019). Dementia Assessment and management in primary care settings: a survey of
current provider practices in the United States. BMC Health Services Research.
Bernstein-Sideman, A., Ma, M., Hernandez de Jesus, A., Alagappan, C., Razon, N., Dohan, D., .
. . Borson, S. (2023). Primary Care Practitioner Perspectives on the Role of Primary Care
in Dementia Diagnosis and Care. JAMA Network Open.
Berwick, D. (2003). Disseminating Innovations in Health Care. JAMA, 1969-1975.
Bloom, S. (2005). Effects of continuing medical education on improving physician clinical care
and patient health: A review of systematic reviews. nternational Journal of Technology
Assessment in Health Care, 380-385.
Boise, L., Camicioli, R., Morgan, D. L., Rose, J. H., & Congleton, L. (1999). Diagnosing
Dementia: Perspectives of Primary Care Physicians. The Gerontologist, 457-464.
Bonomi, S., Gupta, M. R., & Schindler, S. E. (2023). Inadequate Reimbursement for lumbar
puncture is a potential barrier to accessing new Alzheimer's disease treatments.
Alzheimer's & Dementia, 5849-5851.
Boustani, M., Baker, M., & Campbell, N. (2010). Impact and Recognition of Cognitive
Impairment AmongHospitalized Elders. Journal of Hospital Medicine, 69-77.
Bradford, A., Kunik, M. E., Schulz, P., Williams, S. P., & Singh, H. (2009). Missed and Delayed
Diagnosis of Dementia in Primary Care - Prevalence and Contributing Factors.
Alzheimer's Disease and Associated Disorders, 306-314.
Brazil, K., Carter, G., Galway, K., Watson, M., & van der Steen, J. T. (2015). General
practitioners perceptions on advance care planning for patients living with dementia.
BMC Palliative Care.
Brookmeyer, R., Abdalla, N., Kawas, C. H., & Corrada, M. M. (2018). Forecasting the
prevalence of preclinical and clinical Alzheimer's disease in the United States.
Alzheimer's & Dementia, 14, 121-129.
Bruen, B., Docteur, E., Lopert, R., Cohen, J., DiMasi, J., Dor, A., . . . Shih, C. (2016). THE
IMPACT OF REIMBURSEMENT POLICIES AND PRACTICES ON HEALTHCARE
TECHNOLOGY INNOVATION . Washington DC.
Burke, A. D., & Goldfarb, D. (2023). Diagnosing and Treating Alzheimer's Disease During the
Early Stage. Journal of Clinical Psychiatry, 13-19.

154
Burke, J. F., Kerber, K. A., Langa, K. M., Albin, R. L., & Kotagal, V. (2023). Lecanemab.
Neurology, 661-665.
Burns, J. M., Johnson, D. K., Liebmann, E. P., Bothwell, R. J., Morris, J. K., & Vidoni, E. D.
(2017). Safety of disclosing amyloid status in cognitively normal older adults.
Alzheimer's & Dementia, 13, 1024-1030.
C2N Diagnostics, Inc. (2023, March 11). About The Precivity™ Blood Tests . Retrieved from
PrecivityAD Web Site: https://precivityad.com/patient-caregiver-faq
Cahill, S., Clark, M., O'Connell, H., Lawlor, B., Coen, R. F., & Walsh, C. (2008). The attitudes
and practices of general practitioners regarding dementia diagnosis in Ireland.
International journal of geriatric psychiatry, 663-669.
Cahill, S., Maeve, C., Walsh, C., & O'Connell, H. (2006). Dementia in primary care: the first
survey of Irish general practitioners. International journal of geriatric psychiatry, 319-
324.
Cain, M., & Mittman, R. (2002). Diffusion of Innovation in Health Care. Los Angeles:
California Health Care Foundation.
California Health Care Foundation. (2021). California Health Care Almanac. Oakland:
California Health Care Foundation.
Carlfjord, S., Lindberg, M., Bendtsen, P., Nilsen, P., & Andersson, A. (2010). Key factors
influencing adoption of an innovation in primary health: a qualitative study based on
implementation theory. Family Practice.
Chapleau, M., Iaccarino, L., Soleimani-Meigooni, D., & Rabinovici, G. D. (2022). The Role of
Amyloid PET in Imaging Neurodegenerative Disorders: A Review. Journal of Nuclear
Medicine, 13S-19S.
Charmaz, K. (2006). Constructing Grounded Theory A Practical Guide through Qualitative
Analysis. Thousand Oaks: SAGE Publications.
Charmaz, K. (2012). The Power and Potential of Grounded Theory. Medical Socialogy Online,
2-15.
Charmaz, K. (2015). Teaching Theory Construction With Initial Grounded Theory Tools: A
Reflection on Lessons and Learning. Qualitative Health Research, 1610-1622.
Chaudoir, S. R., Dugan, A. G., & Barr, C. (2013). Measuring factors affecting implementation of
health innovations: a systematic review of structural, organizational, provider, patient,
and innovation level measures. Implementation Science.

155
Chen, C., & Zissimopoulos, J. M. (2018). Racial and ethnic differences in trends in dementia
prevalence and risk factors in the United States. Alzheimer’s & Dementia: Translational
Research & Clinical Interventions, 510-520.
Chen, Y., Tysinger, B., Crimmins, E., & Zissimopoulos, J. M. (2019). Analysis of dementia in
the US population using Medicare claims: Insights from linked survey and administrative
claims data. Alzheimer's & Dementia, 197-207.
Chodosh, J., Petitti, D. B., Elliott, M., Hays, R. D., Crooks, V. C., Rueben, D. B., . . . Wenger, N.
(2004). Physician Recognition of Cognitive Impairment: Evaluating the Need for
Improvement. Journal of American Society of Gerontology, 1051-1059.
Claxton, G., Fox, C., Damico, A., Levitt, L., & Pollitz, K. (2016, December 12). KFF Affordable
Care Act. Retrieved from Kaiser Family Foundation: https://www.kff.org/affordablecare-act/issue-brief/pre-existing-conditions-and-medical-underwriting-in-the-individualinsurance-market-prior-to-the-aca/
Clevenger, C. K., Schlenger, A., Gunter, D., & Brewster Glasgow, G. (2023). Cognitive
assessment in primary care: Practical recommendations. The Nurse Practitioner, 26-35.
Cordell, C. B., Borson, S., Boustani, M., Chodosh, J., Reuben, D., Verghese, J., . . . Fried, L. B.
(2013). Alzheimer's Association recommendations for operationalizing the detection of
cognitive impairment during Medicare Annual Wellness Visit in a primary care setting.
Alzheimer's & Dementia, 141-150.
Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and
Mixed-Methods Approaches. Thousand Oaks: Sage Publications, Inc.
Crimmins, E. (2020). Social hallmarks of aging: Suggestions for geroscience research. Aging
Research Review.
Cummings, J. (2023). Anti-amyloid Monoclonal Antibodies are Transformative Treatments that
Redine Alzheimer's Disease Therapeutics. Drugs, 569-576.
de Levante Raphael, D. (2022). The Knowedge and Attitudes of Primary Care and the Barriers to
Early Detection and Diagnosis of Alzheimer's Disease. Medicina, 906-915.
Dearing, J. W., & Cox, J. G. (2018). Diffusion of Innovations Theory, Principles, And Practice.
Health Affairs, 183-190.
Ding, E., Wang, Y., Tang, S., & Shi, X. (2022). A review on the application of the exposome
paradigm to unveil the environmental determinants of age-related diseases. Human
Genomics.
Dorr, D. A., Cohen, D. J., & Adler-Milstein, J. (2017). Data-Driven Diffusion of Innovations:
Successes and Challenges in 3 Large-scale Models. Health Affairs, 257-265.

156
Drabo, E. F., Barthold, D., Joyce, G., Ferido, P., Chui, H. C., & Zissimopoulos, J. (2019).
Longitudinal analysis of dementia diagnosis and specialty care among racially diverse
Medicare beneficiaries. Alcheimer's & Dementia, 1402-1411.
Dubois, B., & Hampel, H. (2016). Preclinical Alzheimer's disease: Definition, natural history,
and diagnostic criteria. Alzheimer's and Dementia, 292-323.
Dubois, B., Padovani, A., Scheltens, P., Rossi, A., & Dell'Agnello, G. (2016). Timely Diagnosis
for Alzheimer's Disease: A Literature Review on Benefits and Challenges. Journal of
Alzheimer's Disease, 617-631.
Dubois, B., Villain, N., Frisoni, G., Rabinovici, G., Sabbagh, M., Cappa, S., . . . Epelbaum, S.
(2021). Clinical Diagnosis of Alzheimer's disease: recommendations of the International
Working Group. Lancet Neurology, 484-496.
El-Hayek, Y., Wiley, R. E., Khoury, C. P., Daya, R. P., Ballard, C., Evans, A. R., . . . Atri, A.
(2019). Tip of the Iceberg: Assessing the Global Socioeconomic Costs of Alzheimer's
Disease and Related Dementias and Strategic Implications for Stakeholders. Journal of
Alzheimer's Disease, 323-334.
Finch, C. E., & Kulminski, A. M. (2019). The Alzheimer's Disease Exposome. Alzheimer's &
Dementia, 1123-1132.
Foley, T., Boyle, S., Jennings, A., & Smithson, W. H. (2017). We're certainly not in our comfort
zone": a qualitative study of GP's dementia care educational needs. BMC Family
Practice.
Fox, M., Fox, C., Cruickshank, W., Penhale, B., Poland, F., & Steel, N. (2014). Understanding
the dementia diagnosis gap in Norfolk and Suffolk: A survey of general practitioners.
Quality in Primary Care, 101-107.
Galvin, J. E., & Sadowsky, C. H. (2012). Practical Guidelines for the Recognition and Diagnosis
of Dementia. Journal of the American Board of Family Medicine, 367-382.
Gransjoen, A. M., Wiig, S., Bakke Lysdahl, K., & Morten Hofmann, B. (2018). Barriers and
facilitators for guideline adherence in diagnostic imaging. BMC Health Services
Research.
Grol, R., Bosch, M., Hulscher, M., Eccles, M., & Wensing, M. (2007). Planning and Studying
Improvementin Patient Care: The Use ofTheoretical Perspectives. The Milbank
Quarterly, 93–138.
Gustavsson, A., Norton, N., Fast, T., Frolich, L., Georges, J., Holzapfel, D., . . . Ferretti, M. T.
(2022). Global Estimates on the number of persons across the Alzheimer's disease
continuum. Alzheimer's & Dementia, 658-670.

157
Hamer, M. K., DeCamp, M., Bradley, C. J., Nease, D. E., & Perraillon, M. C. (2023). Adoption
and Value of the Medicare Annual Wellness Visit: A Mixed-Methods Study. Medical
Care Research and Review, 433-443.
Hampel, H., O'Bryant, S. E., Molinuevo, J. L., Zetterberg, H., Masters, C. L., Lista, S., . . .
Blennow, K. (2018). Blood-based biomarkers for Alzheimer's disease: mapping the road
to the clinic. Nature Neurology, 639-652.
Hansen, E. C., Hughes, C., Routley, G., & Robinson, A. L. (2008). General Practitioners'
experiences and understandings of diagnosing dementia: Factors impacting early
diagnosis. Social Science & Medicine, 1776-1783.
Haye, S., Thunell, J., Joyce, G., Ferido, P., Tysinger, B., Jacobson, M., & Zissimopoulos, J.
(2023). Estimates of diagnosed prevalence and incidence among diverse beneficiaries in
traditional Medicare and Medicare Advantage. Alzheimer's Dementia.
Hinton, L., Franz, C. E., Reddy, G., Flores, Y., Kravitz, R. L., & Barker, J. C. (2007). Practice
Constraints, Behavioral Problems, and Dementia Care: Primary Care Physicians'
Perspectives. Journal of General Internal Medicine, 1487-1492.
Hochhalter, A. K., Bryant, L. L., Hunter, R., Liu, R., Friedman, D. B., Price, A. E., . . .
McCrystle, S. (2012). Multisite Qualitative Study of Primary Care Physicians' and
Midlevel Providers' Self-Reported Practices and Perceptions About Maintaining
Cognitive Health. Preventing Chronic Disease.
Hunter, C. A., Kirson, N. Y., Desai, U., Cummings, A. G., Faries, D. E., & Birnbaum, H. G.
(2015). Medical Costs of Alzheimer's Disease Misdaignosis Among US Medicare
Beneficiaries. Alzehimer's & Dementia, 887-895.
Illiffe, S., Manthorpe, J., & Eden, A. (2003). Sooner or later? Issues in the early diagnosis of
dementia in general practice: a qualitative study. Family Practive, 376-381.
Isaacson, R. S., Ganzer, C. A., Hristov, H., Hackett, K., Caesar, E., Cohen, R., . . . Schel. (2018).
The clinical practice of risk reduction for Alzheimer's disease: A precision medicine
approach. Alzheimer's and Dementia, 1663-1673.
Jack, C., Wiste, H., & Therneau, T. (2019). Associations of Amyloid, Tau, and
Neurodegeneration Biomarker Profiles With Rates of Memory Decline Among
Individuals Without Dementia. JAMA, 2316-2325.
Jacobson, M., Joe, E., & Zissimopoulos, J. (2022). Barrier to seeking care for memory problems:
A vignette study. Alzheimer's & Dementia.
Jacobson, M., Thunell, J., & Zissimopoulos, J. (2020). Cognitive Assessment At Medicare's
Annual Wellness Visit in Fee-For-Service And Medicare Advantage Plans. Health
Affairs, 1935-1942.

158
James, B. D. (2016). TDP-43 stage, mixed pathologies, and clinical Alzheimer’s-type dementia.
Brain, 2983-2993.
Judge, D., Roberts, J., Khandker, R., Ambegaonkar, B., & Black, C. M. (2019). Physician
Perceptions about the Barriers to Prompt Diagnosis of Mild Cognitive Impairment and
Alzheimer's Disease. International Journal of Alzheimer's Disease.
Kaduszkiewicz, H., Bachmann, C., & van den Bussche, H. (2008). Telling “the truth” in
dementia—Do attitude and approach of general practitioners and specialists differ?
Patient Education and Counseling, 220-226.
Kaduszkiewicz, H., Wiese, B., & Van Den Bussche, H. (2008). Self-reported competence,
attitude and approach of physicians towards patients with dementia in ambulatory care:
Results of a postal survey. BMC Health Services Research.
Keshavan, A. P.-S. (2021). Population-based blood screening for preclinical Alzheimer’s disease
in a British birth cohort at age 70. . BRAIN, 434–449.
Khachaturian, Z. S., Khachaturian, A., & Thies, W. (2012). The draft "National Plan" to address
Alzheimer's disease - National Alzheimer's Project Act (NAPA). Alzheimer's &
Dementia, 234-236.
Koch, T., & Illiffe, S. (2010). Rapid appraisal of barriers to the diagnosis and management of
patients with dementia in primary care: a systematic review. BMC Family Practice.
Koh, H. K., & Sebelius, K. G. (2010). Promoting Prevention through the Affordable Care Act.
The New England Journal of Medicine, 1296-1297.
Korthauer, L. E., Denby, C., Molina, D., Wanjiku, J., Daiello, L. A., Drake, J. D., . . . Ott, B. R.
(2021). Pilot Study of an Alzheimer;s disease risk assessment program in a primary care
setting. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring.
Lam, J., Hlavka, J., & Mattke, S. (2019). The Potential Emergence of Disease-Modifying
Treatments for Alzheimer's Disease: The Role of Primary Care in Managing The Patient
Journey. Journal of the American Board of Family Medicine, 931-937.
Langa, K. M., Larson, E. B., Crimmins, E. M., Faul, J. D., Levine, D. A., Kabeto, M. U., &
Weir, D. R. (2017). A Comparison of the Prevalence of Dementia in the United States in
2000 and 2012. Journal of American Medical Association, 177(1), 51-58.
Laske, C. S.-d.-I. (2015). Innovative diagnostic tools for early detection of Alzheimer’s disease.
Alzheimer’s & Dementia, 564-578.
Lazar, R. M., Howard, V. J., Kernan, W. N., Aparicio, H. J., Levine, D. A., Viera, A. J., . . .
White, C. L. (2021). A Primary Care Agenda for Brain Health. Stroke, e295-e308.

159
Lee, J. C., Kim, S., Hong, S., & Kim, Y. (2019). Diagnosis of Alzheimer's disease utilizing
amyloid and tau as fluid biomakers. Experimental and Molecular Medicine.
Leuzy, A. &. (2012). Ethical issues in Alzheimer’s disease: an overview. Expert Review of
Neurotherapeutics, 557–567.
Liss, J., Seleri Assuncao, S., Cummings, J., Atri, A., Geldmacher, D. S., Candela, S. F., . . .
Bittner, T. (2021). Practical Recommendations for timely, accurate diagnosis of
symptomatic Alzheimer's disease (MCI and dementia) in primary care: a review and
synthesis. Journal of Internal Medicine, 310-334.
Liu, J. L., Hlavka, J. P., Hillestad, R., & Mattke, S. (2017). Assessing the Preparedness of the
U.S. Health Care System Infrastructure for an Alzheimer's Treatment. Santa Monica:
RAND Corporation.
Liu, Y., Jun, H., Becker, A., Wallick, C., & Mattke, S. (2023). Detection Rates of Mild
Cognitive Impairment in Primary Care for the United States Medicare Population. J Prev
Alzheimers Dis, 7-12.
Livingston, G., Huntley, J., Sommerland, A., Ames, D., Ballard, C., Banerjee, S., . . . Costafreda,
S. G. (2020). Dementia prevention, intervention, and care: 202 report of the Lancet
Commission. The Lancet, 413-446.
Luck, T., Luppa, M., Sieber, J., Schomerus, G., Werner, P., Konig, H.-H., & Riedel-Heller, S. G.
(2012). Attitudes of the German General Population toward Early Diagnosis of Dementai
- Results of a Representative Telephone Survey. PLOS One, 1-8.
Luig, T., Asselin, J., Sharma, A. M., & Campbell-Scherer, D. L. (2018). Understanding
Implementation of Complex Interventions in Primary Care Teams. Journal of American
Board of Family Practice, 431-444.
Manly, J. J., Jones, R. N., Langa, K. M., Ryan, L. H., Levine, D. A., McCammon, R., . . . Weir,
D. (2022). Estimating the Prevalence of Dementia and Mild Cognitive Impairment in the
US: The 2016 Health and Retirement Study Harmonized Cognitive Assessment Protocol
Project. JAMA Neurology, 1242-1249.
Mansfield, E., Noble, N., Sanson-Fisher, R., Mazza, D., Bryant, J., & Heyn, P. C. (2019).
Primary Care Physicians’ Perceived Barriers to Optimal Dementia Care: A Systematic
Review. The Gerontologist, e697-e708.
Maschke, k. J., & Gusmano, M. J. (2017). Medicare and Amyloid PET Imaging: The Battle Over
Evidence. Journal of Aging & Social Policy, 105-122.

160
Mattke, S., Batie, D., Chodosh, J., Felten, K., Flaherty, E., Fowler, N. R., . . . Zissimopoulos, J.
M. (2023). Expanding the use of brief cognitive assessments to detect early-stage
cognitive impairment in primary care. Alzheimer's & Dementia.
Mattke, S., Cho, S., Bittner, T., Hlavka, J., & Hanson, M. (2020). Blood-based biomarkers for
Alzheimer's pathology and the diagnostic process for a disease-modifying treatment:
Projecting the impact on the cost and wait times. Alzheimer's & Dementia: Diagnosis,
Assessment and Disease Monitoring, 1-8.
Mazurenko, O., Balio, C. P., Agarwal, R., Carroll, A. E., & Menachemi, N. (2018). The Effects
of Medicaid Expansion Under the ACA: A Systematic Review. Health Affairs, 944-950.
Memon, K. N., Shaikh, N., Soomro, R., & Shaikh, S. (2017). Non-Compliance to Doctors’
Advices among Patients Suffering from Various Diseases: Patients’ Perspectives:A
Neglected Issue. Journal of Medicine, 10-14.
Merriam, S. B., & Tisdell, E. J. (2015). Qualitative Research: A Guide to Design and
Implementation. John Wiley & Sons.
Millett, G., Naglie, G., Upshur, R., Jaakkimainen, L., Charles, J., & Tierney, M. C. (2018).
Computerized Cognitive Testing in Primary Care - A Qualitative Study. Alzheimer's
Disease & Associated Disorders, 50-60.
Molineuvo, J. L., Ayton, S., Batria, R., Bednar, M. M., Bittner, T., Cummings, J., . . . Soares.
(2018). Current State of Alzheimer's Fluid Biomarkers. Acta Neurologica, 821-853.
Mormont, E., Bier, J.-C., Bruffaerts, R., Cras, P., De Deyn, P., Deryck, O., . . . Hanseeuw, B.
(2020). Practices and opinions about disclosure of the diagnosis of Alzheimer's disease to
patients with MCI or dementia: a surevy among Belgian medical experts in the field of
dementia. Acta Neuologica Belgica.
Moser, A., & Korstjens, I. (2018). Series: Practical guidance to qualitative research. Part 3:
Sampling, data collection, and analysis. European Journal of General Practice, 9-18.
Nakanishi, M., & Nakashima, T. (2014). Features of the Japanese national dementia strategy in
comparison with international dementia policies: How should a national dementia policy
interact with the public health- and social- care systems? Alzheimer's & Dementia, 10,
468-476.
Namin, A., Vahdat, V., DiGennero, C., Amid, R., & Jalali, M. (2020). Adoption of new medical
technologies: The effects of insurance coverage vs continuing medical education. Health
Policy and Technology, 38-42.
National Council on Aging. (2024, April 7). Alzheimer's Medication Leqembi is Covered by
Medicare—But Access and Effectiveness Questions Remain. Retrieved from National

161
Council on Aging website: https://www.ncoa.org/article/alzheimers-medication-leqembinow-covered-by-medicare-but-access-and-effectiveness-questions-remain
Parker, M., Barlow, S., Hoe, J., & Aitken, L. (2020). Persistent barriers and facilitators to
seeking help for a dementia diagnosis: a systematic review of 30 years of the perspectives
of carers and people with dementia. International Psychogeriatrics, 611-634.
Petersen, S., Winston, L., Phillips, R., Rabin, D., Meyers, D. S., & Bazemore, A. W. (2012).
Projecting US Primary Care Physician Workforce Needs: 2010-2025. Annals of Family
Medicine, 503-509.
Peterson, R. C. (2022). Are plasma markers for Alzheimer's disease ready for clinical use?
Nature Aging, 94-96.
Phillips, J., Dimity Pond, C., Paterson, N. E., Howell, C., Shell, A., Stocks, N. P., . . . Marley, J.
E. (2012). Difficulties in disclosing the diagnosis of dementia. British Journal of General
Practice.
Podhorna, J., Winters, N., & Zoebelein, H. (2020). Alzheimer’s Diagnosis: Real-World
Physician Behavior Across Countries. Adv Ther, 883-893.
Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N., & Rubino, I. (2021). Diagnosis of
Early Alzheimer's Disease: Clinical Practice in 2021. Journal of Prevention of
Alzheimer's Disease.
Porter, M., Larsson, S., & Lee, T. (2016). Standardizing Patient Outcomes Measurement. New
England Journal of Medicine, 503-506.
Prince, M., Ali, G.-C., Guerchet, M., Prina, A., Albanese, E., & Wu, Y.-T. (2016). Recent Global
Trends in the Prevalence and Incidence of Dementia, and Survival with Dementia.
Alzheimer's Research & Therapy, 1-13.
Prins, A., Hemke, F., Pols, J., & Moll van Charante, E. P. (2016). Diagnosing dementa in Dutch
general practice. British Journal of General Practice.
Rahman-Filipak, A., Bolton, C., Grill, J. D., Rostamzadeh, A., Chin, N., Heidebrink, J., . . .
Clark, L. (2023). Biomarker disclosure protocols in prodromal Alzheimer's disease
clinical trials. Alzheimer's & Dementia, 4270-4275.
Rajan, K. B., Barnes, L. L., Wilson, R. S., McAninch, E. A., Weuve, J., Sighoko, D., & Evans,
D. A. (2017). Racial Differences in the Association Between Apolipoprotein E Risk
Alleles and Overall and Total Cardiovascular Mortality over 18 Years. Journal of
American Geriatrics Society.

162
Rajan, K. B., Weuve, J., Barnes, L. L., McAninch, E. A., Wilson, R. S., & Evans, D. A. (2021).
Population estimates of people with clinical Alzheimer's disease and mild cognitive
impairment in the United States (2020-2060). Alzheimer's & Dementia, 1966-1975.
Reale, M., Gonzales-Portillo, I., & Borlongan, C. V. (2019). Saliva, an easily accessible fluid as
diagnostic tool and potent stem cell source for Alzheimer's Disease: Present and future
applications. Brain Research.
Reeves, S., Pelone, F., Harrison, R., & Zwarenstein, M. (2017). Interprofessional collaboration to
improve professional practiceand healthcare outcomes. Cochrane Database of Systematic
Reviews.
Roberts, R., Aakre, J., Kremers, W., Vassilaki, M., Knopman, D. S., Mielke, M. M., . . .
Machulda, M. M. (2018). Prevalence and Outcomes of Amyloid Postivity Among
Persons Without Dementia in a Longintudinal, Population-Based Setting. JAMA
Neurology, 970-979.
Rogers, E. (2003). Diffusion of Innovations. New York: Free Press.
Sabbagh, M., Boada, M., Borson, S., & Chilukuri, M. (2020). Early Detection of Mild Cognitive
Impairment (MCI) in Primary Care. J Prev Alzheimers Dis, 165-170.
Sannemann, L., Muller, T., Waternik, L., Zwan, M., Wimo, A., Stomrud, E., . . . Georges, J.
(2020). General Practitioners' attitude toward early and pre-dementia diagnosis of AD in
five European countries - A MOPEAD project survey. Alzheimer's Dementia.
Schindler, S. E., & Bateman, R. J. (2021). Combining blood-based biomarkers to predict risk for
Alzheimer's disease dementia. Nature Aging, 26-28.
Schindler, S. E., & Bateman, R. J. (2021). Combining blood-based biomarkers to predict risk for
Alzheimer's disease dementia. Nature Aging, 26-28.
Sharma, Y., Cox, L., Kruger, L., Channamsetty, V., & Haga, S. B. (2022). Evaluating Primary
Care Providers' Readiness for Delivering Genetic and Genomic Servcies to Underserved
Populations. Public Health Genomics, 12-21.
Shaw, L. M. (2018). Appropriate use criteria for lumbar puncture and cerebrospinal fluid testing
in the diagnosis of Alzheimer's disease. Alzheimer's & Dementia, 1505-1521.
Snyder, H. M., Perlman, W. R., Egge, R., & Carrillo, M. C. (2018). Alzheimer's disease publicprivate partnerships: Update 2017. Alzheimer's & Dementia, 522-531.
Steenhuysen, J. (2023, October 16). US removes coverage curb on PET scans for Alzheimer's
patients. Retrieved from Reuters: https://www.reuters.com/business/healthcarepharmaceuticals/medicare-removes-coverage-curb-pet-scans-alzheimers-patients-2023-
10-13/

163
Stewart, T. V., Loskutova, N., Galliher, J. M., Warshaw, G. A., Coombs, L. J., Staton, E. W., . . .
Pace, W. D. (2014). Practice patterns, beliefs, and perceived barriers to care regarding
dementia: A report from the American Academy of Family Physicians (AAFP) National
Research Network. Journal of the American Board of Family Medicine, 275-283.
Sullivan, K., Muscat, T., & Mulgrew, K. (2007). Knowledge of Alzheimer's Disease Among
Patients, Carers, and Noncarer Adults Misconceptions, Knowledge Gaps, and Correct
Beliefs. Topics in Geriatric Rehabilitation, 137-148.
Tan, Z. (2022). The dawn of disease modification for Alzheimer's disease: Hope and peril.
Journal of the American Geriatrics Society, 1661-1663.
Thunell, J., Jacobson, M., & Joe, E. B. (2022). Medicare's Annual Wellness Visit and diagnosis
of dementias and cognitive impairment. Alzheimer's Dementia.
Trunz, L., Gandhi, A. V., Karambelkar, A. D., Lange, S. M., Rao, V. M., & Flanders, A. E.
(2021). National Trends in Lumbar Puncture from 2010 to 2018: A Shift Reversal from
the Emergency Department to the Hospital Setting for Radiologists and Advanced Care
Providers. American Journal of Neuroradiology, 206-210.
Turner, R. S., Stubbs, T., Davies, D. A., & Albensi, B. C. (2020). Potential New Approaches for
Diagnosis of Alzheimer's Disease and Related Dementias. Frontiers in Neurology.
Tyrrell, M., Hedman, R., Fossum, B., Skovdahl, K., Religa, D., & Hilleras, P. (2021). Feeling
valued versus abandoned; Voices of persons who have completed a cognitive assessment.
International Journal of Older People Nursing.
van der Schaar, J., Visser, L. N., Bouwman, F. H., Ket, J. C., Scheltens, P., Bredenoord, A. L., &
van der Flier, W. M. (2022). Considerations regarding a diagnosis of Alzheimer's disease
before dementia: a systematic review. Alzheimer's Research & Therapy.
Varabyova, Y., Blanckart, C., Greer, A., & Shreyogg, J. (2017). The determinants of medical
technology adoption in different decisional systems: A systematic literature review.
Health Policy, 203-242.
Vellas, B., & Aisen, P. (2021). New Hope for Alzheimer's Disease. Journal of Prevention of
Alzheimer's Disease.
Vernooij-Dassen, M., Moniz-Cook, E., Woods, R., De Lepeleire, J., & Leuschner, A. (2005).
Factors affecting timely recognition and diagnosis ofdementia across Europe: from
awareness to stigma. INTERNATIONAL JOURNAL OF GERIATRIC PSYCHIATRY, 377-
386.
Vuong, Z. (2018). USC, Harvard Hospitals and Mayo Clinic to Build National Alzheimer's
Research System. Los Angeles: States News Service.

164
Wangler, J., & Jansky, M. (2021). Factors influencing general practitioners' perceptions of and
attitude towards dementia diagnostics and care - results of a survey among primary care
physicians in Germany. Wien Med Wochenschr, 165-173.
Wertz, F. J., Charmaz, K., McMullen, L. M., Josselson, R., Anderson, R., & McSpadden, E.
(2011). Five Ways of Doing Qualitative Analysis: Phenomenological Psychology,
Grounded Theory, Discourse Analysis, Narrative Research, and Intuitive Inquiry. New
York: Guildford.
Weuve, J., Barnes, L. L., Mendes de Leon, C., Rajan, K. B., Beck, T., Aggarwal, N. T., . . .
Evans, D. A. (2018). Cognitive Aging in Black and White Americans. Epidemiology,
151-159.
Zetterberg, H., & Bendlin, B. B. (2021). Biomarkers for Alzheimer's disease - preparing for a
new era of disease-modifying therapies. Molecular Psychiatry, 296-308.
Zhu, Y., Chen, Y., Crimmins, E., & Zissimopoulos, J. M. (2021). Sex, Race, and Age
Differences in Prevalence of Dementia in Medicare Claims and Survey Data. J Gerontol
B Psychol Sci Soc Sci, 596-606.
Zissimopoulos, J., Crimmins, E., & St. Clair, P. (2015). The Value of Delaying Alzheimer's
Disease Onset. Forum for Health Economics and Policy, 18(1), 25-39.
Zissimopoulos, J., Jacobson, M., Chen, Y., & Borson, S. (2022). Knowledge and Attitudes
Concerning Aducanumab Among Older Americans After FDA Approval for Treatment
of Alzheimer's Disease. JAMA Network Open.

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Appendix A: Email Request for Participation in Study
Dear Dr. __________,
My name is Hossein Pourmand, and I am a doctoral candidate at the USC Price School of Public
Policy in the Health Policy program.
For my dissertation, I am conducting interviews with primary care physicians in Los Angeles
County. My dissertation study seeks to better understand the barriers to the adoption of
new diagnostic tools for Alzheimer’s Disease and related dementias (ADRD). My
dissertation is supervised by Professor Julie Zissimopolous of the USC Price School of Public
Policy and approved by the University of Southern California’s Institutional Review Board (IRB
Protocol UP-21-00472).
I am reaching out to respectfully request an appointment for a 60-minute interview with you via
Zoom. Participation in this study is voluntary and participants can withdraw from the interview
at any time without consequence. I would be extremely grateful for your participation. This study
will increase our understanding of barriers to the adoption of emerging diagnostic tools for
ADRD in primary care. This could in turn help inform future health policies to increase the
likelihood of success of these diagnostic tools.
I greatly appreciate your kind consideration of this request and I look forward to hearing from
you at your earliest convenience.
Please do not hesitate to contact me if I can address any questions or concerns.
Sincerely,
Hossein Pourmand
Doctoral Candidate in Health Policy, USC Price School of Public Policy
University of Southern California

166
Appendix B: Guiding Questions Semi-structured Interviews
Question Prompts and/or Sub-question(s) Purpose / what I am trying to elicit / understand
Estimated
Time
Required for
Q&A
1. What types of patients are you
seeing most often (more than 50% of
the time)?
Approximately what percentage of your
patients are older (65 and over), late middleage (45 to 64), and early middle-age (35 to
44). Which races and ethnicities?
Understanding patient profile (middle age, older, racial / ethnic
mix etc..).
Who are the predominant insurers for your
patients (Medicare, Medi-cal, private,
uninsured)? What % are uninsured
patients?
Further understanding patient profile and also how the office
is getting reimbursed for services. Third party payer policies
and limitations on reimbursements may be barrier for adoption
of novel diagnostic tests.
2. How is your practice structured?
Do have nurse practitioner(s) or
physician assistant(s)?
If so, what are some of their roles and
responsibilities when it comes to interaction
with patients or any diagnostic tests? Are
they checking vital signs only or are they
asking patients about their health issues or
concerns?
Understanding the structure of the practice; who is doing what
(i.e. physician, PA, NP etc..); understanding how much time
the physician spends with patients, particularly those who
have cognitive concerns or issues and may require more
interaction time. Understanding if lack of time is a barrier to
adoption of new diagnostic tools.
In a typical office visit how much time do
you spend with an older (65 and older), a
late middle-age and early middle-age
patient?
Understanding if patients in different age groups require more
or less time and if lack of time could be a barrier for the
adoption of new diagnostic tools for different age groups.
How much time do you typically
spend going over results from annual
diagnostic tests (such as blood,
urine, imaging) that you have
ordered for a patient?
How often (approximate % of time) does a
patient ask questions or engage you in a
discussion about a particular diagnostic test
result? Are results shared electronically?
Understanding physician's current commitment of time for
diagnostic test results.
What impact does asking such questions or
protracted discussions about concerns
have on your workflow and ability to provide
care to other patients?
Understanding if lack of time is a barrier for the adoption of
new diagnostic tests.
Do you think that your colleagues
are aware of the Annual Wellness
Visits (AWV) for Medicare patients?
What percentage of your older Medicare
patients come in every year for their AWV?
How long (minutes) are these visits?
Understanding their awareness of this benefit which includes
reimbursement for a cognitive assessment.
1-2 minutes
How often (% of time) do you see a
patient who has cognitive concerns
or exhibiting cognitive issues?
Are these mostly in the 65 and over or are
you seeing some in younger patients?
What % approximately in the younger
patients (late middle-age and early middleage)?
Understanding the extent to which cognition issues are part of
the physician's daily encounters with patients.
Do you wait for the patient to initiate or do
you initiate discussing cognition?
Understanding if
Please describe briefly the ways you
conduct a cognitive assessment.
Understanding which of the current diagnostic tools or
methods they are and are not using.
If PCP states that a cognitive test is
performed: How would you describe the
amount of time you can devote for
assessing cognition for these patients? Do
you perform this cognition test or does a
Clinical Nurse, PA or NP? Which of these
current diagnostic tools or methods do you
use more often and why?
Understanding who is administering the test in different
settings. Note: Any clinician eligible to report evaluation and
management (E/M) services can offer this service. Eligible
providers include:
Physicians (MD and DO)
Nurse practitioners
Clinical nurse specialists
Physician assistants
What are the ways that you currently
diagnose AD?
On a scale of 1 to 10, how comfortable are
you currently with making an AD diagnosis?
Understanding what they are doing now, how comfortable
they are with making AD diagnosis (as a proxy for knowledge)
and how that may impact their decision to implement new
diagnostic tools.
On a scale of 1 to 10, how confident are
you in interpreting cognitive testing results?
Understanding the level of confidence in interpreting results
using current AD diagnosis tools (knowledge) and how that
may impact decision to implement new diagnostic tools.
How has the annual cognition test changed
your workflow and time spent with older
patients? How much more time (in minutes)
are you needing to spend with them?
Understanding what they are doing now and how that may
impact their decision to implement new diagnostic tools.
In a scale of 1 to 10, how
comfortable are you with disclosing
AD diagnosis to patient and/or their
family/caregiver?
Scores less than 5: What are some of the
reasons for your being uncomfortable?
Understanding if lack of comfort with disclosing an AD
diagnosis may be a barrier in adopting novel diagnostic tools.
Understanding if training, knowledge, lack of confidence in
interpreting results from current diagnosis tools and methods
(earlier questions), or patient factors such as stigma
associated with AD diagnosis may impact decision to
implement new diagnostic tools.
2-3 minutes
2-3 minutes
2 minutes
2-3 minutes
4-5 minutes
3-4 minutes

167
Question Prompts and/or Sub-question(s) Purpose / what I am trying to elicit / understand
Estimated
Time
Required for
Q&A
At what point do you refer patients
you have formally diagnosed with AD
or you think may have AD to
specialists? Note: the participants will
be informed that for this study, AD
specialists are: neurologists, geriatric
psychiatrist, and geriatrician.
What % of patients with suspected AD or
AD diagnosis do you refer to a specialist? If
not referring all AD patients to a specialist:
What are some of your reasons for not
doing so? What are some of the factors
that go into your decision in selecting a
patient for referral to a specialist? i.e. which
patients?
Bernstein et al (2019) found that 54% of PCP referred more
than half their patients to a neurologist or specialist. The
responses to these questions could show if this applies to
study participants as well.
How would more accurate diagnostic tools
impact the decision to refer to a specialist?
Understanding impact on decision to implement, if blood test
results can help identify and prioritze referrals to specialists.
Is AD an older person's disease or a
disease of the middle-aged?
Do you view AD as having discrete and
defined clinical stages or as a multifaceted
process moving along a seamless
continuum? If defined by clincial stages,
what are those? If you understand it as a
seamless continuum, please state your
understanding of it.
Understanding how current a PCP's knowledge of AD,
dementia and cognitive issues is in different settings. Lack of
adequate training and keeping up with advances can impact
decision to implement new diagnostic tools.
What are some of the latest
advances in AD diagnosis that you
have become aware of in the last 2-3
years?
Have you heard about C2N Diagnostic and
their PrecivityAD blood test? If yes: What is
your understanding of that test in terms of
applicability, feasibility, costs, third-party
payer coverage?
Understanding how current their knowledge is about emerging
and future diagnosis tools such as blood-based biomarkers.
This could be a barrier for the adoption of the new diagnostic
tools.
Assuming that there is third-party
payer coverage for an AD blood test,
what are some considerations or
factors that would go into your
decision to incorporate a blood test
for AD? What if the test is prognostic
vs. diagnostic? What if it has to be
used in conjunction with current
diagnostic tools to confirm and
improve on diagnostic accuracy for
patients exhibiting cognition issues?
Have your ordered C2N Diagnostic's
Precivity AD blood test for any of your
patients? if so, how many times or how
often (% of time)?
Given that the test is currently not covered
by third-party payers, what factors went into
your decision? Did the patient expressly
request it or did you recommend? What
changes would you need to make in your
practice if a blood-biomarker for presymtomatic AD becomes clinically available,
along with therapeutics that can delay the
onset of symptoms?
Understanding the extent to which the lack of insurance
coverage would be a barrier. Currently the C2N Diagnostic's
PrecivityAD blood test is not covered by any third-party payer
and costs $1250. C2N is offering financial assistance to
patients to bring the out of pocket cost down to between $25
and $400 depending on financial capability. Understanding
how such a test may be prescribed in different settings.
What are some currently FDAapproved drug treatments for AD?
How often (% of time) and to which patients
do you prescribe these drugs? How do you
assess these in terms of risk-benefit and
cost-benefit for your patients?
1. Understanding 1) their knowledge about current and
recently FDA-approved (ex. aducanumab) therapeutics, and
2) how they assess cost-benefit and risk-benefit of
therapeutics. Both can impact decision to implement new
diagnostic tools.
Have you prescribed Aducanumab to any
patients? What were some factors in your
decision to prescribe or not prescribe?
Cost? Third-party payor coverage?
Efficacy?
Understanding decision making for a new therapeutic with
limited data and consensus on benefits and how that may
also impact decision on new diagnostic tools.
What are some of the latest
advances in AD therapeutics that
you have become aware of in the
last 2-3 years?
Understanding how current their knowledge about potential
future therapeutics is as lack of knowledge could be a barrier
for the adoption of the new diagnostic tools.
How do you come across such
information? CME courses?
Conferences? Colleagues? enewsletters?
Understanding how the PCP receives training and CME
information.
Is there anything that I have missed
or question that I should have
asked?
2-3 minutes
8-10 minutes

168
Appendix C: Institutional Review Board Approval
University of Southern Ca lifornia Institutional Review Board
1640 Marengo Street, Suite 700
Los Angeles, California 90033-9269
Telephone: (323) 442-0114
Fax: (323) 224-8389
Email: irb@usc.edu
Date: Jul 23, 2021, 04:53pm
Action Taken: Approve
Principal
Investigator:
Hossein Pourmand
SOL PRICE SCHOOL OF PUBLIC POLICY
Faculty Advisor: Dr. Julie Zissimopoulos
SOL PRICE SCHOOL OF PUBLIC POLICY
Project Title: New Alzheimer's Diagnostic Tools: Barriers and Challenges in Primary Care
Study ID: UP-21-00472
The University of Southern California Institutional Review Board (IRB) designee revi ewed your iStar application and attachments on
07-23-21.
Based on the information submitted for review, this study is determined to be exempt from 45 CFR 46 a ccording to §46.104(d) as
category (2).
As research which is conside red exempt according to §46.104(d), this proje ct is not subject to requirements for continuing revi ew. You
are authorized to conduct this research as approved.
If there are significant changes that increase the risk to subjects or if the funding ha s changed, you must submit an amendment to the
IRB for review and approval. For other revisions to the application, use the “Send Message to IRB” link.
The materials submitted and considered for review of this project included:
1. iStar application dated 05-24-21, updated 07-22-21
2. UP-21-00472 IRBA-edited protocl HP.docx(0.02)
3. Interview Questions - Hossein Pourmand.docx(0.01)
NOTES TO PI:
No permission is granted to access medical records.
INFORMATION SHEET
Consent and recruitment documents are not required to be uploaded for exempt studies; however, researchers are reminded that USC
follows the principles of the Belmont Report, which requires all potential participants to be informed of the research study, their rights
as a participant, confidentiality of their data, etc. Therefore, please utilize the Information Sheet Template available on the IRB website
(http://oprs.usc.edu) and revise the language to be specific to your study. This document will not be reviewed by the IRB. It is the
responsibility of the researcher to make sure the document is consistent with the study procedures listed in the application.

169

170
Appendix D: List of Codes from Manual Open Coding of 24 Interview Transcripts
1. Diagnosis Complexity
2. Disclosure Sensitivity
3. Time Constraints
4. Resource Shortage
5. Inadequate Reimbursement
6. Patient Skepticism
7. Patient Education
8. Test Reliability
9. Knowledge Deficit
10. Training Gaps
11. Cost Burden
12. Insurance Limitations
13. Workflow Disruption
14. Integration Concerns
15. Practice Resistance
16. Efficacy Doubts
17. Perceived Treatment Inefficacy
18. Psychological Impact
19. Communication Barriers
20. Ethical Concerns
21. Decision-making Complexity
22. Adoption Reluctance
23. Policy Ambiguity
24. Clinical Uncertainty
25. Patient Trust Issues
26. Lack of Awareness
27. Perceived Innovation Risks
28. Cultural Attitudes
29. Diagnostic Accuracy
30. Support Infrastructure
31. Evidence-based Hesitation
32. Institutional Barriers
33. Professional Skepticism
34. Patient Compliance Issues
35. Diagnostic Familiarity
36. Lack of Long-term Evidence
37. Reimbursement Complexity
38. Financial Incentives
39. Clinical Relevance
40. Adoption Support Needs

Abstract (if available)

Abstract Despite many advances in the past two to three decades in our understanding of Alzheimer’s disease, timely and accurate diagnosis remains a significant issue. Recent advances in biomarker-based diagnostic tools and their increasing availability in clinical practice offers optimism in addressing these issues. However, the timely and effective adoption of innovative biomarker-based diagnostic tools for Alzheimer's Disease (AD) in primary care may face significant challenges. This qualitative study, underpinned by the Diffusion of Innovation theory, explores the barriers to adopting these tools among primary care physicians. Using grounded theory methodology, semi-structured interviews were conducted with 24 primary care physicians practicing in various settings in Southern California, including solo practices, small group practices, and outpatient clinics within large health systems such as academic medical centers.
The study identified six key themes that highlight the complexity of adopting new diagnostic technologies in primary care: Clinical and Diagnostic Complexities, Systemic and Operational Barriers, Economic and Financial Challenges, Patient-related Issues, Physician Knowledge and Training Gaps, and Therapeutic and Treatment Limitations. These themes illustrate the multifaceted nature of the barriers, ranging from the lack of time and resources, workflow disruptions, and insufficient training, to patient skepticism and concerns about the accuracy and reliability of new diagnostic tools. The extent to which each theme is a barrier varies based on the type of practice, years of experience, and, to a lesser extent, gender.
The findings underscore the need for targeted strategies to support primary care physicians in integrating innovative diagnostic tools into their practices. This includes improving access to resources, streamlining workflows, and enhancing physician education and training. Furthermore, the study's results align with the Diffusion of Innovation theory, demonstrating that the perceived relative advantage, complexity, and compatibility of new diagnostic tools significantly influence their adoption in primary care settings.
This research contributed to the broader understanding of how primary care physicians navigate the challenges of diagnosing Alzheimer’s Disease and offers insights into how healthcare systems and policymakers can better support the adoption of innovative diagnostic technologies. Future research should build on these findings using a mixed-methods approach to further explore and quantify these barriers across a larger and more diverse population of primary care physicians.

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Asset Metadata

Creator Pourmand, Hossein (author)

Core Title Adoption and implementation of innovative diagnostic tools for Alzheimer's Disease: challenges and barriers in primary care

School School of Policy, Planning and Development

Degree Doctor of Policy, Planning & Development

Degree Program Policy, Planning and Development

Degree Conferral Date 2024-08

Publication Date 09/03/2024

Defense Date 08/29/2024

Publisher Los Angeles, California (original), University of Southern California (original), University of Southern California. Libraries (digital)

Tag Alzheimer's disease,dementia,Diagnosis,innovative diagnostics,OAI-PMH Harvest,primary care

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Zissimopoulos, Julie M. (committee chair), Dalton, Philip (committee member), Natoli, Deborah J. (committee member)

Creator Email hpourman@usc.edu,hpourmand@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC11399A774

Unique identifier UC11399A774

Identifier etd-PourmandHo-13480.pdf (filename)

Legacy Identifier etd-PourmandHo-13480

Document Type Dissertation

Format theses (aat)

Rights Pourmand, Hossein

Internet Media Type application/pdf

Type texts

Source 20240904-usctheses-batch-1207 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu