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Physician profiling and clinical pathways: Combining the tools to change physician resource utilization

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Physician profiling and clinical pathways: Combining the tools to change physician resource utilization

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Content PHYSICIAN PROFILING AND CLINICAL PATHWAYS: COMBINING THE TOOLS TO CHANGE PHYSICIAN RESOURCE UTILIZATION by Earl Glendon Greenia A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (PUBLIC ADMINISTRATION) December 2004 Copyright 2004 Earl Glendon Greenia Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. UMI Number: 3155412 INFORMATION TO USERS The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleed-through, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. ® UMI UMI Microform 3155412 Copyright 2005 by ProQuest Information and Learning Company. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. ProQuest Information and Learning Company 300 North Zeeb Road P.O. Box 1346 Ann Arbor, Ml 48106-1346 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table of Contents List of Tables v Abstract vii Chapter I. Formulation and Definition of the Problem Introduction 1 An Overview of Quality in Health Care 3 Need for the Study 5 Purpose of the Study 8 Definitions 9 Chapter II. Overview of Hospital, Intervention and Study Design Overview of Study Hospital 11 Overview of the Intervention 13 Physician Profiling 13 Benchmarking 16 Clinical Pathways 17 All-Patient Refined Diagnosis Related Groups 17 Profile Development in the Study Organization 18 Pathway Development in the Study Organization 20 Intervention Dissemination 22 Overview of the Study Design 23 Selection of Diagnostic Groups 23 Experimental Group 24 Control Group 25 Comparability between Groups 26 Study Population 27 Summary 27 Chapter III. Literature Review and Hypothesis Development Introduction 28 Changing Physician Behavior 29 Social Learning Theory 30 Introduction to Innovation Diffusion 33 Feedback 34 Clinical Audits 34 Physician Profiling 36 Benchmarking 39 Communication 43 Clinical Pathways as Communication 43 ii Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Profiles and Pathways as Innovation 47 Relative Advantage 50 Profiles and Clinical Outcomes 50 Pathways and Clinical Outcomes 51 Relative Advantage 52 Time and the Rate of Adoption 52 Physician Leaders 54 Social Identity 54 Organizational Citizenship Behavior 55 Complexity 58 Summary 59 Chapter IV. Methodology Introduction 60 Review of Previous Methods 60 Statistical Design 64 Unit of Analysis 65 Data Elements 65 Assumptions 67 Data Preparation 68 Data Analysis 69 Hypothesis la 69 Hypothesis lb 69 Hypothesis 2 69 Hypothesis 3 70 Hypothesis 4 70 Hypothesis 5 71 Hypothesis 6 71 Multiple Regression Analysis Model 72 Summary 73 Chapter V. Findings Descriptive Analysis 74 Multiple Regression Analysis 77 Hypothesis la 81 Hypothesis lb 82 Hypothesis 2 82 Hypothesis 3 83 Hypothesis 4 85 Hypothesis 5 85 Hypothesis 6 86 Summary 87 iii Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Chapter VI. Discussion General Limitations 88 Use of Profiles and Pathways 89 Rate of Adoption 91 Cultural Integration and Physician Leadership 91 Complexity 93 Future Research 93 Bibliography 95 Appendices Appendix 1, Text of cover letter that accompanied the profile 103 Appendix 2, Text of User Guide 104 Appendix 3, Sample Profile Report 105 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. List of Tables 2-1. Experimental APRDRG Group 2-2. Control APRDRG Group 2-3. Size of Control and Experimental Group 5-1. Experimental APRDRGs, Pre and post intervention, Length of Stay & Total Charges 5-2. Control APRDRGs, Pre and post intervention, Length of Stay and Total Charge 5-3. Readmissions and Complications, Experimental and Control Groups 5-4. Control Group Examined for Spill-Over Effect 5-5. Correlation Table 5-6. Average Length of Stay as Dependent Variable, Experimental Group 5-7. Average Total Charge as Dependent Variable, Experimental Group 5-8. Average Length of Stay as Dependent Variable, Control Group 5-9. Average Total Charge as Dependent Variable, Control Group 5-10. Experimental APRDRGs, Pre and post intervention, ANOVA Tests Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 5-11. Control APRDRGs, Pre and post intervention, ANOVA Tests 82 5-12. Experimental APRDRGs, Chi-Square Test, Complications and Readmissions 83 5-13. Control APRDRGs, Chi-Square Test, Complications and Readmissions 83 5-14. Experimental Group, Average Length of Stay by Quarter 84 5-15. Experimental Group, Average Total Charge by Quarter 84 5-16. Control Group, Average Length of Stay by Quarter 85 5-17. Control Group, Average Total Charge by Quarter 85 5-18. Average Length of Stay, Opinion leaders v Non-Opinion leaders, 1995 v 1996 86 5-19. Average Total Charge, Opinion leaders v Non-Opinion leaders, 1995 v 1996 86 5-20. Summary of Hypotheses and Findings 87 vi Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Abstract Published studies on the use of clinical pathways and physician profiling to change physician behavior have demonstrated varying impact. Researchers have suggested combining various approaches and tools, but have not evaluated combination interventions. This study contributes to the literature by applying various theories to evaluate physician profiling, benchmarking, and pathway dissemination in a community hospital. The population for the study included physicians who cared for patients within targeted diagnostic groups (APRDRGs) during calendar year 1996, with the prior year used as the baseline. The experimental group consists of 10 APRDRGs. To ensure consistent comparison, only physicians who provided care in both 1995 and 1996 were included in the analysis. In 1995, there were 256 physicians in the experimental group caring for 3,944 patients. In 1996, these same physicians provided care to 3,178 patients. The control group consists of 10 APRDRGs. In 1995, there were 246 physicians in the control group and 1,377 patients. In 1996, there were 213 physicians and 1,018 patients. One-way analysis of variance, Student’s-T and chi-square tests were used to examine differences in means for resource utilization in both the pre and post intervention for physicians receiving intervention and those not receiving, physician leaders and non- leaders, and clinical outcomes. Regression analysis was used to examine effect of cultural integration and pathway complexity. vii Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The results suggest that the combined dissemination of physician profiles and clinical pathways may change physician behavior. Specifically, overall length of stay and total charges declined for physicians when provided the intervention. There were no significant changes in readmission and complication rates between the pre and post intervention periods. The hypothesis that resource utilization patterns would be lower for physician leaders than non-leaders was not supported. Nor was the hypothesis that resource utilization would be lower for those physicians who were more culturally integrated into the organization versus those less integrated. The hypothesis that the less complex the clinical pathway, the greater the reduction in resource utilization patterns was supported. Support was also provided for the hypothesis that resource utilization patterns will decline over time. The results also suggested that providing profiles and guidelines for a specific set of diagnoses and procedures may not have a beneficial spillover effect on diagnoses and procedures that differ in clinical nature. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Chapter I Formulation and Definition of the Problem Introduction Over the last two decades the health care industry has been radically transformed by the federal government’s reaction to increasing health costs that have exceed inflation and threatened its capacity to provide continued coverage for the indigent and elderly. One of the most dramatic changes occurred in 1983 when the Health Care Financing Administration replaced Medicare’s traditional fee-for-service reimbursement structure with a prospective payment system. This challenged the belief, long propagated by physicians and generally accepted by others, that permitting the doctor to serve exclusively as the agent of the patient best protects the patient's interests. In replacing retrospective reimbursement with prospective payment, the Federal government, in consultation with the medical profession, created Diagnosis Related Groups (DRGs). This system assigns patients to mutually exclusive groups based on clinical factors such as medical diagnosis, operative procedure, co morbidities and complications. Under the retrospective payment system, hospital payment was based on actual costs; with DRGs they are paid a fixed amount regardless of cost. Thus, the financial incentive for hospitals changed from providing more care to providing less care. 1 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. With this shift, cost control became one of the most important management strategies that differentiated successful from unsuccessful hospitals. Beginning in the early 1990s, hospital executives implemented a variety of strategies to address escalating expenses, and recognized that reducing length of stay was paramount in containing costs (Cleverley and Harvey 1992). It is the practice of physicians, through ordering tests and treatments, which largely determines the financial success of the hospital in a managed care environment. Physician decisions directly and indirectly influence the cost of care. Thus, one way to reduce the cost of care is to influence the physician’s decision-making process. Methods to influence physician behavior and reduce length of stay have included utilization review, benchmarking, physician profiling, and the use of clinical pathways. Experts have recommended combining these methods, since traditional strategies, such as continuing medical education conferences, have had little direct impact on changing professional practice (Davis, et al., 1995). The managed care environment continues to pressure physicians and hospitals to reduce the cost of care without negatively effecting clinical outcomes. One way to meet this challenge is to reduce unnecessary care or services provided to patients during their hospital stay. Many institutions have implemented clinical pathway programs designed to enhance physician awareness of best practices, with the goal of reducing unnecessary treatment and ultimately costs. Similarly, physician profiles have been used to make physicians aware of how their practice patterns impact cost by 2 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. comparing their performance to their colleagues. The use of feedback and profiling is based on the observation that physicians usually know little about their aggregate resource consumption patterns and even less about their peers. The rationale for providing feedback and profiles is based on the assumption that physicians have a strong professional motivation to conform to generally accepted practices and provide care in a manner similar to peers. An Overview of Quality in Health Care Despite uncertainty about how to define and measure clinical quality, interest in quality management and outcomes remains keen. The increasing focus on quality stems from recognizing that value is only achieved by balancing quality with cost. Measuring, monitoring, and improving outcomes have broad implications for hospitals in managed care markets. To better understand the research, a brief overview of quality management follows. Donabedian (1980) argued that quality could be evaluated based on structure, process, and outcomes. Structure encompasses physical factors, such as buildings; and professional and institutional factors, like the regulatory and financing environments in which care is delivered. Process refers to the actions that health-care providers take to deliver care, such as performing examinations, ordering tests, and prescribing medications. Outcomes are the end result of the process interventions; i.e., the effect on the patient's health. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Much of the current focus is on exploring process and outcome measures. There are advantages to using process measures instead of outcome measures for performance evaluation purposes. It is easier for healthcare providers to accept responsibility for their actions in providing care rather than for their patients' outcomes, because there are numerous uncontrollable factors that affect outcomes. Process measures are also useful in evaluating the quality of care for chronic conditions for which the final outcome may take years to determine, such as congestive heart failure or pulmonary disease. Thus, it is convenient to concentrate on process measures rather than outcomes measures for performance measurement. However, there are several clinical outcomes measures that are relatively easy to obtain, such as readmission rates, nosocomial infections, and surgical complications. Quality management is a structured, systematic process for creating organization-wide participation in planning and implementing continuous improvement. The science of quality management is a diverse collection of concepts and tools developed in the fields of statistics, engineering, operations research, management science, market research and psychology. Quality management concentrates on changing complex systems and processes in order to continually improve organizational services and outputs. There are four dimensions required for a successful program: 1) the cultural dimension, 2) the technical dimension, 3) the strategic dimension, and 4) the structural dimension (Donabedian 1980). The cultural dimension refers to the underlying beliefs, values, norms, and 4 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. behaviors of the organization that support continuous quality improvement (CQI) efforts. The technical dimension refers to the extent to which employees have been trained in CQI tools and group decision-making processes that support improvement efforts. A structured problem-solving approach that incorporates statistical methods to diagnose problems and measure progress is essential. The strategic dimension refers to the extent to which the organization’s improvement efforts are focused on key priorities, with emphasis on the link between the improvement efforts and the organization’s fundamental business objectives. Lastly, the structural dimension refers to inter-organizational entities, such as top leadership, project teams, task forces, work groups, and reporting mechanisms. This dimension integrates the cultural, technical, and strategic dimensions. Need for the Study Healthcare organizations have suffered a steady decline in operating margins in recent years while facing increased competition and pressure to provide higher levels of customer service, quality of care, and innovation in delivery. The ability to rapidly find, evaluate, and implement change that will lead to strategic improvement is critical. More than half of the physicians in the United States are subjects of either clinical or economic profiling (Emmons and Wozniak, 1994). Presenting such peer- comparison information feedback to physicians, attempts to stimulate consensus on treatment alternatives and allow them to make better-informed decisions about 5 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. resource inputs. Even though the results of before-and-after studies on profiling vary, profiles are widely used as an information feedback mechanism. Unfortunately, most studies on profiling have serious methodological limitations that restrict the strength of their conclusions (Epstein 1991). Further, there have been few investigations that analyze the difference in the effectiveness of profiling under different circumstances. Epstein (1991) argues the need to identify factors that determine the effectiveness of different interventions, given the complexity of changing physician behavior. The Physician Payment Review Commission (1992) found that most profiling studies were limited to the use of a specific treatment or service, such as laboratory tests or pharmaceutical agents, instead of examining all services associated with a clinical encounter. The issue of high costs is closely associated with the provision of multiple services that may or may not be related. In their meta-analysis of randomized results, Balas, et ah, (1996), discovered that while some randomized clinical trials of information feedback have been successful in changing practice patterns, several other trials indicated inconclusive or non-significant results. The randomized, controlled trial literature suggests that profiling can produce a modest, but statistically significant effect on changing physician behavior (Kim, et ah, 1999). Clinical pathways and physician profiling have become popular tools for changing physician utilization patterns. To date, the published studies demonstrate varying impact on ability to improve clinical care. Spoeri and Ullman (1997) argue that the need for profiling will continue for two reasons: there will be continued 6 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. pressure to reduce healthcare costs, and reluctance to micromanage physician decisions about clinical resource use. It is clear that further study is required on the effects of modifying important characteristics, such as the content, source, timing, recipient, and format of feedback. While researchers have suggested combining various approaches, they have not been able to empirically ascertain the best mix of complementary interventions (Thomson, et al., 1999). A meta-analysis performed by Bero (1998) suggests that passive dissemination of information and small-dose education are generally ineffective, but guideline dissemination is effective. He found disparate results for any single tool and concluded that the use of multiple tools is more effective. Thompson (1997) argues that performance measurement is most useful when used as a formative tool as part of a broad set of quality-improvement activities. However, none of the existing research has examined the impact of a combined program consisting of profding, benchmarking, and clinical pathways. Goldfield (1999) believes significant medical leadership and support is critical in gaining physician acceptance. Here too, no studies have examined whether physician participation in the development of such programs impacts the effectiveness of the intervention. Lastly, little of the existing research examines physician profiling or the use of clinical guidelines from any major theoretical perspective. This research draws from social learning, diffusion, social identity and organizational citizenship behavior theories to develop a framework to review these interventions. 7 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Purpose of the Study This study contributes to the healthcare management literature by evaluating a comprehensive intervention implemented at a 350-bed community hospital in southern California. A pragmatic goal of this research is to assist hospital administrators with implementing similar programs. Additionally, this study attempts to address some of the weaknesses of previous research identified in the previous section. Specifically, this study examines: 1. All services (inputs) associated with the hospital encounter, as measured by total charges. 2. Regular dissemination of profiles and pathways over the long-term. (As will be demonstrated in chapter three, most of the existing studies focus on relatively short periods of time). 3. Changes in both clinical outcomes measures resource utilization metrics. 4. Some important characteristics of profiling and pathways, such as the content, source, timing, and format. 5. The role of physician leaders in developing and disseminating profiles and pathways. 6. The impact of a combined program consisting of profiling, benchmarking, and clinical pathways. 7. Profiling and clinical pathways from different theoretical perspectives. 8 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. This empirical study seeks to address the following research questions: 1. Do resource utilization patterns (as measured by length of stay and total charges) differ between physicians who receive profdes and clinical pathways versus those who do not receive profdes and clinical pathways? 2. Is there a difference in outcomes (as measured by readmission and complication rates) between physicians who receive profdes and clinical pathways versus those who do not receive profdes and clinical pathways? 3. Do resource utilization patterns differ between those physicians culturally integrated in the organizational culture versus those physicians who are not as culturally integrated? 4. Do resource utilization patterns differ between physician leaders who receive profdes and clinical pathways versus the non-leader physicians who receive profdes and clinical pathways? 5. Is there is an adoption rate for physician use of profdes and clinical pathways? (As measured by changes in resource utilization over time). 6. Does the complexity of the clinical pathway impact acceptance and use of the tool, as measured by resource utilization. Definitions Key terms defined for the purposes of this study: 1. Benchmarking: the comparison of a particular process or outcome against an identified best practice. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 2. Clinical Guideline: systematically developed statements regarding preferred clinical management strategies used to educate or assist physician decision-making under specific clinical circumstances. 3. Clinical Pathway: disease or procedure-specific operational guidelines that provide recommendations for delivery of clinical care, displayed by day of hospitalization in a modified Gantt chart format. 4. Feedback: the provision of clinical and administrative data to physicians about their own practice and outcomes. 5. Information Sharing: the distribution of clinical pathways to physicians. 6. Physician Leader: Physicians who held leadership roles (elected medical committee member, elected department chair, appointed medical directors), at any point during the study, actively participated in the profile or pathway development process or were identified as influential by the organization. 7. Physician Profile: physician-specific reports with patient-level and procedure-specific detail, outlining precisely how physicians vary from their peers in the way they use hospital resources to provide care, and select outcome measures. 10 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Chapter II Overview of the Hospital, Intervention, and Study Design Overview of Study Hospital The study organization is a 350-bed community hospital located in southern California. It is a full-service general acute care hospital with an emergency room. Although not affiliated with a medical school, it has a small family practice residency program. The organization discharges approximately 11,000 patients from the inpatient setting in any given year. The medical staff consists of approximately 750 physicians; of these, about 200 admit over 90% of the patients. The hospital has existed for over eighty years and is one of two in the city; there is fierce competition between the two organizations. There is a high rate of managed care penetration, with approximately seventy-percent of all patients in a managed care program of some sort. Further, the hospital and a large affiliated medical group (an independent-practice association) were early participants in the Medicare capitation/risk-sharing agreements. In this arrangement, both the hospital and the medical group benefit financially when costs are held below the payments received. Over time, this partnership has fostered a shared vision between the hospital and medical group to aggressively manage the cost of caring for nearly 13,000 capitated members. The organization enjoys a long and successful history with implementing CQI programs, and uses an interdisciplinary approach when implementing 11 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. improvement projects. The quality management department consists of a director who is a registered nurse, two quality assurance specialists (both registered nurses), and a master’s prepared decision support analyst. These individuals provide technical and facilitation support for all quality projects. The Quality Outcomes Committee, a medical staff committee with ex-officio members from the executive management team, identifies and prioritizes opportunities and sanctions the initiation of all projects. Davis, et al. (1995) found that the results of variance analyses, along with length of stay and charge data, when presented to demonstrate the degree to which resource utilization can be standardized, can positively impact the bottom line. Over the past ten years, reduction of unnecessary variation has slowly gained acceptance as a technique to reduce length of stay and hospital charges, while maintaining quality, and offers considerable advantages in the managed care environment. In response to declining revenues and increasing costs, hospital administration asked the Quality Management department to develop and implement a physician-profiling and clinical pathway program. The project was named, the “Best Practices Initiative.” Senior administrative and medical leadership believed that the program of profiles and pathways was compatible with the long standing acceptance of continuous quality improvement of the organization. The project was given status as a strategic priority and monthly status reports were provided at several medical staff department meetings where peer-review occurred (Internal Medicine, Family Practice, Surgery, Obstetric & Gynecology, and 12 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Pediatrics), as well as committees that dealt with broad functional issues (Quality Outcomes, Utilization Management, Pharmacy & Therapeutics, Medical Executive, and the Governing Board). The project co-directors (the director of the quality management department and the master’s prepared decision support analyst) reviewed the relevant literature to determine critical successful factors for implementing such a program. They selected the organization’s top-10 (in terms of volume) diagnosis groups for inclusion in the program. Overview of the Intervention Physician Profiling Medical practice profding has gained prominence in recent years as insurance companies, managed care organizations, and government agencies have used and promoted this method of analyzing resource utilization (Brand, et al., 1995). Physician profding focuses on patterns of care rather than specific clinical decisions; the data helps identify and characterize differences in practice style to which individual physicians or hospital staffs can respond. Profding is not based on rigid rules; it can accommodate legitimate exceptions in which the appropriateness of clinical decisions is judged separately (Welch, et al., 1994). Further, profding can play an important role in performance assessment, utilization review and quality improvement (Lasker, et al., 1992). 13 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. A typical profiling report examines both resource measures, such as length of stay and ancillary charges, as well as outcomes measures, like readmission, mortality, and surgical complication rates for patients treated for a specific illness during a fixed time frame, usually one year. The primary goal of profiling is to make physicians aware of how their practice impacts cost by comparing their performance to their colleagues. Kongstvedt (1996) states that the most important use of profiles is producing feedback to assist the physicians with understanding and modifying their practice style. He summarizes key functions of profiling: 1. Health plans can use profiles and other information to make decisions about including or excluding physicians from their network. 2. Medical groups and health plan can use the profiles to allocate bonuses or risk-pool incentive funds. 3. Profiles may be used to provide intangible rewards, like exempting physicians with favorable profiles from utilization review. 4. Profiles can be used to compare, or benchmark, physicians. 5. Profiling can be used to identify physicians with low-cost, high-quality outcomes, and disseminate these practices, in the form of guidelines or pathways, throughout the organization. Of these key uses, the hospital’s administrative and medical leadership committed to using profiles to compare and benchmark physicians and to identify 14 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. physicians with low-cost, high-quality outcomes. The goal was to study, develop and disseminate practice guidelines throughout the organization. The leadership group also agreed to not use resource utilization data in the credentialing and reappointment process; that is, physicians did not need to fear that they would be removed from the staff if their practice patterns were unfavorable when compared to their peers. Kongstvedt (1996) offers several suggestions for designing profdes, criteria he believes are critical in obtaining physician acceptance. These are summarized below: 1. Feedback must be consistent and understandable. 2. Providing regular and accurate data is vital to changing behavior. 3. Frequent and regular contact will help create an environment for positive change. 4. Data must encompass an adequate time period. 5. Reports should be no more than one or two pages in length. 6. Graphics should be used to convey large amounts of data. Further, Kassirer (1994) theorized that feedback is likely to be more successful when it is individualized, clinically specific, close in time to the behavior, targeted to the correct physician, and when there is an agreed practice norm. 15 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Benchmarking Benchmarking is the comparison of a particular work process metric or outcome internally or against other organizations such as the top competitor, functional leader, or even to an unrelated industry. The purpose is to identify best practices or a “gold standard,” with the goal of setting competitive performance measure levels to surpass. Kongstvedt (1996) states that profiles are of limited utility unless the results are compared with some type of standard. The most common way of comparing results is to provide data for the individual physician in comparison to one or more of the following: 1. Hospital average results - this is a simple average for all practitioners within the organization, and the least sophisticated. 2. Specialty or peer group - this compares the practitioner within their own specialty. 3. Peer-group, adjusted for severity - this is the most complicated approach, but as described earlier, the most meaningful, and the method most likely to be accepted by the physicians. For clinical functions, there are many potential, ready-made networks of people with similar problems and interests (Camp and Tweet 1994). Hospitals and physicians could compare themselves to similar institutions or providers, competitors, or the best in the industry on a severity-adjusted DRG-by-DRG basis. 16 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Clinical Pathways Clinical pathways are an extension of the critical path method; they are operational versions of guidelines that attempt to explicitly define and codify diagnostic and treatment processes. They are planning tools that specify the use and timing of procedures in relation to the patient's recovery; most have tables of treatments and medications, by day, displayed in a Gantt chart format. Once low-cost, high-quality practices are identified, organizations can develop clinical pathways based on these practices, disseminate the pathways to other providers (Bernstein 1998), and benchmark all providers against best practices. Many believe that adherence to such a pathway can reduce variation in clinical management and improve clinical outcomes while reducing the average length of stay and associated costs. All-Patient Refined Diagnosis Related Groups It is important that the data used in profiles be severity adjusted for medical and non-medical factors known to affect clinical performance, and that sufficient numbers of events be measured to ensure that differences are not due to chance alone (Physician Payment Review Commission, 1992; Orav, et al., 1996). Salem- Schatz, et al. (1994) caution that failure to adjust for case mix in physician practice profiles may lead to overestimates of variation and misidentification of outliers; if unadjusted practice profiles are used for decisions about education, sanctions, or employment, physicians may be subject to inequitable decisions and actions. 17 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Further, doctors who believe they are providing high-quality care are unlikely to accept evidence to the contrary, unless the severity of illness is considered. Fortunately, there are a number of case-mix adjustment techniques that permit the comparison of severity and outcomes by factoring co-morbidities, age, and pre existing conditions. The study site used the all-patient refined diagnosis related groups (APRDRG) system developed by the 3M Corporation. The methodology is similar to the DRG system used by the Medicare program, with some significant differences; most notably, the APRDRG system calculates a patient illness severity score. Thus, the APRDRG methodology provides a sophisticated tool for comparing risk-adjusted consumption of resources. It uses ICD-9 codes of primary and secondary diagnoses, the interaction of secondary diagnoses, co-morbidities, age, and non-operating room procedures to calculate a severity value. The method assigns patients to one of four discrete complexity of illness values: 1 (Minor), 2 (Moderate), 3 (Major), and 4 (Extreme). A high complexity of illness is primarily determined by the interaction of multiple diseases. This tool, used nationally, reduces noise due to patient factors and affords better between-physician comparisons. Profile Development in the Study Organization The project co-directors initially met with each elected medical department chairperson and medical executive committee member to explain the rationale for the program and to seek their advice on methods to implement the program. Next, 18 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. the project co-directors drafted a profile format. Charge and clinical data were extracted from the hospital’s medical record and patient billing systems into an Excel spreadsheet for manipulation and report creation. The profiles used simple averages (arithmetic mean) to compare the physician’s resource utilization and outcomes (observed) against case-mix and severity-adjusted averages of all other physicians (expected). Because outliers can obscure the relationships and create noise, the organization removed all length of stay outliers from the database used to generate the profiles. An outlier was defined as a discharge where the length of stay was greater than the average length of stay plus three standard deviations for the particular APRDRG; this is similar to the method used by the Medicare program. Extreme cases (complexity of illness of 4) were also excluded from the profile because a high complexity of illness is primarily determined by the interaction of multiple diseases. Nearly all of the length of stay outliers had a complexity of illness of 4. Numerous measures were considered for the profile by a small team of administrators and key physician leaders. The goal was to cover the broad spectrum of hospital services for which the attending physician was responsible. Several iterations were proposed to key executives, elected physician leaders, medical directors and other respected members of the medical staff. After demonstrating the utility (format) and accuracy (content) of the report, the profile was approved by the medical executive committee. The final product contained graphical depictions of ancillary resource utilization that were severity adjusted, as well as outcomes measures rates such as 19 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. infections, complications, readmissions, and death. The profile was disease or procedure-specific, and detailed physician-specific resource utilization and clinical outcomes. The specific measures that will be reviewed in this study are: 1. Average length of stay, and 2. Average total charge, and 3. Readmission rate, and 4. Surgical complications rate. Pathway Development in the Study Organization Each of the ten APRDRGs was treated as a separate performance improvement project, and also identified by hospital administration as a strategic initiative. For each APRDRG, a multi-disciplinary team was appointed to develop a clinical pathway. Teams were given the charge of balancing the benefits of standardization with the physicians’ prerogative to make decisions tailored to individual patient care needs. Each team had at least two physicians: there was at least one “best practice” physician and one physician leader (a department chair, medical director, or office of the medical staff executive committee). The project co-directors served as facilitators for each team. The teams began the process by reviewing literature relevant to pathway development, current scientific literature for the particular disease or procedure, and pathways developed at other hospitals and by recognized professional societies. The teams discovered that many existing pathways were complicated, often several pages 20 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. long, and detailing nearly every aspect of care. Physicians on the teams expressed concern that long, complicated tools may be ignored and suggested that more simplistic tools be considered. They also expressed that if the pathway was not “home grown,” acceptance by their colleagues was unlikely. The team developed pathways based on the practice of those physicians with low-cost, high-quality outcomes (best practice physicians). This required copious review of medical records, and was time intensive. The tool was based on the care provided to patients with a complexity of illness of 2 and 3 (approximately 85% of the patients). Those with a complexity of one were excluded because variability in treatment was much less pronounced than those with a severity of 2 or 3. Similarly, the cases with a complexity of 4 were excluded because the patients often present with unique combinations of co-morbidities that would prohibit the development of a clinical pathway. Here too, there were several iterations of the pathway. The final product was subject to review and approval by the appropriate medical staff committees. In the end, each team developed a relatively simple pathway that highlighted only the most critical aspects of care. It was a grid format, the columns were the day of treatment, and the rows the key aspect of care (such as a medication, respiratory treatment, or diagnostic test). An “X” was placed at the intersection, denoting the day that the key aspect should occur. The final product was unique; the pathway was not like any of those developed by other organizations. 21 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Intervention Dissemination Every physician who took care of any patient falling into one of the treatment APRDRGs received a two-page report (by mail) profiling their practice patterns. To facilitate physician use and understanding of the report, a one-page “user’s guide” was also included. Accompanying each profile was the clinical pathway based on the collaborative efforts of the multidisciplinary team for each particular APRDRG. Because both the profile and pathway were specific to the APRDRG, it was possible that a physician could receive several reports. To ensure that the medical staff understood the program, a cover letter signed by the chief executive officer and chief medical officer, explained the purpose of the program. Specifically, the letter commented, “If we are to continue serving our community, we must use our resources as effectively and efficiently as we can. If we do this, the hospital will reduce the cost of care, while improving quality. We believe that development and implementation of such things as best practice guidelines, clinical pathways, and suggestions you may see when comparing your information to your peers, play an important role in dealing with these challenges.” The initial report covered a 12-month period; it was mailed in January 1996, and covered calendar year 1995 (the pre-intervention period). The study co directors believed if regular dissemination of the reports communicated a relative advantage (reduced costs and improved outcomes), acceptance would increase over time. Thus, another set of reports were distributed six months later, (sent out in July 22 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 1996, covering January-June 1996); again in October (covering January-September 1996), and again in January 1997 (covering January-December). This provided physicians with the ability to regularly monitor their practice patterns, and reinforced the intervention. It is important to note that the cover letter that accompanying the July mailing reported that the program resulted in a cost avoidance of one million dollars. A sample of the intervention packet can be found in the appendix. Overview of the Study Design Selection of Diagnostic Groups Consistent with the basic principles of quality management and statistical process control, the project directors wanted to be able to demonstrate whether the program was an effective method to change physician practice patterns. If the program was successful, it would be expanded to cover a larger number of diagnoses. Thus experimental and control groups were established. In selecting the APRDRGs for the control and experimental groups, the following conditions were established: a) There were at least 50 cases in the baseline year (1995), b) The mix represented a variety of specialties; i.e., cardiology, surgery, pediatrics, c) There were ten APRDRGs assigned to each group, and 23 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. d) To minimize halo or spillover effects, APRDRGs selected for the control group were clinically different from the experimental group based on a higher level grouping, known as Major Diagnostic Category (MDC). Experimental Group Ten APRDRGs are included in the experimental group. To ensure consistent comparison, only those physicians who provided care in both 1995 and 1996 were included in the study. This exclusion eliminated only 43 cases. In 1995, there were 256 physicians in the experimental group caring for 3,944 patients. In 1996, these same 256 physicians provided care to 3,178 patients. The APRDRGs selected for study and the corresponding MDC are listed in table 2-1. Table 2-1. Experimental APRDRG Group APRDRG Description Major Diagnostic Category 14 Cerebrovascular Disorder, Excluding TIA Nervous System 88 Chronic Obstructive Pulmonary Disease Respiratory System 89 Simple Pneumonia & Pleurisy Respiratory System 96 Bronchitis & Asthma Respiratory System 127 Heart Failure & Shock Circulatory System 209 Major Joint & Limb Procedure Musculoskeletal System & Connective Tissue 358 Uterine & Adnexa Procedures Female Reproductive System 370 Cesarean Delivery Pregnancy, Childbirth & Puerperium 372 Vaginal Delivery Pregnancy, Childbirth & Puerperium 757 Back & Neck Procedures Musculoskeletal System & Connective Tissue 24 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Control Group An equal number of APRDRGs with similar costs and lengths of stay define the control group. The project directors were concerned with two competing issues: halo effect and volume. To ensure sufficient volume, the APRDRGs selected for the control group, were those ranking in volume just below the experimental group. However, to minimize possible spillover, the control group APRDRGs were selected from different MDCs. Three high volume APRDRGS were removed, and replaced with the next three from the list. Thus, the experimental group represented the organization’s highest volume APRDRGs; and the control group were the next highest in volume that were different in clinical nature. The control group APRDRGs and the corresponding MDC are listed in table 2-2. Table 2-2. Control APRDRG Group APRDRG Description Major Diagnostic Category 63 Ear, Nose, Mouth & Throat Procedures Ear, Nose, Mouth & Throat 174 GI Hemorrhage & Perforation Digestive System 182 Gastroenteritis & Abdominal Pain Digestive System 188 Digestive System Diagnoses Digestive System 277 Cellulitis Skin, Subcutaneous Tissue & Breast 296 Nutritional & Metabolic Disorders Endocrine, Nutritional & Metabolic 320 Kidney & Urinary Tract Infections Kidney & Urinary Tract 323 Urinary Stones Kidney & Urinary Tract 397 Coagulation Disorders Blood, Blood Forming Organs, Immunology 787 Laparoscopic Cholecystectomy Hepatobiliary System & Pancreas In 1995, there were 246 physicians in the control group caring for 1,377 patients. In 1996, there were 213 physicians in the control group caring for 1,018 25 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. patients. Between the two years, there were 248 different physicians. A limitation of the study is the inability to fully control for possible halo or spillover effects since 148 of the 246 physicians received reports on APRDRGs in the experimental group, although they did not receive a report on those APRDRGs in the control group. The size of these groups is summarized in table 2-3. Table 2-3. Size of Control and Experimental Groups 1995 1996 Control Group Physicians 246 213 Patients 1,377 1,018 Experimental Group Physicians 256 256 Patients 3,944 3,178 Comparability between Groups An apparent weakness of the selection of the DRGs for each group is volume; there are nearly three times as many patients in the experimental group versus the control group. This difference could not be corrected, the experimental group represented the organization’s highest volume DRGs and the control group was the next highest in volume. Given the significant difference in volume, a power analysis was conducted to ensure that the number of discharges in each group was large enough to detect significant difference at an alpha level of 0.05. The power was determined to be 0.87; generally a power greater than 0.80 is considered to be good, and the concern for sufficient numbers in each group was satisfied. Further, the limitation is offset, given that there are nearly an equal number of physicians in each group. Lastly, the baseline (1995) resource utilization statistics are 26 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. comparable, and not statistically significant: the average length of stay for patients in the experimental group was 3.40 days versus 3.38 for the control group; average total charges were $10,911 (experimental) and $11,937 (control). Study Population The population for the study included those attending physicians who provided care to patients within the targeted APRDRGs during the study period, January 1, 1996 through December 31, 1996. The prior calendar year served as the baseline. All attending physicians practicing in the hospital had the potential to be included in the study, either as part of the experimental group or the control group. Hospital-based physicians (pathologists, radiologists, emergency medicine physicians, and anesthesiologists) are excluded from the study since they do not typically prescribe patient care (i.e., write physician orders). Summary The purpose of this chapter was to provide an overview of the organization to better understand the context of the intervention. Key elements of the intervention were summarized to clarify elements of the study design. Other elements of the study design, including methodology and data analysis will be presented later in this study once a theoretical framework is established. 27 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Chapter III Literature Review and Hypothesis Development Introduction Reducing inappropriate variation in resource utilization is a recognized strategy to control costs and improve quality. Information sharing and information feedback are common and relatively inexpensive interventions for changing resource utilization (Avorn et al., 1992, Balas et al., 1996). One approach is to share peer-comparison profiles with physicians, assist them with interpretation, provide benchmarks, and disseminate clinical guidelines or pathways. However, the results of before-and-after studies on profiling vary, despite the frequent use of this information feedback technique (Balas et al., 1996). Provider uncertainty and differing opinions about the value or efficacy of procedures have been stated as the primary cause for variation in utilization. Rogers (1995) and others detail several enabling factors that when aligned with the goal of encouraging innovation diffusion, can significantly increase the chance for successful evaluation, adoption, diffusion, and sustainability. The successful adoption of innovations often depends on the network of interpersonal relationships within a system or an organization. The ability within an organization to share ideas, observe trials of new ideas, and be influenced by the behavior and beliefs of trusted individuals all influence successful adoption and diffusion. 28 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. This chapter reviews the literature to assess the effectiveness of clinical pathway dissemination, benchmarking, and physician profiling as feedback and information tools to change behavior. An exhaustive review of health administration and medical journals was conducted; major topics included: physician profiling, utilization review, quality management, clinical pathways, and benchmarking. This chapter also reviews the institutional school of organization theory and diffusion of innovation theories to provide a robust theoretical perspective. The literature and research reviewed in this chapter were selected based on theoretical framework, content, and methodology. Changing Physician Behavior The first tenet on which the practice of medicine is built is the sanctity of the relationship between the patient and the physician, and the physician’s ethical duty and professional commitment to act in the patient’s best interests. They are motivated by their formal education, clinical experiences, personal beliefs and values, economic incentives and influences in their working environment. Medical practice is characterized by a high degree of uncertainty; cause and effect relationships are not always clear. This uncertainty arises because the physician cannot be sure that he or she knows everything about the patient that is relevant to their diagnosis and treatment. The physician’s preference for diagnostic certainty may incline them to use more, not fewer tests. Additionally, when faced with a patient with a particular diagnosis, the physician often has several options to 29 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. choose from. Physicians must make implicit judgments based on their knowledge, training, and past experience. These judgments vary widely and are the primary source of practice variation. Social Learning Theory Social learning theory (Bandura 1969,1971) emphasizes the importance of observing and modeling the behaviors, attitudes, and reactions of others. Much behavior is learned observationally through modeling: from observing others one forms an idea of how new behaviors are performed, and on later occasions this coded information serves as a guide for action. Several special features of a physician's background make changing their behavior a complex process. A physician's background, ethics, and beliefs strongly shape their opinions and influence their practice behaviors. Individual physicians clearly differ in their clinical practice styles as a function of their individual nature, medical training, and clinical experience. Increased understanding of the etiology of the disease, cause-and-effect relationships, and new technologies all serve to render a physician’s initial training obsolete. Over the course of their careers, most physicians will modify their practice styles many times. Social learning theory can help us understand these modifications. The theory encompasses attention, memory and motivation and provides a framework to explain human behavior in terms of continuous reciprocal interaction between cognitive, behavioral, an environmental influences. The component processes underlying 30 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. observational learning include attention, retention, motor reproduction, and motivation. Attention includes modeled events, such as distinctiveness, affective valence, complexity, prevalence, functional value; and observer characteristics, like sensory capacities, arousal level, perceptual set, and past reinforcement. Retention refers to symbolic coding, cognitive organization, symbolic rehearsal, and motor rehearsal). Motor reproduction includes physical capabilities, self-observation of reproduction, and accuracy of feedback. Motivation refers to external and self-reinforcement. Physician behavior is constantly changed during medical school and residency training through formal and informal exposure to guidelines by program leaders and department chiefs who serve as thought leaders. Residents may cite or hear cited position statements and/or guidelines by physician societies to entrench practice behavior norms. Also during residency training, mentors, supervisors, and peers seek to mold behavior. Repetitive assessment of values, attitudes, and skills is a part of this initial training (Cassel et al., 1997; Holmboe and Hawkins, 1998). After medical school, there are numerous educational opportunities vying for the practicing physician’s attention. Physicians' regularly receive advertisements for continuing medical education courses, often combined with vacation features. In addition, written, audio, or video education courses to complete at home, by mail, or on the Internet are offered, in hopes of capturing physicians' limited time for interventions to improve their performance. Researchers have found evidence that physicians contemplating or adopting behavior change attend conferences to validate and test the reliability of their 31 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. learning and behavior, either that of new information and innovations, or that of what they are already doing in practice (Putnam and Campbell 1989). Passive education strategies embodied in continuing medical education conferences have been found to be ineffective (Davis, et al., 1995); research also suggests that printed materials are ineffective (Freemantle, et al., 1999). There are three principles from social learning theory that are relevant to this study: 1. The highest level of observational learning is achieved by organizing and rehearsing the modeled behavior symbolically and then enacting it overtly. Coding modeled behavior into words, labels or images results in better retention than simple observation. 2. Individuals are more likely to adopt a modeled behavior if it results in outcomes they value. 3. Individuals are more likely to adopt a modeled behavior if the model is similar to the observer and has admired status and the behavior has functional value. Further, Rogers (1995) suggests that most individuals evaluate an innovation, not on the basis of scientific research by experts, but through the subjective evaluations of peers, and especially opinion leaders, who have adopted the innovation. Most professionals today complain of an overload of information, and physicians are not immune from this overload. With a glut of new ideas flowing 32 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. across their desks, it may be useful to understand how physicians select new ideas to experiment with. Rogers’ extensive work on innovation diffusion can provide a framework. Introduction to Innovation Diffusion Diffusionism refers to the point of view in anthropology that explains social change in a given society as a result of the introduction of innovations from another society (Rogers, 1995). Since the 1960s, the diffusion model has been applied in a wide variety of disciplines such as education, public health, communication, marketing, geography, general sociology, and economics. Diffusion of Innovation theory provides a framework for understanding the process of social communication, adaptation, and change within a given social system. The innovation-decision process refers to the cognitive process in which an individual or group passes from initial awareness knowledge of the innovation, to forming an interest in the innovation, to a decision to adopt or reject, to implementation or experimentation of the new idea, and finally to confirmation or adoption of the innovation into lifestyle. An individual seeks information at various stages in order to decrease uncertainty about an innovation's expected consequences. The diffusion model suggests that the most important single indicator of effectiveness is the rate of adoption of an innovation. Rogers proposes five attributes that determine the rate of adoption: relative advantage, compatibility, 33 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. complexity, trialability, and observability. There are four constructs in his framework: characteristics of the innovation, communication, time, and social system. Several of these attributes and concepts are discussed in more detail in the remainder of this chapter. Feedback This section examines literature and research that evaluates the effectiveness of using feedback techniques (clinical audits and physician profiling), and information sharing (clinical guidelines and pathways) to change physician use of hospital resources. Clinical Audits Audit and feedback, which stem from behavioral and learning theories, are approaches that seek to modify physician performance through external stimuli. Behavioral and affective theories (Andersen, 1974, 1995) including social cognitive theory (Bandura 1969, 1971) and the health belief model (Rosenstock, 1974; Maiman and Becker, 1974) suggest that an individual's behavior change is governed by his or her goals and perceptions, which are in turn affected by internal and external forces that may be malleable. These theories hypothesize that feedback of performance or behavior norms, or compliance reminders can change physician behavior. Balas et al (1996) concluded that peer feedback has a statistically significant although small effect on utilization. Cochrane (1999) found that audit 34 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. and feedback can sometimes be effective, in particular with prescribing medications and ordering diagnostic tests; however, effects appear to be small to moderate. He cautions against relying solely on this approach, and argues that complementary interventions can enhance effectiveness. Historically, hospitals have used clinical audits as both quality assurance and utilization review tools to characterize care through the systematic review of a series of patient experiences. Information is usually obtained by reviewing medical records for documentation of specific clinical practices. Such audits examine issues of quality surrounding clinical management of minor acute problems or preventive health practices, chronic disease management and the use of specialty consultations. While clinical audits have been widely used to assess performance, the evidence on their efficacy in modifying physician behavior is conflicting. To date, there has been no formal synthesis of studies on the use of audits to affect clinical performance. Many of the studies were not well controlled and most did not include a strategy for randomizing the physicians who were given feedback. Rather, most were pre and post evaluation designs, based on interventions conducted at a single site or with a small number of practices. A study at one hospital demonstrated significant improvement in the utilization of preventive health processes when those processes were audited, and no improvement in those processes that were not monitored (Holmboe et al, 1998). Two delimited studies, examining the quality of pap smears, demonstrated that performance of both residents and faculty physicians substantively improved after 35 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. they received feedback from clinical audits (Curtis, et al., 1993; Norton, et al., 1997). The Ambulatory Care Medical Audit Demonstration Project (Palmer and Hargraves 1996) is the largest formal study of the use of audit information in the United States. The project was designed as a randomized controlled clinical trial of the use of quality-improvement techniques to improve clinical performance in primary care. Although audit information was only one component in this multidimensional intervention, the study demonstrated that it is possible to improve quality through audit information feedback. Other reviews suggested that auditing as feedback has only a small effect on overall resource utilization (Balas, et al., 1996) but a significant effect on prescribing drugs and ordering diagnostic tests (Thomson, et al., 1999). This may be explained by the fact that drugs and tests frequently change, thus doctors are predisposed to scanning for ideas. While researchers suggested combining feedback with other approaches, they have not found evidence pointing to the superiority of any complementary interventions (Thomson, et al., 1999). Additional study is required on the effects of modifying important characteristics, such as the content, source, timing, recipient, and format of feedback. Physician Profiling Over time, profiles can be utilized to communicate to physicians the relative advantages of clinical pathway adoption. Drawing from Rogers (1995), physician 36 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. profiling is more likely to be accepted if it provides a relative advantage (i.e., improve quality); consistent with existing values (e.g., not used for economic credentialing); easy to understand; and produces observable results. Further, given the volume of managed care patients and the Medicare risk-sharing agreement, reduction of costs is a clear relative advantage. Studies of physician profiling as a tool for changing physician behavior present mixed results. A meta-analysis of randomized trials of profiling found only 12 eligible trials; many of the studies under evaluation had notable design flaws (Balas, et al., 1996). The analysis found that profiling had a statistically significant positive effect on utilization. The randomized, controlled trial literature suggests that profiling can produce a modest, but statistically significant effect on changing physician behavior (Kim, et al., 1999). Concerns for small sample sizes, absence of risk adjustment, and reliability of data collection methods along with other methodological concerns (Balas, et al., 1996) have resulted in mixed opinions regarding physician profiling as a tool for improving quality of care and for the mixed results seen in previous studies. In light of pressures for healthcare reform and skepticism regarding physicians' decision making, it is unlikely that methodological concerns will dissuade regulators and managers from expanding scrutiny of physician practice (Massanari 1994). A study by Hofer et al. (1999), examined the usefulness of physician profiling for patients with diabetes, one of the most prevalent conditions in clinical practice. The authors conducted a study of approximately 3,600 patients with type 37 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. II diabetes, under the care of 232 different physicians. They were unable to reliably detect any true differences in care among the physicians, as measured by office visit and hospitalization rates. These utilization rates are rather coarse proxies for measuring care processes; unfortunately, the article did not describe the assessment tool in sufficient detail to determine if more sophisticated measures were collected. The authors highlighted the power problem with their study: a physician would need to have over 100 diabetic patients for the statistical analysis to achieve an 80% reliability rate; however, over 90% of primary care physicians in the study had less than 60 patients with diabetes (Hofer, et al., 1999). An experimental-control group study was conducted in a large community hospital to determine the effect of a physician education program on hospital length of stay and total patient charges (Johnson et al., 1993). The intervention consisted of a one-time exposure of physicians to clinical and financial information about their individual practice patterns for the treatment of pneumonia patients. The study concluded that providing physicians with specific information about their practice behavior resulted in decreased charges and length of stay. The improvement in resource utilization was observed for two years following the provision of practice specific data to physicians in the experimental group. Johnson and Martin (1996) concluded that physician profiles are effective in reducing hospital resource consumption in elective total hip replacement. Over a seven-week period, orthopedic surgeons in the intervention group were given graphical charts profiling their specific length of stay and average total charges and 38 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. that of their peers. Length of stay declined from 13.7 days to 9.9 days; charges were reduced from $22,103 to $18,607; and the variance for each dropped by one- half or more. Benchmarking In 1984, Winickoff, et al., published their study investigating physician compliance with colorectal cancer screening standards. The standard required a digital examination and occult blood stool test at annual check-ups for patients aged 40 and older. Three intervention strategies to improve compliance were implemented during a three and one-half year period: educational meeting, retrospective feedback of group compliance rate, and retrospective feedback of individual compliance rate, and retrospective feedback of individual compliance rates compared to peers. During the first six-month period, physicians receiving feedback improved from 66.0% to 79.9%. Behavior changes were found to continue up to twelve months after the intervention. A study on information-sharing as a tool for modifying physician practice was conducted by Marton, Tul and Sox (1985) which compared two interventions by assigning 56 physicians into four groups: a control group; a feedback group, which received information about their use of laboratory tests; a manual group, which received an educational manual addressing cost-effective laboratory utilization; and a manual-plus-feedback group, which received both interventions. After the introduction of the interventions, physician test use was monitored for 39 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. seven and one-half months. The study compared mean laboratory charges and mean number of tests ordered per patient visit per physician for each of the study groups both before and after the intervention. The study concluded that these simple techniques could modify physician use of the laboratory, but did not suggest that one intervention was superior to another. Berwick and Coltin (1986) conducted a study of physician feedback in a health maintenance organization. In a crossover design controlled clinical trial, three interventions were studied on the use of thirteen common blood tests among thirty-five internists within three ambulatory care centers. Overall use fell by 14.2% in a 16-week period during which physicians received confidential feedback on their individual rates of use compared with peers (cost feedback). Eleven of 12 tests showed some decrease. Similar feedback on rates of abnormal test results (yield feedback) and a program of test-specific education failed to show a consistent effect. Variability in rates of test use among physicians, as measured by the coefficient of variation, fell by 8.3% with cost feedback, by 1.3% with yield feedback, and by 2.3% with education, but these changes were inconsistent across tests. This may suggest that either the tests or the diagnosis have characteristics that make the determination of appropriate use more difficult. In 1989, Pugh, et al., published their study of a controlled trial to determine the effect of daily feedback about inpatient charges on physician knowledge and behavior. The study examined two medical wards in an academic medical center to determine the effect of providing daily charge feedback information on charges. 40 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. There was a significant reduction in mean total charges (17%), length of stay (18%), room charges (18%) and diagnostic testing (20%) in the sub group. The authors concluded that charge feedback alone is effective in decreasing resource utilization in a teaching hospital. Tierney, Miller and McDonald (1990) studied the effect of informing physicians of the charges for outpatient diagnostics in a primary medical care practice. All physicians in the study ordered tests from computer workstations. For the intervention group (half of the physicians), charges for the test being ordered and total charges for tests for that patient were displayed on the computer. The control group did not receive messages about charges. The authors found that the intervention was effective in significantly reducing the number and cost of tests ordered; however, they noted that the effects did not persist after the intervention was discontinued. Frazier, et al. (1991) conducted a prospective controlled trial in an internal medicine teaching clinic to determine whether an educational program using a drug cost manual could assist physicians in reducing their patients’ out-of-pocket expenses for prescription medications. Thirty-one interns received a manual of drug prices annotated with prescribing advice, two feedback reports, and a weekly prescribing reminder. The control group of twenty interns concurrently participated in a manual-based cholesterol management education program. In addition, feedback reports were generated from the carbon copies of the prescription written by physicians, and were only distributed to the intervention group. Each report 41 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. contained the physician’s own data with averages for all physicians in the intervention group for comparison. It was found that the intervention group physicians prescribed less expensive drugs within certain drug groups. Berkey (1994) examined a collaborative benchmarking approach developed by SunHealth Alliance, in which more than 120 hospitals participated in 15 projects. One clinical project, involving four hospitals, was focused on reducing the length of stay and mortality rates for pneumonia patients. Each hospital formed internal task forces, who reviewed comparative data, analyzed their hospitals' care processes, determined opportunities for improvement, and chose best practices for developing a clinical pathway. Similarly, such sharing of data among hospitals hastened the evolution of continuous improvement at Voluntary Hospitals of American/Pennsylvania to a focus on learning from the best. Banaszak (1993) examined two DRGs, appendectomies and cesarean section deliveries, and found that comparative outcome data showed significant variation. A study of the benchmarked hospitals showed characteristics, specific to those institutions, which resulted in reduced resource consumption and positive clinical outcomes. This quantification of best practices was a catalyst for the organization to implement a clinical benchmarking project, with the goal to standardize routine care, reduce variation, and improve financial performance. 42 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Communication Concepts from social learning, innovation, social influence, and power theories suggest that participatory guidance, where physicians are given the opportunity to develop norms and strategies and for change will lead to change. Rogers (1995) defines communication as the process by which participants create and share information with one another in order to reach a mutual understanding. A communication channel is the means by which messages get from one individual to another. Thus, a clinical pathway can also be viewed as a communication vehicle or channel. Rogers (1995) espouses that mass media channels are more effective in creating knowledge of innovations, whereas interpersonal channels are more effective in forming and changing attitudes toward a new idea, and thus in influencing the decision to adopt or reject a new idea. It seems reasonable to suggest that the organization’s dissemination of profiles and pathways can be construed as a mass media channel. Clinical Pathways as Communication Pathways are intended to change behavior by providing definitive information on best practices from authoritative sources to well-trained, interested, logical practitioners. Drawing from Rogers (1995), a clinical pathway is more likely to be accepted if it provides a relative advantage, is consistent with existing values, is easy to understand, and produces clear results. Relative advantages of 43 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. clinical pathways might include improved efficiency, such as decreased length of stay, and improved effectiveness, such as better clinical outcomes. Weingarten, et al. (1994) evaluated the effects of providing physicians a practice guideline that recommends consideration of early hospital discharge for low-risk patients with chest pain. During six intervention periods, physicians received a structured message posted on patients' charts the day after admission that conveyed risk information and the guideline recommendation. Use of the practice guideline recommendation with concurrent reminders was associated with a 50% to 69% increase in guideline compliance and a decrease in length of stay from 3.54 to 2.63. The intervention was associated with a total (direct and indirect) cost reduction of $1,397 per patient. At another institution, uncertainty regarding the optimal evaluation of suspected deep vein thrombosis resulted in wide variations in practice (Pearson et al, 1995). To address variation in practice while maximizing the efficiency and quality of care, the institution developed a critical pathway guideline for the emergency department evaluation of patients suspected of having the condition. A multidisciplinary team reviewed current practice, benchmarked it against other institutions, and developed the pathway. In its final form, the pathway balanced the benefits of standardization with the prerogatives of physicians to make decisions tailored to individual patients. Computerized clinical outcomes measurement systems are often routinely available to help physicians and administrators assess resource utilization as well as 44 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. improve the quality of care. At another institution (Krivenko and Chodroff, 1994), a physician subcommittee focused on the best outcomes rather than the poorest to determine the variations in processes of care that might have led to either superior or inferior clinical outcomes. They learned that each hospital must develop its own approach to common clinical conditions. These approaches become standardized in the form of institutional attitudes, beliefs, policies, and procedures - physician involvement at all stages was critical (Krivenko and Chodroff, 1994). In reviewing the literature, several cardiac surgery success stories were found. Andersson (1993) found success with coronary artery bypass graft (CABG) patients at Scripps Memorial Hospital. They developed clinical pathways for four DRGs in cardiovascular surgery in order to stabilize those clinical processes, collect data on them, and make improvements. The result was a 20 to 30 percent decrease in length of stay and a similar reduction in charges. Barnes, et al. (1994) reviewed the clinical processes and outcomes at Borgess Medical Center, where they analyzed and streamlined the processes of caring for a CABG patient. The team used comparative data, specialty and peer review organization guidelines, medical records, charge data, and relevant literature to drive the process. One year after the pathways were implemented, average total charges per patient decreased from $35,700 to $32,700; length of stay decreased from 11.1 to 9.7 days. At the Medical Center Hospital of Vermont, the combination of pathways and algorithms for CABG patients resulted in a reduction of 2.5 days for total length of stay (including 1 day in intensive care), for a mean cost savings of $3,500. None of these studies included the pathway in the 45 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. publication, so it is not possible to determine the similarities or differences or to suggest any relationship between the design and the effectiveness. Bernard, et al. (1995) examined the use of a feedback system to direct and monitor physician and hospital practice on general medicine services of an 880-bed university hospital. For the over 2,000 admissions on both a control service and the intervention service, the mean length of stay decreased when compared with historic norms. There also was a trend for the intervention service to have fewer LOS outliers than expected. Ancillary service use decreased by 17% on both control and intervention services. Other internal medicine services experienced a 29% increase in ancillary service use. A major weakness of the study was that it did not incorporate severity measures into the analysis. Overall, the study suggests that both direct and indirect interventions can produce temporary change. Kramolowsky, et al. (1995) determined that physician awareness of hospital costs for radical retropubic prostatectomy impacted physician practice. They reviewed 256 consecutive prostatectomies performed by fourteen urologists during a four-year period at a community hospital. Following two years of data collection, the physicians were provided cost information and factors that may decrease charges. Significant decreases were noted for charges, length of stay, need for intensive care, and operating time. Faced with the closing of its service, the Orthopaedics Department at Mt. Sinai Medical Center (New York), developed clinical pathways to ensure appropriate utilization. The service realized a 40% savings in materials, and reduced length of stay 46 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. by five to six days (Ferdinand 1994). Bristol Regional Medical Center, facing the challenge of managed care organizations, instituted this process and achieved significant cost savings, largely because of the working partnership between the administration and its medical staff. In simple pneumonia, major benchmark or "best practice" variations were incorporated into new clinical pathways, leading to decreased resource use (Clare et al., 1995). Bero (1998) found disparate results for any single tool and concluded that the use of multiple tools may be more effective. However, the literature search did not find any studies that evaluated the combined use of physician profdes and pathway dissemination. Profiles and Pathways as Innovation Rogers (1995) defines an innovation is an idea, practice, or object that is perceived as new. The term innovation does not necessarily refer to the creation of new ideas or products but to the introduction of previously unknown ways of providing care and services that may be an improvement over existing methods. In the study organization, neither physician profiles nor clinical pathways were previously employed, and thus, it can be reasonably argued, they represent an innovation. Based on learning theory and innovation diffusion theory, it seems reasonable to expect physicians exposed to profiling or pathways to behave differently (i.e., modify their practice patterns) than those who have not been 47 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. exposed. However, efforts to implement guidelines to change individual physician behavior have frequently failed. Research suggests that simple provision of information, even in the form of guidelines, is insufficient. A meta-analysis performed by Bero (1998) suggests that passive dissemination of information and small-dose education are generally ineffective; but that guideline dissemination is effective. Grimshaw and Russell (1993) concluded that explicit guidelines improve clinical practice when introduced in the context of rigorous evaluations; however, the magnitude of the effect varies considerably. In general, physicians do not like to be told how to practice medicine. The likelihood adopting pathways can be influenced by several factors: the scientific rigor of the guidelines used to develop the pathways, characteristics of the health-care professional (e.g., specialty and number of years in practice), characteristics of the practice setting (e.g., association with academic medical center or urban vs. rural location), incentives, regulation, and patient factors (Taylor- Vaisey, 1997). There is little existing research that examines the cultural dimension. According to Donabedian (1980), the cultural dimension refers to the underlying beliefs, values, norms, and behaviors of the organization that support continuous quality improvement (CQI) efforts. Rogers (1995) believes another cultural factor that influences acceptance is compatibility, or the degree to which the innovation is consistent with existing values and past experiences of adopters. He cautions that an idea that is incompatible with existing values and beliefs may not be adopted as 48 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. rapidly as one that is compatible. The diffusion process can be delayed. The adoption of an incompatible innovation often requires the adoption of a new value system before accepting the innovation. As suggested by learning theory, clinical pathways can be seen as representing a coded version of modeled behavior. An extension of diffusion theory suggests that for pathways to be accepted then, they must be consistent with existing values and balance the benefits of standardization with the prerogatives of physicians to make decisions tailored to individual patient care needs. In the study organization, the pathways were developed by physicians practicing within the hospital and not imported from some other organization, so it seems reasonable to expect that they are consistent with existing values and more likely to be accepted. Acceptance implies that there are more efficient and effective practices to treat patients within specified illness or diagnostic groups. This research expects that there may be varying levels of acceptance occurring among the participants within their voluntary attitudes and behaviors. Kerr and Hiltz (1982) and Hiltz and Johnson (1989) found that usage is a measure of acceptance, but usage alone is not a sufficient indicator of success. Operationally, this study defines acceptance as an observable decline in resource utilization. Therefore, Hypothesis la. Resource utilization patterns will decline when physicians are provided profiles and clinical pathways. 49 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Hypothesis lb. Resource utilization patterns will not decline when physicians are not provided profiles and clinical pathways. Relative Advantage Most healthcare organizations have been using critical pathways for some time in an attempt to standardize practice and improve clinical outcomes (Coffey, et al., 1995). Proponents of guidelines and pathways argue that the use of these tools contributes to enhanced outcomes. This section reports on the clinical outcomes for the literature and research evaluating the effectiveness of using feedback techniques (clinical audits and physician profiling), and information sharing (clinical guidelines and pathways) that was examined in the previous section. As with resource utilization, studies examining the change in clinical outcomes have illustrated mixed results. Profiling and C linical O utcom es Some studies reported a favorable change in outcomes. Bernard, et al. (1995) examination of the use of a feedback system to direct and monitor physician and hospital practice on general medicine services found that the intervention service experienced significantly fewer preventable deaths (21% versus 3%, p=0.04). A major weakness of the study was that it did not incorporate severity measures into the analysis. Kramolowsky, et al. (1995) study of physician awareness of hospital costs 50 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. for radical retropubic prostatectomy demonstrated a significant decrease in the complication rate. In a few studies there was no change in outcomes. Balas, et al. (1996) meta analysis of 12 eligible randomized trials of profiling found there was no significant improvement in clinical outcomes. The Pugh, et al. (1989) study of a controlled trial to determine the effect of daily feedback about inpatient charges on physician knowledge and behavior found no change in neither mortality nor readmission rates within 30 days. This is not surprising, considering that the focus of the profile was financial, not clinical. The study on the treatment of pneumonia patients in which physicians were given clinical and financial information about their individual practice patterns (Johnson et al., 1993) reported “no compromise” in outcomes as measured by mortality, readmission rates, and infections or other complications. Bernard, et al. (1995) also reported no differences in readmission, mortality rates, and patient satisfaction. Pathways and Clinical Outcomes In Barnes, et al. (1994) review of implementing CABG pathways found no change in outcomes; the mortality rate held constant at 2.7%. Conversely, at the Medical Center Hospital of Vermont, the combination of pathways and algorithms for CABG patients readmission and mortality rates decreased (Schriefer 1994). The chest pain guideline study (Weingarten, et al. 1994) reported no significant difference in the complication rate in the post-intervention period. Similarly, the pneumonia 51 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. guideline study at Bristol Regional Medical Center reported no change in the quality of care, as measured by readmission rates (Clare et al., 1995). Again, none of these studies included the pathway in the publication, so it is not possible to determine the similarities or differences or to suggest any relationship between the design and the effectiveness. Relative Advantage Rogers (1995) defines relative advantage refers to the degree to which an innovation is perceived as better than the idea it supersedes. This advantage may be measured in economic terms, social prestige, convenience, and satisfaction. This principle of diffusion theory suggests that individuals are more likely to adopt a modeled behavior if it results in outcomes they value. Thus, it seems reasonable to assume that physicians will accept and implement clinical pathways that can improve patient outcomes; therefore, Hypothesis 2. There will be an improvement in clinical outcomes when physicians are provided profiles (that include clinical outcomes measures) and clinical pathways. Time and the Rate of Adoption The time dimension is involved in diffusion in three ways: the innovation- decision process, innovativeness of the adopters, and the rate of adoption. Rogers 52 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (1995) defines the rate of adoption as the relative speed with which the innovation is adopted by the social system. The new idea or innovation typically moves slowly through the social system when it is first introduced. Then, as the number of adopters increases, the diffusion of the new idea moves at a faster rate. The rate of adoption is usually measured as the number of members of the system that adopt the innovation in a given time period. Some innovations spread faster than others. The explanation for this phenomenon lies in the complex interaction of characteristics of the idea itself and the presence of various enabling factors in the environment. Identifying innovations for testing through examination of the characteristics of the innovation itself, coupled with the support and presence of various enablers, would create greater opportunity for successful deployment and diffusion. Adoption is often the result of increasing network pressures from peers, and intervention strategies that help potential adopters overcome barriers, therefore it seems reasonable to expect physician acceptance and use of the tools to increase over time. Rogers (1995) defines observability as the visibility of the results; the easier it is for individuals to see the results, the more likely they are to adopt it. Visibility stimulates peer discussion of a new idea; i.e., friends of an adopter often request information about it. Over time, profiles that benchmark performance can be utilized to communicate relative advantages of clinical pathway adoption to physicians. Further, since the innovation was disseminated several times in one year, and the Best Practices program was a regular agenda item for several medical 53 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. staff department meetings, it seems likely that observability was favorably enhanced overtime. Thus, it seems reasonable to suggest that, over time, a physician reluctant to adopt the innovation may become more accepting if he sees that the data for his peers has produced favorable results (e.g. a decline in length of stay and an improvement in clinical outcomes); therefore, Hypothesis 3. Resource utilization patterns will decline over time for physicians who are provided profiles and clinical pathways. Physician Leaders This section examines key ideas from social identity and organizational citizenship behavior theories to examine the performance of physician leaders in the study. Social Identity Social identity theory (Tajfel and Turner, 1979) involves three central ideas relevant to this study: 1. Categorization: The assignment of objects to better understand the social environment. It facilitates permits the definition of appropriate behavior by reference to the norms of group. 54 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 2. Social identification: An individual’s belief that that he belongs to a defined group. Group membership is not abstract to the individual; it is a real, true and vital part of the person. 3. Social comparison (Festinger 1954): A positive self-concept is a part of normal psychological functioning. An extension of this concept is that individuals evaluate themselves by comparing themselves to other group members. Usually, people compare their group with other groups in ways that reflect positively on themselves. Organizational Citizenship Behavior Organizations have been defined as systems of formal positions and roles (Blau & Scott, 1962) in which participants conform to the expectations of their positions. The term “organizational citizenship behavior” has been used to describe organizationally beneficial behavior of workers that is not prescribed but occurs freely to help others achieve the task at hand (Bateman & Organ, 1983). This willingness of participants to exert effort beyond their formal obligations has been recognized as an essential component of effective organizational performance. The practice of medicine is a complex activity that requires professional judgments and cannot fully be prescribed by clinical pathways. Thus, organizational citizenship behavior theory can provide useful insights in understanding physician acceptance and use of clinical pathways. 55 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Generalized compliance is a basic dimension of organizational citizenship behavior (Smith, Organ, and Near, 1983) relevant to this study. Generalized compliance refers to the impersonal conscientiousness of doing things “right and proper” for their own sake. In defining organizational citizenship behavior, Organ (1988) highlights some specific categories of discretionary behavior and explains how each helps to improve efficiency in the organization; two are relevant to this study: 1. Conscientiousness (e.g., efficient use of time and going beyond minimum expectations) enhances the efficiency of both an individual and the group. 2. Civic Virtue (e.g., serving on committees and voluntarily attending functions) promotes the interests of the organization. Borman and Motowidlo (1993) have proposed that individuals contribute to organizational effectiveness by doing things that are not necessarily their main task functions but are important because they shape the organizational and social context that supports task activities. In general, citizenship behaviors contribute to organizational performance because these behaviors provide an effective means of managing the interdependencies between members of a work unit and, as a result, increase the collective outcomes achieved (Organ, 1988; 1990,1997; Smith, Organ, & Near, 1983). Organizational citizenship also reduces the need for an organization to commit scarce resources to maintenance functions, thus freeing up more resources for goal-related activities. 56 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. In the study hospital, many of the highest-volume admitters belong to the same medical group. This medical group has also partnered with the hospital in a managed care risk-sharing agreement to provide care to large population of Medicare recipients. The group has a well-developed utilization management system and for several years, the physician leaders of the group have worked closely with hospital administration. Based on learning theory and innovation diffusion theory, it seems reasonable to expect physicians that are more culturally integrated in the organization to adopt clinical pathways and respond to profiles more rapidly than those physicians who are not so ingrained; therefore: Hypothesis 4. Resource utilization patterns for those physicians culturally integrated in the organizational culture will decline more than for those physicians who are less culturally integrated when both physicians are provided profiles and clinical pathways. Further, drawing from these perspectives, it seems reasonable to categorize the physicians who agreed to and helped support the program as physician leaders. As described earlier, these physicians were closely involved in developing and approving the clinical pathways and physician profiles. Further, as leaders, they are more likely to identify with the organization and its priorities. The theories suggest that the physician leaders would feel some obligation to demonstrate good citizenship, and “set 57 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. the example” for their colleagues, by using profile information and following clinical pathway recommendations. Therefore, Hypothesis 5. Resource utilization patterns will be lower for physician leaders than for physicians who are not leaders. Complexity Rogers (1995) defines complexity as the level of difficultly that may be encountered when trying to understand and use the innovation. Rogers believes that complexity influences adoption; innovations that are difficult to understand or use will not be as readily accepted as those perceived as easy to understand or use. Similarly, adoption of a difficult innovation may require the adopters to develop new skills and understandings. Rogers believes that ideas that can be tested on a small scale will generally be adopted more quickly. He refers to this as trialability, or the degree to which an innovation may be experimented with on a limited basis. An innovation that can be tested represents less uncertainty to the individual who is considering it for adoption, who can learn by doing. It could be argued that innovations that are less complex are more likely to be tested. As described in chapter two, the organization designed clinical pathways that were less complex than those they reviewed from other organizations. However, some pathways were more (or less) complex than others; therefore, 58 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Hypothesis 6. The less complex the clinical pathway, the greater the chance of accepting and using the pathway, and thus a greater change (reduction) in resource utilization patterns. Summary The literature indicates that a primary reason for profiling and pathway dissemination is to assist with continuous quality improvement efforts and reduce costs associated with unnecessary practice variation. The review demonstrates that there is varied evidence on the effectiveness of both tools and suggests the need for additional study. Profiling and pathways will have limited utility if physician behavior does not change as a result. Physicians wish to compare favorably with their peers, thus showing them how they rank against their colleagues and providing information about the best practice may to be an effective change strategy. The successful adoption of innovations often depends on the network of interpersonal relationships within a system or an organization. The ability within an organization to share ideas, observe trials of new ideas, and be influenced by the behavior and beliefs of trusted individuals all influence successful adoption and diffusion. There are several dimensions and attributes that may have a great influence on adoption: communication, relative advantage, time, social systems, opinion leaders, and complexity. 59 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Chapter IV Methodology Introduction This chapter first reviews the methods used by other researchers to assess the effectiveness of feedback and information sharing interventions. Then, it summarizes the methods and procedures used to collect, tabulate, and analyze the research data for this study. Review of Previous Methods This section summarizes the methods used in the studies that were reviewed in the previous chapter. Much of the research in the affective domain used controlled trials to determine efficacy. However, there is inconsistency in methods to evaluate the use of profiles and pathways. The Winickoff, et al. (1984) study investigating physician compliance with colorectal cancer screening standards employed a pre and post T-test design to evaluate the efficacy of educational meetings, and retrospective feedback of group compliance rate. The intervention that used retrospective feedback of individual compliance rate, and retrospective feedback of individual compliance rates compared to peers was evaluated with the chi-square test to compare performance between groups between periods. Similarly, the 1989 Pugh, et al., controlled trial 60 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. using daily feedback about inpatient charges employed T-tests to test for significant change in physician behavior. Marton, Tul and Sox (1985) study on information-sharing as a tool for modifying physician test-ordering practices utilized two-way analysis of variance tests to compare group means, and comparisons using both the Kruskal-Wallis test on ranks and Student T-test were also used. Paired comparisons were also made for each group before and after the intervention. Berwick and Coltin (1986) study of physician feedback in a health maintenance organization employed a crossover design controlled clinical trial. Three interventions were studied on the use of thirteen common blood tests among thirty-five internists within three ambulatory care centers. The blood tests were divided into three groups that were balanced for type of test and utilization rates. Three interventions were developed for use in a modified Greco-Latin square design with crossover of interventions, test groups, and ambulatory care center. In the Test-Specific Education intervention, two consecutive weekly departmental meetings were devoted to the discussion of appropriate use of tests in each of the three blood test groups. In the Peer Comparison Feedback on Cost of Test Use, intervention physicians received individual reports comparing their specific utilization rates against their colleagues for each of the tests within a particular test group. In the Peer Comparison Feedback on Yield on Test intervention, individual physicians received reports that ranked their abnormal test result rates for each test within the particular test group. In the crossover design, each test group was 61 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. subjected to each of the three interventions in a different center. Rates of test use, as measured by tests per 1000 encounters, and variation, as measured by coefficient of variation, among physicians within centers were measured during baseline and intervention periods. The effects of the intervention were measured by studying rate changes during intervention periods, compared with preceding nonintervention periods. Intervention effects on the change were analyzed using analysis of variance and Kruskal-Wallis techniques. Tierney, Miller and McDonald (1990) studied the effect of informing physicians of the charges for outpatient diagnostics in a primary medical care practice. All 121 physicians in the study ordered tests from computer workstations. For the intervention group (half of the physicians), charges for the test being ordered and total charges for tests for that patient were displayed on the computer. The control group did not receive messages about charges. A questionnaire to determine the physicians’ knowledge of test charges was administered once prior to the intervention and six months after the intervention. For each physician, the mean charges of tests ordered and the mean charges for tests per patient visit were calculated for each study period. When comparing the mean values for the intervention and control groups in the pre-intervention period, a weighted analysis of variance was used; for comparisons within the intervention period, a weighted analysis of covariance, with each physician’s pre-intervention mean entered as a covariate was used. To determine the accuracy of the physician’s estimate of the test charges, the absolute value of the percent deviation of each physician’s estimate 62 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. for each test was calculated. This score was used to compare the knowledge of test charges in the intervention and control groups at baseline, and the degree of improvement after the intervention. The experimental-control group study (Johnson et al., 1993) examining the effect of a physician education program on hospital length of stay and total patient charges consisted of a one-time exposure of physicians to clinical and financial information about their individual practice patterns for the treatment of pneumonia patients. Analysis of variance and T-tests were used to compare the intervention and control groups and to test for significant differences. Weingarten, et al. (1994) evaluated the effects of providing physicians a practice guideline recommending consideration of early hospital discharge for low- risk patients with chest pain. During six intervention periods, physicians received a structured message posted on patients' charts the day after admission that conveyed risk information and the guideline recommendation. Because patients receive usually care from many physicians, this study did not use individual physicians as the unit of analysis, but rather the aggregate practice for this specific diagnosis. Complication rates were compared using a chi-square test or Fisher exact test. Continuous data for the study groups were compared using the Student T-test, the Wilcoxon rank-sum test, or both when the data were notably distributed in a non normal pattern. An adjusted analysis comparing the two study groups with respect to total costs and length of stay was done using a stepwise regression procedure. 63 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. In the Johnson and Martin (1996) study, orthopedic surgeons were presented with verbal and written physician-specific materials over a seven-week period. X- bar and R charts (control charts) were constructed to monitor effects of the educational program on overall resource utilization, as measured by length of stay and average total charges. These charts were shared with each surgeon in the intervention group along with data profiling their specific practice and that of their peers. Two sample T-tests were used to test for statistical differences in mean length of stay and total charges. The studies by Johnson and Martin (1996), Weingarten, et al. (1994), and Johnson et al., (1993), and relate most closely to this empirical study. Statistical Design This study uses a quasi-experimental design since physicians were not randomly assigned to either the experimental or control groups. To determine if there were any changes in practice patterns attributable to the profile intervention, the data on select profile measures were subjected to statistical analysis. For purposes of the study, improvement was defined as: 1. A decrease in the average length of stay from 1995 to 1996, 2. A decrease in the average total charge from 1995 to 1996 (note: there was no change is the hospital’s pricing structure between 1994-1996; thus, adjusting for such changes was not required), 3. Decrease in the readmission rate, and 64 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 4. Decrease in the complications rate. Unit of Analysis To provide for a robust analysis of the data, there are several levels of analysis: at the physician level, the DRG level, and over time. Similar to the Weingarten (1994) study, DRGs are used as one unit of analysis since the profile reports for each physician were APRDRG-specific, and every physician providing care to a patient within the target APRDRGs received a report - even if it was only one patient. This was a conscious decision, given the difficulties of exclusion. For example, if one physician received the intervention on a specific APRDRG (e.g., Pneumonia), and another did not, it would be difficult to prevent the sharing of the profile or guideline between physicians. Controlling for spillover was problematic. To best limit spillover, the study would need to be conducted at two different sites where communication does not occur on a routine basis, or perhaps segment the physicians into groups but do not routinely communicate in a professional context. Given the small size of the organization, and the fact that many of the high-volume physicians belonged to the same medical group, these options were not feasible. Data Elements Patient-level data elements used in this study are: 1. Record Number: Unique number used to identify each inpatient admission. 65 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 2. Primary Physician Identification Number (MDID): The identification number for the physician primarily responsible for the majority of the patient’s care (usually the attending physician or primary surgeon). 3. All-Payer Refined Diagnosis Related Group (APRDRG): Diagnostic category (illness or operative procedure) for which the patient is being treated. 4. Experimental DRG (EXP): A value of one is assigned when the patient is in an APRDRG that is in the experimental group. 5. Control DRG (CON): A value of one is assigned when the patient is in an APRDRG that is in the experimental group. 6. Complexity of Illness (COI): Severity of illness scale discussed previously. 7. Complexity of Pathway (PWComp): A measure of the complexity of the pathway. The number, ranging from 20 to 50, represent the number of critical elements (treatments, medications, diagnostic tests) specified by the pathway. 8. Length of Stay (LOS): Number of days the patient was in the hospital. 9. Total Charges (TOTCHG): Charge data, obtained from the hospital's financial database, were based on actual prices charged for services rendered during the hospital stay. Charges for all services were aggregated into total charges. 66 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 10. Readmission (READM): A discrete variable, where a value of 1 is assigned if the patient is readmitted to the hospital for the same diagnosis within thirty days of the discharge date. 11. Complication (COMP): A discrete variable, where a value of 1 is assigned if the patient’s medical record data identifies a complication that occurred while the patient was in the hospital. 12. Physician Leader (MDLEAD): A value of 1 is assigned if the physician primarily responsible for the majority of the patient’s care: a) held leadership roles (elected medical committee member, elected department chair, appointed medical directors) during the study, b) actively participated in the profile or pathway development process or c) were identified as influential by the organization. There were 16 physicians identified as leaders. 13. IP A (IP A): A value of 1 is assigned if the physician was a member of the IPA medical group closely affiliated with the hospital. Assumptions The basic assumptions regarding this study are: 1. Hospital billing data (patient charges) are a reliable source of information to estimate physicians’ resource utilization of services and to monitor practice patterns. 67 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 2. Physicians understand the use of patient charges, which is the statistic used to measure their utilization of services on the profile. 3. Physicians understand the use of clinical pathways. 4. Physicians understand the use of severity adjustments, which were applied to their profiles to adjust for the severity of illness relative to their caseload. Data Preparation The APRDRG data were examined at three levels of aggregation: 1. Ten targeted APRDRGs where physicians received a profile and pathway (experimental group), 2. Ten targeted APRDRGs where physicians received a profile and pathway (experimental group), and were considered to be leaders. 3. Ten non-targeted targeted APRDRGs for which no physician received a profile or pathways (control group). For each group, a pre-intervention measure (1995) and a post-intervention measure (1996) were taken. For each measurement period, and each group as a whole, utilization and outcomes rates were calculated for the four measures described earlier. The aggregate data was analyzed with SPSS. 68 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Data Analysis Hypothesis la Resource utilization patterns, as measured by length of stay and total charges, for those APRDRGs where physicians receive profiles and clinical pathways will decline following the intervention. One-way analysis of variance (ANOVA) was performed to determine any significant differences for mean length of stay and mean total charge. Hypothesis lb Resource utilization patterns, as measured by length of stay and total charges, for those APRDRGs where physicians do not receive profiles or clinical pathways will not significantly decline following the intervention. One-way analysis of variance was used to determine any significant differences for mean length of stay and mean total charge. Hypothesis 2 There will be a significant improvement in outcomes, as measured by readmission and complication rates, for the intervention APRDRGs when comparing the baseline period (1995) to the post-intervention period (1996). The chi-square test was performed to determine any significant differences between APRDRG-COI for readmission and complication rates. 69 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Hypothesis 3 In terms of rate of adoption, Rogers theorizes that innovation goes through a period of slow, gradual growth before experiencing a period of relatively dramatic and rapid growth. Since none of the existing studies have reviewed the effect of profiles or pathways over time, this study explores this issue. The project directors anticipated that resource utilization would decline over time. Thus, given the one- year time frame of this study, it seems reasonable to expect the measures to consistently decline in each of the four quarters of the study year. The Student T- test was employed to determine any significant differences between variations for mean length of stay and mean total charge when comparing each quarter’s average against the baseline for both the experimental and control groups. Hypothesis 4 Resource utilization, as measured by length of stay and total charges, for those APRDRGs for “more culturally integrated” physicians who are exposed to the innovation will decline significantly more than for those “less culturally integrated” physicians who are exposed to the innovation. It seems reasonable to propose that those physicians who spend more time in the hospital are more likely to be more culturally integrated. The number of discharges was used as a proxy for time in the hospital; i.e., physicians with a greater caseload spend more time in the hospital. Multiple regression analysis was employed to test this proposition, and also used to test hypotheses 5 and 6. The models are discussed later in this chapter. 70 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Hypothesis 5 Following the intervention, resource utilization patterns, as measured by length of stay and total charge will be significantly lower for physician leaders who receive profiles and clinical pathways versus non- leader physicians who also receive profiles and clinical pathways. The Student T-test was employed to determine any significant differences between APRDRG-COI variations for mean length of stay and mean total charge. The multiple regression analysis model described below was also used to examine this proposition. Hypothesis 6 Rogers believes that complexity influences adoption; innovations that are difficult to understand or use will not be as readily accepted as those perceived as easy to understand or use. As described in chapter two, the organization designed clinical pathways that were less complex than those they reviewed from other organizations. However, it is likely that some of the pathways were perceived as more (or less) complex than others. As described in chapter two, the pathways developed in the study organization were presented in a grid format, with the columns representing the day of treatment, and the rows containing the critical aspect of care. An “X” was placed at the intersection, denoting the day that the care should occur. It seems reasonable to propose that the number of critical elements on the grid (which ranged from 20 to 50) can be used as a proxy for the complexity of the pathway. 71 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Multiple Regression Model The models propose that several characteristics are determinants of average length of stay and average total charge. These determinants are: complexity of illness, average complexity of the pathway, status as a leader, membership in the medical group, and number of patients. It is expected that complexity of illness and complexity of the pathway will be positive values; that is, as these increase, so does the average length of stay or average total charge. Conversely, status a leader, membership in the medical group, and number of patients are expected to be negative values. Model 1: AVGLOS = Fx (Avg COI, PWComp, MDLEAD, IP A, NPat) Model 2: AVGTOTCHG = Fx (Avg COI, PWComp, MDLEAD, IP A, NPat) Two similar models are examined for the control group. Since there is no intervention, pathway complexity was removed from the models. These models will enhance the analysis of complexity of illness and pathway complexity. Model 3: AVGLOS = Fx (Avg COI, MDLEAD, IP A, NPat) Model 4: AVGTOTCHG = Fx (Avg COI, MDLEAD, IP A, NPat) Where Avg COI, PWComp, NPt, are continuous variables; MDLEAD and IP A are discrete variables. 72 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Summary This chapter presented the research methodology and procedures, including issues related to the study population, design, instrumentation, and statistical analysis. A quasi-experimental method applying Student T-test, chi-square test, analysis of variance, and multiple regression analysis was used to determine the impact of profiling and clinical pathways in modifying physician resource utilization within the hospital setting. The next chapter contains the analyses and findings. 73 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Chapter V Findings Descriptive Analysis Statistical analysis was performed using SPSS, version 8.0. Table 5-1 provides descriptive statistics in terms of volume, standard deviation, and the change in average length of stay and average total charge between the pre and post intervention periods. Overall, there was a reduction in the mean length of stay in the experimental group from 4.43 days to 3.69 days. Changes in mean total charge per case were pronounced; overall, there was a reduction in the mean from $10,911 to $9,215. Table 5-1. Experimental APRDRGs, Pre and post intervention, Length of Stay & Total Charges________________ ______________________________________ Pre-Intervention (1995) N=3,944 Post-Intervention( 1996) N=3,178 Change Mean Min Max Std Dev Mean Min Max Std Dev Mean Std Dev Mean % LOS 4.43 1 48 2.73 3.69 1 22 2.28 -0.74 -1.68 -16.60% Charge 15,739 2,557 143,815 7,909 13,274 1,010 63,123 5,850 -2,466 -5,686 -15.70% As hypothesized and illustrated in table 5-2, the change in the control group was much less pronounced. In fact, the overall mean increased for both average length of stay and average total charge. 74 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 5-2. Control APRDRGs, Pre and post intervention, Length of Stay and Total Charge____________________________________________________________ Pre-Intervention (1995) N = l,337 Post-Intervention( 1996) N = l,018 Change Mean Min Max Std Dev Mean Min Max Std Dev Mean Std Dev Mean % LOS 3.46 1 18 2.1 3.64 1 18 2.03 0.19 -0.07 5.49% Charge 12,563 1,075 105,161 12,584 13 1,541 58,672 5,894 21 -1,316 0.17% Table 5-3 summarizes the number of readmissions and complications for both the control group and the experimental group for the pre-intervention period (1995) as well as the post-intervention period (1996). The data demonstrate a reduction in the absolute numbers for each category. This finding is explored further later in this chapter. Table 5-3. Readmissions and Complications, Experimental and Control Groups Readmissions Complications YEAR Control Experimental Control Experimental 1995 21 48 9 36 1996 19 30 6 25 Change -2 -18 -3 -11 In table 5-4, the analysis of the control group is further refined to examine the descriptive statistics for those APRDRGs where the physicians received any report for an APRDRG in the experimental group. The data were divided into two groups: those who received the treatment and those who did not. Length of stay increased from 1995 to 1996 for both groups. Average charge also increased for the group that received the treatment. Interestingly, the average charge for the group that received no reports declined, albeit very slight (by $264, from $17,091 to Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. $16,827, or 1.5%). At the aggregate level these results suggest that spill-over or contamination effects were minimized. Table 5-4. Control Group examined for Spill-Over Effect Received Exp] Ceport for DRG No Report TO!rAL 1995 1996 1995 1996 1995 1996 Cases 1,089 854 248 164 1,337 1,018 MDs 161 148 85 65 246 213 Avg LOS 3.28 3.48 3.81 3.97 3.38 3.56 Std Dev 2.36 2.35 2.93 3.00 2.48 2.47 Avg Charge 10,764 11,185 17,091 16,827 11,937 12,094 Std Dev 7,848 7,024 13,537 11,275 9,491 8,128 Correlation values between the variables are presented in table 5-5. Six values were of significant magnitude (above 0.35). The correlation between complexity of illness and length of stay was 0.3979. Similarly, the correlation between complexity of illness and total charge was 0.4001. These relationships are expected, since patients with more severe illness tend to spend more time in the hospital and receive more care. The correlation between length of stay and total charges was 0.825. Such a relationship is expected, since as length of stay increases, so does the total charge. Two variables were strongly correlated with pathway complexity: length of stay (0.4870) and total charge (0.6091). Here too, such a relationship was predictable. Lastly, the correlation of 0.3811 between IPA Member and MD Leader was not surprising, given the strong relationship between the hospital and the IPA. 76 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 5-5. Correlation Table Illness Complex Re admission Compli cation Length o f Stay Total Charge MD Leader IPA Member Pathway Complex Illness Complexity 1.0000 Readmission 0.0472 1.0000 Complication 0.0605 -0.0101 1.0000 Length o f Stay 0.3979 0.0312 0.0478 1.0000 Total Charge 0.4001 0.0327 0.1127 0.8254 1.0000 MD Leader -0.0195 -0.0165 0.0371 -0.0178 0.0322 1.0000 IPA Member -0.1097 -0.0429 0.0095 -0.1180 -0.1326 0.3811 1.0000 Pathway Complexity 0.3344 0.1065 0.0518 0.4870 0.6091 0.0177 -0.3003 1.0000 Multiple Regression Analysis For convenience, the overall results of the multiple are presented first, and then further discussed under the relevant hypotheses. Diagnostics were performed on the statistical output, and are summarized below. a) Absence of Heteroskedasticity was satisfied; the plot of residuals revealed two tight groups with many observations falling within +/- 5, with some outliers. b) Normal Distribution of Error Term appeared to be satisfactory. The actual vs. expected results in the normal probability plot were close together. However, a stem-leaf plot indicated some skewing toward the right. c) A bsence o f Outliers appeared to be satisfied; there w ere three outliers. d) Absence of Multicollinearity appeared to be satisfactory; the standard errors are not substantial. 77 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. e) Linearity was not satisfied; there was an expected relationship between complexity of illness and length of stay. As table 5-6 illustrates, the adjusted R-squared value reveals that slightly more than 32% of the variation in average length of stay is explained by average complexity of illness, average complexity of pathway, status as a leader, membership in the medical group, and number of patients in the experimental group. The model suggests that only complexity of illness and complexity of pathway are significant determinants (.05 level). It was interesting to note that status as a physician leader increases length of stay, while membership in the IPA decreases length of stay. Table 5-6. Average Length of Stay as Dependent Variable, Experimental Group Regression Statistics Multiple R 0.5690 R Square 0.3238 Adjusted R Square 0.3103 Standard Error 1.6401 Observations 256 ANOVA d f 55 MS F Signif F Regression 5 321.9877 64.3975 23.9412 0.0000 Residual 250 672.4546 2.6898 Total 255 994.4423 Coefficients Standard Error tS ta t P-value Intercept -1.5232 0.5365 -2.8390 0.0049 Number o f Discharges -0.0011 0.0054 -0.2012 0.8407 Average Complexity o f Illness 1.0499 0.2403 4.3698 0.0000 Average Complexity o f Pathway 0.0798 0.0150 5.3279 0.0000 IPA Membership -0.2962 0.4711 -0.6289 0.5300 Leader Status 0.2826 0.2383 1.1858 0.2368 78 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Similarly, table 5-7 shows that the adjusted R-Square value reveals that slightly more than 31% of the variation in average total charge is explained by average complexity of illness, average complexity of pathway, status as a leader, membership in the medical group, and number of patients in the experimental group. Again, this model suggests that only complexity of illness and complexity of pathway are significant determinants (.05 level). In this model, physician leader status decreases average total charge, while membership in the IPA increases the amount. Table 5-1. Average Total Charge as Dependent Variable, Experimental Group Regression Statistics Multiple R 0.5599 R Square 0.3135 Adjusted R Square 0.2998 Standard Error 5419.9098 Observations 256 ANOVA d f SS MS F S ignif F Regression 5 3353372429 670674486 22.8311 0.0000 Residual 250 7343855600 2937522 Total 255 10697228029 Coefficients Standard Error t Stat P-value Intercept -3845.5578 1773.0246 -2.1689 0.0310 Number o f Discharges -6.2052 17.9456 -0.3458 0.7298 Average Complexity o f Illness 2696.8600 794.0211 3.3965 0.0008 Average Complexity o f Pathway 297.2891 49.4756 6.0088 0.0000 IPA Membership 797.1239 1556.7021 0.5121 0.6091 Leader Status -858.7364 787.4801 -1.0905 0.2765 79 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The results of the regression analysis for length of stay in the control group are presented in table 5-8. It is important to note that the adjusted R-Square value for both the control and experimental groups is similar: 0.3086 and 0.3103, respectively. The only variable of note in this regression was complexity of illness. It is interesting to compare the difference in the coefficient for COI between the two groups: 1.7138 in the control group, vs. 1.0499 for the experimental group. Table 5-8. Average Length of Stay as Dependent Variable, Control Group Regression Statistics Multiple R 0.5671 R Square 0.3216 Adjusted R Square 0.3086 Standard Error 1.5095 Observations 213 ANOVA d f SS MS F Signifi cance F Regression 4 224.6994 56.1749 24.6539 0.0000 Residual 208 473.9352 2.2785 Total 212 698.6347 Coefficients Standard Error tS ta t P-value Intercept 0.0769 0.3443 0.2233 0.8235 Number o f Discharges 0.0244 0.0246 0.9929 0.3219 Average Complexity o f Illness 1.7138 0.1817 9.4311 0.0000 IPA Membership -0.3652 0.4240 -0.8615 0.3900 Leader Status -0.3060 0.6234 -0.4908 0.6241 The results of the regression analysis for average total charge in the control group are presented in table 5-9. It is important to note that the adjusted R-Square value for both the control and experimental groups is similar: 0.2809 and 0.2998, respectively. Again, the only significant variable was complexity of illness. It is 80 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. interesting to compare the difference in the coefficient for COI between the two groups: 2,696 in the control group, vs. 5,246 for the experimental group. Table 5-9. Average Total Charge as Dependent Variable, Control Group Regression Statistics Multiple R 0.5427 R Square 0.2945 Adjusted R Square 0.2809 Standard Error 4987.5533 Observations 213 ANOVA d f SS MS F Signif icance F Regression 4 2159853967 539963492 21.7065 0.0000 Residual 208 5174143088 24875688 Total 212 7333997055 Coefficients Standard Error tS ta t P-value Intercept 1173.0059 1137.5997 1.0311 0.3037 Number o f Discharges 100.5218 81.2298 1.2375 0.2173 Average Complexity o f Illness 5246.8479 600.4229 8.7386 0.0000 IPA Membership -1102.8282 1400.8476 -0.7873 0.4320 Leader Status -1360.5677 2059.8489 -0.6605 0.5097 Hypothesis la To determine the statistical differences among the experimental APRDRGs for length of stay and total charge, the means between the pre and post intervention periods were compared using one-way analysis of variance (ANOVA). Prior to conducting the analysis, box plots were generated to determine if there was anything unusual about the distribution; this revealed some outliers and a few extreme cases. Assumptions for one-way ANOVA were upheld. ANOVA procedures are reasonably robust to departures from normality, and data 81 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. transformations were not necessary. The significance level is based on actual F values and degrees of freedom. The results of table 5-10 suggest support for the hypothesis given the statistically significant decrease in length of stay and charges. Table 5-10. Experimental APRDRGs, Pre and post intervention, ANOVA Tests Mean Lenjgth o f Stay Mean Total Charge Pre (1995) Post (1996) F-Value P-Value Pre (1995) Post (1996) F-Value P-Value 3.40 2.93 2.074 0.040* 10,911 9,215 1.870 0.035* * Statistically significant (p<=.05) Hypothesis lb To determine the statistical differences among the control APRDRGs for length of stay and total charge with each APRDRG-COI, a comparison between the pre and post intervention periods using one-way analysis of variance (ANOVA) was conducted. As shown in table 5-11, the hypothesis was supported; there was not a significant decline in the APRDRGs where physicians did not receive profiles or pathways. Table 5-11. Control APRDRGs, Pre and post intervention, ANOVA Tests Mean Len ^th o f Stay Mean Total Charge Pre (1995) Post (1996) F-Value P-Value Pre (1995) Post (1996) F-Value P-Value 3.38 3.56 0.410 0.681 11,937 12,094 -1.541 0.126 Hypothesis 2 In the experimental group, there were slight decreases in both the complication and readmission rates. To determine if these changes were significant, 82 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. analysis of variance and Kruskal-Wallis techniques were used. None of the changes were statistically significant, as illustrated in table 5-12. Table 5-12. Experimental APRDRGs, Chi-Square Test, Complications and Readmissions Pre (1995) Post (1996) Change ChiSq P Value Readmission 0.0122 0.0091 -0.0030 0.097 0.756 Complication 0.0157 0.0067 -0.0090 0.382 0.536 In the control group, there was a slight decrease in the readmission rates and a slight increase in the complication rate. Analysis of variance and Kruskal-Wallis techniques found that these changes were not statistically significant; see table 5-13: Table 5-13. Control APRDRGs, Chi-Square Test, Complications and Readmissions Pre (1995) Post (1996) Change ChiSq P Value Readmission 0.0094 0.0187 0.0093 0.167 0.683 Complication 0.0079 0.0059 -0.0020 0.027 0.870 Thus, no evidence was provided for the hypothesis. That is to say, there was no support for the contention that the use of clinical pathways and physician profiles leads to significant improvement in outcomes. Hypothesis 3 To explore if there changes over time, average length of stay and total charge for the four calendar quarters of 1996 for both the experimental and the control groups were compared against the baseline. The Student T-test was employed to examine 83 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. the change. As tables 5-14 and 5-15 illustrate, there were significant decreases for the experimental group during the last two quarters of 1996, thus offering limited support for the hypothesis that innovation goes through a periods of gradual, then relatively dramatic and rapid growth periods. Table 5-14. Experimental Group, Average Length of Stay by Quarter__________ N ALOS Change + T-Statistic p-value 1996 Q1 784 3.56 0.16 0.77 0.44 1996 Q2 788 2.93 -0.47 2.01 0.09 1996 Q3 800 2.67 -0.73 2.34 0.031* 1996 Q4 806 2.59 -0.80 2.36 0.029* + Versus baseline (1995) average o f 3.40 * Statistically significant (p<,05) Table 5-15. Experimental Group, Average Total Charge by Quarter N Average Charge Change + T-Statistic p-value 1996 Q1 784 10,312 -599 0.81 0.43 1996 Q2 788 9,147 -1,764 2.12 0.06 1996 Q3 800 8,910 -2,001 2.32 0.035* 1996 Q4 806 8,516 -2,394 2.69 0.027* + Versus baseline (1995) average o f $10,991 * Statistically significant (p<05) Figure 5-1 graphically illustrates the change in average length of stay and average total charge over time. Figure 5-1. Experimental Group, Average Length of Stay and Charge by Quarter. Length of Stay 4.0 3.5 3.0 2.5 2.0 0.5 0.0 1996 Q1 1996 Q2 1996 Q3 1996 Q4 Average Charge 84 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Further, as tables 5-16 and 5-17 illustrate, resource utilization in the control group did not significantly change. N ALOS Change + T-Statistic p-value 1996 Q1 257 3.74 0.36 1.75 0.09 1996 Q2 250 3.47 0.09 0.76 0.47 1996 Q3 252 3.32 -0.06 0.71 0.49 1996 Q4 259 3.71 0.33 2.03 0.08 + Versus baseline (1995) average o f 3.38 Table 5-17. Control Group, Average Total Charge by Quarter N Average Charge Change + T-Statistic p-value 1996 Q1 257 $12,558 $621 1.57 0.09 1996 Q2 250 11,897 -39 0.67 0.46 1996 Q3 252 11,652 -285 0.72 0.48 1996 Q4 259 12,253 316 2.01 0.08 + Versus baseline (1995) average of! 511,937 Hypothesis 4 As previously illustrated in the multiple regression analysis output in tables 5-4 and 5-5, there was no support for the hypothesis that utilization for those physicians more culturally integrated in organization will decline more than for those less culturally integrated. Hypothesis 5 To determine if there was a statistical difference between physician leaders and non-leaders, the Student T-test was used to analyze differences in length of stay and total charge. As tables 5-18 and 5-19 show, this hypothesis was not supported. 85 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Further, the results of the multiple regression analysis did not extend support for this proposition. Table 5-18. Average Length of Stay, Leaders v. Non-Leaders, 1995 v. 1996 Non Leader Leader Difference P-Value 1995 3.40 3.37 -0.02 0.89 1996 3.14 2.90 -0.24 0.12 Change -0.26 -0.47 P-Value 0.16 0.08 5-19. Average Total Charge, Leac ers v. Non-Leaders, 1995 v. 1996 Non Leader Leader Difference P-Yalue 1995 10,987 11,865 878 0.48 1996 9,798 10,329 532 0.45 Change -1,189 -1,535 P-Value 0.06 0.07 Hypothesis 6 The multiple regression analysis models (see tables 5-4 through 5-7 above) suggest that complexity of pathway is a significant determinant for both length of stay and total charges. Thus offering support for the contention that the less complex the clinical pathway, the greater the chance of accepting and using the pathway, and thus a greater change (reduction) in resource utilization patterns. 86 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Summary The findings of the data analysis are summarized in table 5-20. Table 5-20. Summary of Hypotheses and Findings Proposition Finding la Resource utilization patterns will decline when physicians are provided profiles and clinical pathways. Supported lb Resource utilization patterns will not decline when physicians are not provided profiles and clinical pathways. Supported 2 There will be a significant improvement in outcomes between the pre and post intervention periods. Not Supported 3 Resource utilization patterns will decline over time (each quarter). Supported 4 Resource utilization for physicians culturally integrated in the organizational culture will decline more than for those physicians less culturally integrated. Not Supported 5 Resource utilization patterns will be lower for physician leaders than for physicians who are not leaders. Not Supported 6 The less complex the clinical pathway, the greater the reduction in resource utilization patterns. Supported 87 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Chapter VI Discussion Several researchers have suggested combining various approaches and multiple tools to change physician behavior. However, they have not found evidence identifying the best mix of complementary interventions. None of the existing research has examined the impact of a comprehensive program consisting of profiling, benchmarking, and clinical pathways. This study attempts to bridge this gap in the current published research. General Limitations A case-study approach is utilized, thus generalization and the ability to replicate this study may be severely limited, given the distinct characteristics of any organization. In particular: 1. The study organization had a long history of physician involvement in quality improvement activities. 2. Many physicians had been sensitized to the importance of cost containment given the organization’s early participation in the Medicare risk-sharing program. 3. The organization’s shaky financial status may have impacted the physicians’ adoption of the innovation. 88 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. There are several other limitations of this study that must be highlighted: 1. Physicians in the study may not have been exposed to the same patients for the entire study period, since patients may change physicians at any time. 2. Physicians take call for their partners, thus increasing the possibility of study contamination. 3. Physicians may not have reviewed either their profiles or the pathways. 4. The intervention group was limited to ten diagnoses; a different number or variety may have changed the results. Use of Profiles and Pathways Changing inappropriate utilization patterns has been touted as one way to control costs and improve the quality of healthcare. It seems that the dissemination of a diagnosis-specific physician profile in concert with a diagnosis-specific clinical pathway was found to be effective in reducing resource utilization, as measured by length of stay and total charges. However, this study did not measure whether or not pathways were actually used by physicians to assist their decision-making. This study also suggests that resource utilization patterns, as measured by length of stay and total charges, may not decline for those diagnostic groups where physicians do not receive profiles or pathways. Previous studies that randomly assigned physicians to participate in similar programs were unable to control for spillover or unable to adequately explain the effects. This study attempted to 89 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. control for and examine spillover in two different ways: by examining the results between the control and experimental APRDRG groups and within the control group by comparing those physicians who received information on the patients in the experimental group against those who did not receive information for patients in the control group. By using APRDRGs that differed in clinical nature for both the control and intervention groups, controlling for spillover was not necessary. Thus, the organization did not need to deal with the thorny issue of designing methods to prevent physicians from discussing the intervention with their control-group colleagues and avoided the ethical dilemma of not sharing clinical pathways based on best practice. However, this cannot be definitively assessed; since the control group consisted of only ten APRDRGs. It is possible that other APRDRGs did experience spillover. While the results demonstrated that the use of profiles and pathways did not positively impact outcomes, it is important to note that that efficiency was improved without degrading clinical results. It is possible that better aggregate care may result with prolonged measurement and monitoring processes that facilitate changes in care design and delivery. Lastly, this study does not test for differences between use of the tools and guidelines since both the pathway and profile were tested together. Future research may take a multiple methods approach comparing one intervention with two interventions, with three interventions, etc. Overall, this study suggests that the combined dissemination of both physician profiles and clinical pathways may be a 90 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. sufficient diffusion method to communicate the need for physicians to change their resource utilization behaviors. Rate of Adoption The results of this study suggest that there is a rate of adoption for profiles and clinical pathways, supporting Rogers (1995) belief that the innovation typically moves slowly through the social system when it is first introduced. It is important to note that four quarters may not provide a sufficient data to conclude that there is a rate of adoption. However, the data demonstrated that there was not significant change until the third and forth quarters in 1996 - suggesting that physicians may have taken a “wait and see” approach. Cultural Integration and Physician Leadership Extension of various theories suggested that physicians more culturally integrated in the organizational culture to adopt clinical pathways and respond to profiles more rapidly than those physicians less ingrained in the culture. However, this hypothesis was not supported by the data. Perhaps volume was not a valid proxy to measure organizational entrenchment. The literature suggests that physician leaders are critical in ensuring the success of both profiling and pathway (or guideline) development and dissemination programs. An extension of diffusion theory suggests that physician participation in the development of such programs impacts the effectiveness of the intervention, and 91 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. recommends including medical directors and respected members of the medical staff in the development of both profile metrics and the clinical pathway. Lastly, an extension of organizational citizenship behavior theory might suggest that the physician leaders would feel some obligation to demonstrate good citizenship, and would “set the example” by using profile information and following clinical pathway recommendations. While it is difficult to accurately access the impact that such medical staff leaders play, this study hypothesized that these leaders would have lower resource consumption patterns than their non-leader colleagues when both groups receive profiles and pathways. However, this hypothesis was not supported. Although not statistically significant, it was interesting to note that physician leader status decreased average total charge, while membership in the IP A increased the amount. Conversely, physician leader status increased length of stay, while membership in the IP A decreased length of stay. It is possible the physician leaders were practicing medicine in a manner suggested by the profiles before the dissemination of the profile. That is, that they were familiar with and following current evidenced-based guidelines that were used to develop the pathways. Another plausible explanation is that the pathway was developed based on their practice; i.e., they were the best practice physicians. At the other extreme, it is possible that some physician leaders opted not to follow the guideline. 92 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. A weakness of this study is that it did not directly assess the leaders’ impact, instead it attempted to measure the change in their behavior. However, the supposition that their support is critical makes sense intuitively, and it is not likely that the medical staff would have accepted the intervention without the leaders’ support. Complexity Rogers (1995) believes that complexity influences adoption; innovations that are difficult to understand or use will not be as readily accepted as those perceived as easy to understand or use. Although the measure of pathway complexity was simplistic, the data offered support for the hypothesis that the less complex the clinical pathway, the greater the chance of accepting and using the pathway. Future Research This dissertation has identified several questions and issues to be considered in future research on physician profiling. In summary, future studies might include: 1. Whether pathways developed in one organization can be directly applied in another. 2. Whether other quality models (e.g., Juran, Crosby, or hybrids) are more effective in changing physician behavior 3. Examination of both the format and content of profiles to determine how different metrics and feedback methods impact physician behavior. 93 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 4. Examination of the characteristics of guideline adoption (i.e., characteristics of the health-care professional, the practice setting, incentives, regulation, and patient factors). 5. Examination of different intervention combinations and organizational characteristics (such as type, size, ownership, teaching status, payer mix, etc.) to identifying the most effective mix of combinations given a specific set of organizational characteristics. 6. Examination of traits and characteristics to measure the level of cultural integration in the organization. 7. Examination of rate of adoption over a longer period to fully explore the question. 8. 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Changing and learning in the lives o f physicians. New York, NY: Praeger, 1989. Rogers EM. Diffusion o f innovations, 4th ed. New York, NY: Free Press, 1995. Rosenstock IM. Historical origins of the health belief model. Health Education Monograph 2: 328-335, 1974. Salem-Schatz S, Moore G, Rucker M, Pearson SD. The case for case-mix adjustment in practice profiling: when good apples look bad. Journal o f the American M edical Association 272(11): 871-4, 1994. Schriefer J. The synergy of pathways and algorithms: two tools work better than one. Joint Commission Journal on Quality Improvement 20(9): 485-99, 1994. Scott WR. Institutions and organizations (second edition). Thousand Oaks CA: Sage Publications, 2001. Smith CA, Organ DW, and Near JP. Organizational citizenship behavior: It’s nature and antecedents. Journal o f Applied Psychology 68, 653-663, 1983. 101 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Soumerai SB, Avorn J. Principles of educational outreach (academic detailing) to improve clinical decision making. Journal o f the American M edical Association 263: 549-556. 1990. Soumerai SB, et al: Effect of local medical opinion leaders on quality of care for acute myocardial infarction. Journal o f the American Medical Association 279:1358-1363,1998. Spoeri RK, Ullman R. Measuring and reporting managed care performance: lessons learned and new initiatives. Annals o f Internal Medicine 127(8): 726-32, 1997. Tajfel H, Turner JC. An integrative theory of social conflict. In Austin W and Worchel S (eds.), The social psychology of intergroup relations. Monterey, CA: Brooks/Cole, 1979. Thompson RS. Systems approach and the delivery of health services. Journal o f the American Medical Association 277: 668-671, 1997. Thomson MA, Oxman AD, Davis DA, et al. Audit and feedback to improve health professional practice and health care outcomes: Part II. Cochrane Review, 1999. Tierney WM, Miller ME, McDonald CJ. The effect on test ordering of informing physicians of the charges for outpatient diagnostic tests. New England Journal o f Medicine 322(21): 1499-504, 1990. Weingarten SR, Riedinger MS, Conner L, Lee TH. Practice guidelines and reminders to reduce duration of hospital stay. Annals o f Internal Medicine 120(4): 257-63, 1994. Welch HG, Miller ME, Welch WP. Physician profiling: an analysis of inpatient practice patterns in Florida and Oregon. New England Journal o f Medicine 330(9): 607-12, 1994. Winickoff RN, Coltin KL, et al. Improving physician performance through peer comparison feedback. Medical Care 22(6): 527-34, 1984. 102 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Appendix 1, Text of Cover Letter that Accompanied the Profile Dear Colleague, Attached is information regarding what we call "Best Practice” diagnoses. We have targeted several all-payer refined diagnosis related groups (APR-DRGs) for best practice initiatives because of their high cost or high volume. The goal of a best practice initiative is to reduce variation in care, outcome, and cost by identifying practice patterns with quality outcomes and appropriate resource utilization. Statistical and qualitative analysis can be used to develop clinical paths which can reduce variation. The purpose of these reports is to enhance your awareness of these statistically determined best practices, and to allow you to compare your practice patterns to your peers in terms of outcomes and resource utilization. If we are to continue serving our community, we must use our resources as effectively and efficiently as we can. If we do this, the hospital will reduce the cost of care, while improving quality. We believe that development and implementation of such things as best practice guidelines, clinical pathways, and suggestions you may see when comparing your information to your peers, play an important role in dealing with these challenges. Several committees composed of physicians and hospital personnel developed clinical paths for identifying key success factors to deliver cost-conscious quality care to our community. We believe that reducing the variation in the way we care for similar patients could significantly improve outcomes, patient satisfaction and reduce costs. This is not a matter of who's doing something right or better than a colleague. It is simply looking at the difference and asking, "Can I do something differently that enhances patient care by reducing unnecessary variability?" We hope you find this information useful. We encourage you to discuss these findings with your colleagues and to participate actively on a best practice committee. These reports were produced by Decision Support Services and Quality Management; if you have any questions regarding these reports, feel free to contact <the Director of Quality M anagement at <phone number>. Sincerely, <Name> <Name,> MD President and Chief Executive Officer Vice President, Medical Affairs 103 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Appendix 2, Text of User Guide UNDERSTANDING YOUR REPORT Each Report Contains: 1. Analysis by Attending Physician/Primary Surgeon This report is alphabetically coded to preserve confidentiality. You will find your code printed on a separate sheet. Information includes: Volume, Average Length of Hospital Stay, Average Complexity of Illness, Discharge Status (Regular, Home Health, SNF, and Expired), Readmission Rate (same patient within 30 days for the same DRG), and average (mean) charges per case for these areas: Room and Board, Surgery, Pharmacy, Laboratory, Radiology, Respiratory, Physical Medicine, Supplies (SPD) and Other. At this time, charges are a proxy measure of cost; the hospital is actively examining cost accounting systems for future implementation. 2. Hospital-Wide Outcomes Examines all patients within the DRG by discharge status, readmission rate, and admission source. Variables examined are length of stay complexity of illness, charges, patient age, and mortality rates. 3. Analysis by Complexity o f Illness (COI) Examines patients based on the complexity of their illness. Complexity of Illness is an index of case complexity from 1 (minor) to 4 (extreme). Charge opportunity illustrates the potential reduction in charges that could be realized if all discharges with a total charge were equal to the mean. Standard deviation of length of stay and charges is provided to illustrate the range of variance from the average in practice patterns. The higher the standard deviation, the more variance. 4. A Graph o f Ancillary Charges by Complexity o f Illness Provides a visual depiction of ancillary services utilization (pharmacy, laboratory, radiology, respiratory, and physical therapy/occupational therapy. Comparing Your Outcomes with Peers Physicians are ranked by average charge per case in descending order; this is not a best-to-worst ranking. When comparing your statistics to others, you should try to find a physician with an average COI that is similar to yours. Look at everything — You may find that your LOS is higher than a comparable colleague, but your outcomes (i.e., death rate and readmission) are better. 104 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Appendix 3 , Sample Profile Report Medical Center Clinical Activity Profile Diagnostic Group 148: Major Small & Large Bowel Procs Jan 95 thw Dec 98 i n O Physician Nam e Sam ple, Ima Number 1234 Specialty Surgery Medical Staff Departm ent _________ Surgery_________ Minor Moderate Major Extreme Total Avg fC O I-1 1 fC O I-2 1 rcoi-3) fC O I-4 1 C ases C O I You 0 2 0 4 6 3.33 0% 33% 0% 67% Surgery 17 32 17 9 75 2.24 Department 23% 43% 23% 12% All Discharges 17 32 18 9 76 2.25 22% 42% 24% 12% You Peers Regular 33% 55% Home Health 17% 18% CEC 0% 14% Outside SNF 0% 3% Transfer 50% 1% You Peers Deaths (In Hospital) 2 1 Percentage 33.3% 1.3% Average Age 52.0 64.3 Average COI 4.00 4.00 National Death Rate: 4.8% Complexity Adjusted Comparison with Peers Ancillary Charges 25,000 19,433 20,000 13,197 15,000 10,509 7,970 10,000 4,749 5,000 R E S P PM LAB RAD RX □ Expected (Peers) □ Observed (You) Length of Stay (Excludes CEC days) 16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00 11.83 Expected (Peers) Observed (You) You Peers Within 30 Days, Sam e DRG 0% 0% Wthin 30 Days, Any DRG 17% 9% Wthin 60 Days, Any DRG 17% 13% Wthin 90 Days, Any DRG 17% 14% You Peers Average OR Time (Minutes) 173 175 Average PACU Time (Minutes) 128 162 Avg Blood Units Tranfused 5.17 1.87 Infections per 1,000 C ases 0.0 71.4 Complications per 1,000 0.0 171.1 I Telem etry j | Intensive Care| You Peers You Peers Num ber of Patients 0 5 4 16 Percent of C a se s 7% 67% 23% Avg Days in Tele/Unit 6.0 8.5 4.1 A verage Complexity 2.4 3.5 2.9 Complexity Adj ALOS 6.0 8.5 7.3 Complexity Adjusted CEC Length of Stay 0.0 0.0 Expected (Peers) O bserved (You) CEC days are not included in acute days Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission. Sam ple, Ima Diagnostic Group 148: Major Small & Large Bowel Procs P age 2 Average Length of Stay in Days 20.00 15.00 10.00 5.00 0.00 m 3.2 82 COI-1 □ You COI-2 □ D ept CO I-3 C O M □ A ll HCFA Maximum Length of Stay: 10.0 Days C ases Below/Equal to HCFA Max: 83% (You) 76% (peers) COI-1 COI-2 COI-3 COI-4 National Average LOS 7.01 8.78 12.73 19.73 Most Frequent Diagnoses ICD-9 DescriDtion C ases 56211 Diverticuli Colon no Hem 11 56081 Intestinal Adhes w Obstr 3 5570 Acute Vase Insuff Intestine 2 1534 Mai Neo Cecum 8 1536 Mai Neo Ascend Colon 5 1540 Mai Neo Rectosigmoid Jet 2 55221 Obstr Incisional Hernia 1 1533 Mai Neo Sigmoid Colon 5 1541 Mai Neo Rectum 3 V552 Atten To Ileostomy 2 I ICD-9 DescriDtion C ases 4573 Right Hemicolectomy 19 4562 Part Sm Bowel Resect NEC 10 4576 Sigmoidectomy 13 4575 Left Hemicolectomy 5 4572 Cecectomy 5 8965 ABG 1 4863 Anterior R e d R e sed NEC 4 4579 Part Lg Bowel Excis NEC 3 4652 Lg Bowel Stoma Closure 1 4574 Transverse Colon Resect 2 Avg Ancillary Charges by Complexity 7000 6000 5000 4000 3000 2000 1000 Resp Rx Lab R ad PM 12000 10000 8 0 0 0 6 0 0 0 4 0 0 0 2000 12000 10000 8000 6000 4000 2000 30000 25000 20000 15000 10000 5000 □ You □ D ept □ All VO O Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission.

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Asset Metadata

Creator Greenia, Earl Glendon (author)

Core Title Physician profiling and clinical pathways: Combining the tools to change physician resource utilization

Degree Doctor of Philosophy

Degree Program Public Administration

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

Tag health sciences, health care management,OAI-PMH Harvest,Political Science, public administration

Language English

Contributor Digitized by ProQuest (provenance)

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c16-569717

Unique identifier UC11335899

Identifier 3155412.pdf (filename),usctheses-c16-569717 (legacy record id)

Legacy Identifier 3155412.pdf

Dmrecord 569717

Document Type Dissertation

Rights Greenia, Earl Glendon

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the au...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus, Los Angeles, California 90089, USA