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Data management system assessment: a global surgical aid organization case study

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Data management system assessment: a global surgical aid organization case study

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DATA
MANAGEMENT
SYSTEM
ASSESMENT:

A
GLOBAL
SURGICAL
AID
ORGANIZATION
CASE
STUDY

by

Timothy
Justin
Gillenwater,
Jr.

A
Thesis
Presented
to
the

FACULTY
OF
THE
USC
GRADUATE
SCHOOL

UNIVERSITY
OF
SOUTHERN
CALIFORNIA

In
Partial
Fulfillment
of
the

Requirements
for
the
Degree

MASTER
OF
SCIENCE

(CLINICAL
AND
BIOMEDICAL
INVESTIGATIONS)

May
2012

Copyright
2012

Timothy
Justin
Gillenwater,
Jr.

ii

Table
of
Contents

List
of
Tables
..............................................................................................................................................
iii

List
of
Figures
.............................................................................................................................................
iv

Abstract
.......................................................................................................................................................
vii

Chapter
1.
Introduction
......................................................................................................................
1

Section
1.01
Background
................................................................................................................
1

Section
1.02
Understanding
the
Need
......................................................................................
5

Section
1.03
Setting
and
Purpose
...............................................................................................
7

Chapter
2.
Operation
Smile
...............................................................................................................
9

Section
2.01
Scope,
Budget,
Environment,
and
Workforce
.............................................
9

Section
2.02
Flow
of
information
.............................................................................................
11

Chapter
3.
Identifying
problems
.................................................................................................
19

Section
3.01
Current
methods
of
electronic
data
management
.................................
19

Section
3.02
Limitations
of
data
management
methods
................................................
22

Section
3.03
Summary
..................................................................................................................
26

Chapter
4.
Addressing
needs
-‐
A
proposed
solution
..........................................................
28

Section
4.01
Software
...................................................................................................................
30

Section
4.02
Hardware
.................................................................................................................
35

Section
4.03
Networking
.............................................................................................................
37

Section
4.04
Summary
..................................................................................................................
38

Chapter
5.
Conclusion
–
Beyond
Operation
Smile
...............................................................
40

Section
5.01
Future
research
and
directions
......................................................................
42

Bibliography
..............................................................................................................................................
45

Appendix
A
–
Surgical
Outcomes
Research
in
Guwahati,
India
..........................................
53

Appendix
B
–
Operation
Smile
Data
Management
Limitations
...........................................
58

Appendix
C
–
Electronic
Health
Records
and
Mobile
Health
Technology
......................
66

Appendix
D
–
The
Operation
Smile
Medical
Record
(OSMR)
...............................................
71

iii

List
of
Tables

Table
1:
Limitations
to
current
data
management
methods
................................................
23

Table
2:
Characteristics
of
Study
Population
..............................................................................
55

Table
3:
Study
Results
...........................................................................................................................
56

iv

List
of
Figures

Figure
1:
Flow
of
care
in
a
surgical
mission
................................................................................
11

Figure
2:
Flow
of
information
during
patient
intake
...............................................................
12

Figure
3:
Flow
of
information
during
patient
screening
........................................................
14

Figure
4:
Flow
of
information
during
surgery
............................................................................
15

Figure
5:
Flow
of
information
during
postoperative
period
................................................
17

Figure
6:
Flow
of
information
during
patient
follow-‐up
........................................................
18

Figure
7:
Description
of
Operation
Smile
Electronic
Data
Management
System
........
21

Figure
8:
Portrait
and
landscape
modes
of
proposed
solution
that
demonstrate

look
and
feel
of
patient
chart
.............................................................................................................
29

Figure
9:
Paper
forms
become
electronic
.....................................................................................
30

Figure
10:
Electronic
forms
are
restructured
and
optimized
without
spatial

constraints
........................................................................................................................................
31

Figure
11:
The
networking
strategy
of
the
proposed
solution
...........................................
38

Figure
12:
Important
patient
health
information
is
recorded
in
pen
on
the

front
of
the
chart
......................................................................................................................................
59

Figure
13:
Form
ambiguity.
A
blank
value
for
B.
and
C.
is
unclear.
...................................
60

Figure
14:
Free
text
entry
outside
of
forms
.................................................................................
61

Figure
15:
Illegible
handwriting
.......................................................................................................
61

Figure
16:
OSMR
Form
1,
Page
1
......................................................................................................
71

Figure
17:
OSMR
Form
1,
Page
2
......................................................................................................
72

Figure
18:
OSMR
Form
1,
Page
3
......................................................................................................
73

Figure
19:
OSMR
Form
1,
Page
4
......................................................................................................
74

Figure
20:
OSMR
Form
2
......................................................................................................................
75

v

Figure
21:
OSMR
Form
3,
Page
1
......................................................................................................
76

Figure
22:
OSMR
Form
3,
Page
2
......................................................................................................
77

Figure
23:
OSMR
Form
3,
Page
3
......................................................................................................
78

Figure
24:
OSMR
Form
4
......................................................................................................................
79

Figure
25:
OSMR
Form
5
......................................................................................................................
80

Figure
26:
OSMR
Form
6,
Page
1
......................................................................................................
81

Figure
27:
OSMR
Form
6,
Page
2
......................................................................................................
82

Figure
28:
OSMR
Form
6,
Page
3
......................................................................................................
83

Figure
29:
OSMR
Form
6,
Page
4
......................................................................................................
84

Figure
30:
OSMR
Form
6,
Page
5
......................................................................................................
85

Figure
31:
OSMR
Form
6,
Page
6
......................................................................................................
86

Figure
32:
OSMR
Form
6,
Page
7
......................................................................................................
87

Figure
33:
OSMR
Form
7
......................................................................................................................
88

Figure
34:
OSMR
Form
8
......................................................................................................................
89

Figure
35:
OSMR
Form
9,
Page
1
......................................................................................................
90

Figure
36:
OSMR
Form
9,
Page
2
......................................................................................................
91

Figure
37:
OSMR
Form
10
...................................................................................................................
92

Figure
38:
OSMR
Form
11,
Page
1
...................................................................................................
93

Figure
39:
OSMR
Form
11,
Page
2
...................................................................................................
94

Figure
40:
OSMR
Form
12
...................................................................................................................
95

Figure
41:
OSMR
Form
13
...................................................................................................................
96

vi

Figure
42:
OSMR
Form
14
...................................................................................................................
97

Figure
43:
OSMR
Form
15
...................................................................................................................
98

vii

Abstract

While
international
surgical
humanitarian
organizations
can
reduce
the
global

burden
of
surgical
disease
and
improve
healthcare
infrastructure
in
developing

countries,
the
overall
impact
on
the
lives
of
those
treated
is
difficult
to
quantify.

Given
the
challenging
environments
in
which
they
operate,
these
aid
groups
face

substantial
problems
associated
with
providing
services,
collecting
and
maintaining

patient
health
information
as
well
as
tracking
indicators
of
quality
and
longer-‐term

outcomes.

Surgical
charities
suffer
from
lacking
transparency
and
accountability:
a

direct
result
of
not
recording
and
openly
reporting
on
their
practices
and
results.

Although
their
use
and
potential
strengths
are
well
established,
electronic
health

records
have
not
been
applied
in
the
field
during
surgical
missions.

Recent

innovations
in
mobile
health
software
solutions,
real-‐time
connectivity,
and

portability
of
hardware
have
opened
the
door
to
implementing
electronic
health

records
in
challenging
environments.

Such
technological
advances
can
be
leveraged

to
solve
the
unmet
data
management
needs
of
humanitarian
organizations,
thus

improving
on-‐site
patient
tracking
and
operational
efficiency
as
well
as
patient

outcome
and
quality
control
analysis.

Electronic
data
collection
and
distribution

from
these
resource-‐limited
settings
may
play
a
significant
factor
in
advancing
the

establishment
of
international
surgical
practice
guidelines.

viii

Operation
Smile
is
a
global
surgical
charity
dedicated
to
the
treatment
of
congenital

facial
deformities.
To
audit
outcomes
and
improve
quality
of
care,
the
organization

translates
information
from
existing
pen-‐and-‐paper
medical
records
to
a
basic

system
of
electronic
databases.
Yet,
significant
challenges
to
research
and
reporting

persist
due
to
a
lack
of
state-‐of-‐the-‐art
data
management
strategies.

This
thesis
will
elucidate
the
design
and
development
of
an
electronic
data

management
application
that
collects,
processes,
and
distributes
information.

The

design
of
the
system
is
based
on
first-‐hand
experience
with
Operation
Smile

missions
and
an
attempt
to
use
mission
data
for
outcomes
assessment.
The

proposed
solution
will
be
an
invaluable
resource
for
real-‐time
mission-‐based

patient
management
as
well
as
a
constantly
updated
investigational
database
for
the

study
of
disease
etiology
and
surgical
outcomes
in
cleft
care
in
the
developing
world.

1

Chapter
1. Introduction

Section
1.01 Background

Access
to
essential
surgical
care
has
been
proposed
as
a
core
component
of
the
basic

human
right
to
health.(McQueen,
Ozgediz,
Riviello,
Hsia,
Jayaraman,
Sullivan,
&

Meara,
2010b)
The
World
Health
Organization
(WHO)
estimates
that
11
percent
of

the
global
burden
of
disease
is
amenable
to
treatment
or
prevention
by

surgery.(Debas
H,
2006)
This
proportion,
defined
as
the
global
burden
of
surgical

disease,
is
projected
to
increase
as
the
worldwide
number
of
people
suffering
from

traumatic
injury
and
non-‐communicable
diseases
grows.
(Daar
et
al.,
2007;
Hofman,

Primack,
Keusch,
&
Hrynkow,
2005)

Paul
Farmer,
M.D.,
a
global
health
leader,
has
described
surgery
as
“the
neglected

stepchild
of
global
health,”
and
the
facts
substantiate
that
claim.(Farmer
&
Kim,

2008)
Today,
there
are
over
two
billion
people
without
access
to
surgical

care.(Ozgediz,
Jamison,
Cherian,
&
McQueen,
2008)
The
poorest
one-‐third
of
the

world’s
population
receives
only
3.5
percent
of
the
surgical
procedures

worldwide.(Weiser
et
al.,
2008)
Despite
the
evidence
for
the
lack
of
surgical
capacity

in
the
developing
world,
there
are
no
large
philanthropic
foundations
that
list
the

global
provision
of
surgery
as
a
priority.(Bae,
Groen,
&
Kushner,
2011)

2

The
role
of
surgery
in
global
health
is
becoming
increasingly
acknowledged
within

the
last
decade.

The
WHO
recently
established
the
Emergency
and
Essential

Surgical
Care
program
with
the
goal
of
“providing
life-‐saving
surgical
care
to
meet

the
need
in
areas
of
the
world
where
the
burden
is
high,
access
is
low,
and
the

disparity
is
great.”(“About
Surgery,”
n.d.)
As
a
part
of
this
initiative
the
WHO

introduced
the
“Safe
Surgery
Saves
Lives”
Global
Patient
Safety
Checklist
and

provided
an
education
and
resource
manual
for
hospitals
in
developing
countries.

(“Surgical
Care
at
the
District
Hospital
-‐
The
WHO
Manual,”
n.d.;
“Safe
Surgery
Saves

Lives,”
n.d.)
Similarly,
in
2006
World
Bank’s
Disease
Control
Priorities
Project

included
surgical
disease
within
its
list
of
priorities
for
the
first
time.(“DCPP
-‐

Disease
Control
Priorities
in
Developing
Countries
(2nd
Edition),”
n.d.)
Following

this
trend,
the
American
College
of
Surgeons
addressed
the
need
for
a
plan
to

develop
surgical
services
in
impoverished
countries,
and
then
advocated
for
its

member
surgeons
to
direct
research
activities
toward
this
end.(Duba
&
Hill,
2007)

These
progressive
initiatives
are
beginning
to
bolster
the
efforts
of
international

surgical
humanitarian
organizations
(ISHOs)
that
have
delivered
surgical
care
to

people
in
resource
poor
environments
for
decades.(Hughes,
Alkire,
Martin,
Semer,
&

Meara,
2011)
While
medical
mission
work
can
be
traced
back
to
secular
and

spiritual
roots,
the
modern
ISHO
did
not
appear
until
the
1950s
with
the
African

Medical
and
Research
Foundation.
(“AMREF
|
Our
History,”
n.d.;
Sherman
&
Magee,

2008)
Recognizing
the
need
for
surgical
care
in
a
population
lacking
both
medical

facilities
and
surgeons,
these
pioneers
solved
the
problem
by
transporting
the
gear

3

and
the
expertise
to
the
people.
For
the
first
time,
the
medical
knowledge
of
how
to

perform
safe,
effective
anesthesia
and
surgery
intersected
with
the
logistical

capacity
to
mobilize
and
deploy
a
team
of
trained
medical
volunteers
and
surgical

equipment.
Thus
was
born
the
modern
surgical
mission.

There
is
abundant
evidence
that
populations
in
developing
countries
do
not
have

the
existing
infrastructure
or
personnel
to
provide
even
the
most
basic
surgical

services.(Wasunna,
1987)
Given
this
reality,
many
organizations
embrace
the

concept
of
bringing
surgical
equipment
and
personnel
to
underserved

environments.
Catalogs
kept
by
the
American
College
of
Surgeons
/
Operation

Giving
Back,
the
U.S.
State
Department,
MissionFinder.org,
and
The
International

Healthcare
Opportunities
Clearinghouse
list
hundreds
of
ISHOs
that
provide

mission-‐based
care.
(“Lamar
Soutter
Library
-‐
University
of
Massachusetts
Medical

School,”
n.d.;
“Mission
Finder:
Classified
Directories
of
Christian
Missions

Opportunities:
Search
Engine
and
Extensive
Listings,”
n.d.;
“Operation
Giving
Back:

Surgical
Volunteer
Opportunities
and
Global
Health
Resources,”
n.d.;
“U.S.
PVO

Registry,”
n.d.)
Collectively,
these
organizations
have
been
estimated
to
conduct

over
6,000
missions
per
year,
with
annual
expenditures
in
excess
of
$250

million.(Maki,
Qualls,
White,
Kleefield,
&
Crone,
2008)
A
limited
survey-‐based
study

revealed
that
surgical
charities
carry
out
more
than
220,000
surgeries
each
year
–

the
actual
number
of
operations
performed
worldwide
is
likely
to
be
substantially

higher.(McQueen,
Hyder,
Taira,
Semer,
Burkle,
&
Casey,
2010a)

4

Despite
the
modern
proliferation
of
ISHOs,
the
idea
of
mission-‐based
surgery
is
not

without
problems.
There
are
no
international
quality
standards
like
those
in
the

United
States
and
other
developed
countries,
though
there
are
initiatives
towards

achieving
this
end.
(Chu,
Trelles,
&
Ford,
2011;
Maki
et
al.,
2008;
Schneider
et
al.,

2011)
Patients
are
exposed
to
potential
risks
from
complex
surgical
procedures,
and

poor
surgical
outcomes
add
to
the
burden
of
disease.
Short
and
long-‐term
follow
up

is
necessary
to
evaluate
and
establish
the
benefits
of
surgical
intervention
on
patient

health.
In
the
developed
world,
adverse
surgical
outcomes
such
as
mortality,

infection,
and
complications
are
fundamental
surgical
quality
control
indicators.(J.

D.
Birkmeyer,
Dimick,
&
Birkmeyer,
2004)

Surgical
aid
groups
can
differ
substantially
in
their
procedures
and
practices,

including
internal
quality
assessment,
making
it
hard
to
compare
quality
standards

across
the
board.
These
organizations
also
suffer
from
a
seeming
lack
of

transparency
that
results
from
undocumented
surgical
outcomes.
(Abelson
&

Rosenthal,
1999;
Kettle,
1999;
Nthumba,
2010;
Welling,
Ryan,
Burris,
&
Rich,
2010)

In
the
absence
of
standardized,
high-‐quality
data,
ISHOs
cannot
demonstrate
if
they

are
adhering
to
the
same
standards
of
care
abroad
that
they
would
in
their
home

countries.
As
such,
the
true
impact
of
these
organizations
on
the
health
and
well

being
of
patients
remains
undocumented.

5

Section
1.02 Understanding
the
Need

The
need
for
surgical
charities
to
transparently
document
quality
measures,
track

and
follow
up
on
patients,
and
analyze
surgical
outcomes
data
is
well
recognized.

(“Summit
Proceedings
and
Policy
Compendium,”
2009;
McQueen
et
al.,
2009;

Ozgediz
et
al.,
2008)
Effective
collection
of
quality
data
by
these
organizations

during
missions
is
an
essential
first
step
towards
meeting
this
need.
Internally,

comprehensive
and
accurate
collection
and
analysis
of
information
surrounding
the

surgical
mission
can
lead
to
improvements
in
resource
allocation,
mission
efficiency,

and
surgical
outcomes.
On
a
global
level,
sharing
of
mission
data
could
advance

understanding
of
surgical
disease
epidemiology
and
targeting
of
treatment

strategies.

The
comprehensive
collection
of
data
that
is
fit
for
statistical
analysis
is
a

challenging
task.
Historically,
handwritten
notes
in
paper
charts
have
been
used

during
the
surgical
mission.

Paper
charts
have
the
advantages
of
being
durable,
low

tech
and
familiar.

However,
paper-‐based
medical
record
keeping
does
not
provide

data
that
is
easily
accessible
or
useful
after
the
mission
is
complete.
Data
stored
in

paper
charts
is
locked
in
hard-‐copy
form
and
cannot
easily
be
searched,
sorted,
or

filtered.
Though
data
is
collected,
it
is
difficult
to
analyze
after
the
mission
and
not

suitable
for
research
and
reporting
purposes.

Electronic
health
records
(EHRs)
and
breakthroughs
in
information
technology,

however,
have
led
to
improvements
in
collection
of
data
on
health
quality

indicators.
(Bates
&
Gawande,
2003;
Rogoski,
2004)
Electronic
data
collection

6

improves
data
quality
and
facilitates
data
analysis
and
sharing.(Menachemi
&

Collum,
2011)
Furthermore,
EHRs
and
electronic
data
collection
tools
have
been

used
effectively
by
humanitarian
organizations,
such
as
Partners
in
Health
in
Haiti,

that
function
on
a
more
permanent
basis
in
developing
countries.(“Medical

Informatics
|
Partners
In
Health,”
n.d.)
In
these
situations,
the
use
of
an
electronic

health
record
or
data
collection
tool
has
improved
rates
of
patient
tracking
and

follow
up,
decreased
medication
and
laboratory
errors,
and
positively
impacted
the

collection
and
study
of
outcomes
data.(Blaya,
Fraser,
&
Holt,
2010)
Despite
these

clear
advantages,
ISHOs
are
not
using
information
technology
in
the
field,
resulting

in
a
lost
opportunity
to
understand
and
improve
upon
humanitarian
surgical
care
in

the
developing
world.

Recent
innovations
in
mobile
health
solutions,
hardware
durability
and
portability,

and
networking
capabilities
enable
extension
of
EHRs
and
information
technology

to
surgical
missions.
Leveraging
these
applications
at
the
point
of
care
could
result

in
enhancements
in
data
collection,
analysis
and
sharing.
By
piggybacking
on
the

latest
mobile
health
solutions,
ISHOs
are
in
position
to
leapfrog
out-‐of-‐date

technology
and
arrive
at
the
forefront
of
information
management
in
global
health.

As
data
collection
improves
and
research
yields
results,
the
humanitarian
surgical

community
could
incorporate
new
data-‐driven
policies
and
guidelines
into
the

standards
of
care
in
the
field
and
contribute
to
the
overall
fund
of
knowledge
about

global
burden
of
surgical
disease.

7

Section
1.03 Setting
and
Purpose

Operation
Smile
is
a
large
ISHO
dedicated
to
the
comprehensive
care
of
cleft
lip
and

palate.(“Operation
Smile,”
n.d.)
While
Operation
Smile
delivers
care
primarily

through
international
surgical
missions,
the
organization
has
been
moving
to

establish
permanent
centers
of
care
in
high-‐need
areas
around
the
world.
Operation

Smile
recognized
a
lack
of
access
to
cleft
care
in
the
rural
Indian
State
of
Assam,

where
there
were
an
estimated
26,000
cases
of
untreated
orofacial
clefts.(Varma,

n.d.)
In
2010
the
organization
began
construction
on
a
new
center
in
the
state

capitol,
Guwahati,
to
facilitate
cleft
care
in
this
region.

Just
prior
to
the
completion
of
the
center
in
May
2011,
Operation
Smile
conducted

two
large
surgical
missions
at
Mohendra
Mohan
Choudury
Hospital
in
Guwahati,
the

host
facility
where
the
center
was
being
constructed.
During
these
surgical
missions,

a
large
volume
of
cleft
surgeries
was
carried
out
in
a
short
amount
of
time
–
654

cleft
lip
repairs
in
two
months.

Operation
Smile
collects
a
wealth
of
data
regarding
surgical
processes
in
paper

charts
as
a
routine
part
of
surgical
mission
medical
record
keeping.
The
surgical

mission
data
collected
during
the
missions
in
Guwahati
was
used
to
study
the

associations
among
processes
of
care
and
surgical
outcomes
in
this
setting.
The

results
of
this
effort
are
attached
in
Appendix
I.

8

However,
the
methods
of
data
management
currently
used
by
Operation
Smile
have

limitations
that
can
be
improved
to
yield
higher-‐quality
results.
This
thesis
draws
on

observations
that
were
made
when
conducting
the
above
study.

This
body
of
this
thesis
is
structured
in
three
chapters,
with
three
purposes:

• In
Chapter
2,
the
scope
and
structure
of
Operation
Smile
and
the
flow
of

information
in
a
typical
surgical
mission
are
described.

• In
Chapter
3,
the
current
electronic
data
management
strategies
Operation

Smile
uses
for
research
and
reporting
are
described
and
their
limitations
are

discussed.

• In
Chapter
4,
a
novel
electronic
solution
is
elaborated
that
would
address
the

data
management
requirements
of
Operation
Smile.
This
solution
would

generate
information
that
has
utility
both
during
the
mission
and
afterwards

for
research
and
reporting.

9

Chapter
2. Operation
Smile

This
Chapter
summarizes
the
structure
of
Operation
Smile
and
outlines
the

processes
of
data
collection
that
the
organization
has
adopted.
Section
2.01
focuses

on
the
scope,
budget,
environment,
and
workforce
of
Operation
Smile.
Section
2.02

illustrates
the
overall
flow
of
information
that
occurs
during
a
surgical
mission.

Section
2.01 Scope,
Budget,
Environment,
and
Workforce

Operation
Smile
Inc.
is
the
largest
volunteer-‐based
cleft
care
provider
in
the

developing
world.(“2011
Annual
Report,”
n.d.)
In
2011,
the
organization
conducted

164
surgical
missions
in
80
countries.
Operation
Smile
also
staffs
and
helps
to
fund

13
comprehensive
cleft
care
centers
in
nine
countries
that
provide
full-‐time
surgical

and
multidisciplinary
services.
Last
year,
the
organization
conducted
over
330,000

healthcare
evaluations,
18,000
surgeries,
and
17,000
postoperative
evaluations.
The

majority
(80%)
of
surgeries
occur
during
the
surgical
missions,
while
the
remainder

(20%)
occurs
at
comprehensive
care
centers.

The
revenue
of
Operation
Smile
is
chiefly
derived
from
donations,
of
both
money

and
contributed
services.(“Combined
Financial
Statements
and
Schedules,”
2011)
In

2011,
the
total
revenue
of
the
organization
was
$73,108,204.
The
vast
majority
of

this
revenue
-‐
$49,991,861
-‐
was
spent
on
program
services
including
medical

missions,
education,
and
sustainability.
In
2011,
Operation
Smile
spent
nearly

XXXXX
on
printing,
shipping,
and
warehousing
its
paper
medical
records.

10

Operation
Smile
has
defined
the
mission
environment
by
setting
out
minimum

infrastructure
requirements
that
facilities
must
meet
in
order
to
host
the
surgical

mission.
Examples
of
such
include
a
constant
supply
of
electricity
and
availability
of

laboratory
and
intensive
care
facilities.
Internet
or
wireless
data
networks
are
not

required
or
consistently
available.

The
Operation
Smile
healthcare
workforce
consists
almost
exclusively
of
short-‐term

volunteers
who
donate
their
time
and
expertise
for
the
duration
of
one
surgical

mission,
which
is
generally
a
week.
Roles
include:
surgeons,
anesthesiologists,

pediatricians,
speech
therapists,
dentists,
nurses,
child
life
specialists,

photographers,
researchers,
administrators,
and
others.
The
workforce
is
drawn

from
volunteers
in
both
the
host
country
and
international
centers.
In
2011,
the

organization
had
more
than
5,000
volunteers
from
80
countries.
While
some

volunteers
are
relatively
new
to
the
mission
environment,
others
are
veterans
that

have
participated
in
dozens
of
missions.
A
notable
and
growing
component
of
the

Operation
Smile
healthcare
staff
work
on
long-‐term
contracts
at
the
comprehensive

care
centers.

11

Section
2.02 Flow
of
information

On
every
surgical
mission,
five
processes
of
care
are
necessary
to
provide
a
safe

surgical
experience:
Patient
Intake,
Screening,
Surgery,
Post
Operative,
and
Follow

Up.
(See
Figure
1)

Figure
1:
Flow
of
care
in
a
surgical
mission

As
the
patient
transitions
through
these
processes
of
care,
there
is
an
accompanying

flow
of
information.
In
Operation
Smile,
all
documentation,
with
the
exception
of

photographs,
is
handwritten
in
a
paper
chart
that
is
unique
to
each
patient.

Standardized
photographs
are
obtained
by
Patient
Imaging
Technicians
(PITs),

uploaded
to
a
Filemaker
Database,
and
tagged
with
the
patient’s
chart
number.
The

flow
of
information
is
broken
down
as
follows:

(a) Patient
Intake

The
patient
recruitment
process
begins
with
a
non-‐health
care
volunteer

administering
a
4-‐page
questionnaire
in
the
patient’s
native
language.
A
variety
of

demographic
and
epidemiologic
data
is
collected.
Included
in
this
questionnaire
is

information
about
the
recruitment
process
and
how
the
patient
came
to
be
at
the

Operation
Smile
mission.
Informed
consent
is
signed.
Standardized
photos
are
taken

Intake Screening Surgery Follow Up Post-Op

12

by
PITs.
Patients
are
then
moved
on
to
patient
screening.
(Appendix
IV:
Operation

Smile
Medical
Records
Form
1
and
2).
(See
Figure
2)

Figure
2:
Flow
of
information
during
patient
intake

Patient Flow
Patient Established Epidemiology
Volunteer
Patient Demographic
Data Recorded
Form 1
Photo / Consent
Volunteer
Patient Epidemiology
Data Recorded
Form 1
Patient Photo
Taken
Intake
Patient
Imaging
Technician
Consent
Volunteer
Form 2

13

(b) Patient
Screening

Nurses
take
vital
signs
and
labs.
(See
Appendix
IV:
Operation
Smile
Medical
Records

Form
1).
The
surgeon
evaluates
the
patient,
prioritizes
them
according
to
Operation

Smile
criteria,
and
details
the
type
of
cleft
and
proposed
operative
plan.
(See

Appendix
IV:
Operation
Smile
Medical
Records
Form
1
and
3).

Pediatricians
and

anesthesiologists
perform
a
focused
review
of
systems
and
a
physical
exam.
(See

Appendix
IV:
Operation
Smile
Medical
Records
Form
1).
They
then
indicate
whether

the
patient
is
a
healthy
candidate
for
surgery.
Next,
a
speech
therapist
evaluates

every
patient
and
gives
recommendations
for
the
potential
benefit
of
surgery
on

speech.
(See
Appendix
IV:
Operation
Smile
Medical
Records
Form
1
and
4).

A

dentist
evaluates
every
patient
and
gives
recommendations
regarding
dental

interventions.
(See
Appendix
IV:
Operation
Smile
Medical
Records
Form
1
and
14).

At
the
end
of
the
patient
screening
process,
the
mission
team
leaders
decide
if
the

patient
will
receive
surgery
during
the
mission.
If
the
patient
is
selected
for
surgery,

an
appointment
is
scheduled
and
the
patient
returns
for
admission
on
the
night

before
surgery.
(See
figure
3)

14

Figure
3:
Flow
of
information
during
patient
screening

(c) Surgery

Patients
are
admitted
on
the
day
before
surgery.
Vital
signs
are
collected
and
the

patients
are
kept
without
oral
intake
prior
to
surgery.
A
surgeon
or
pediatrician

completes
preoperative
orders.
(See
Appendix
IV:
Operation
Smile
Medical
Records

Form
11).
Immediately
prior
to
start
of
surgery,
an
operating
room
nurse
completes

the
WHO
surgical
safety
checklist.
(See
Appendix
IV:
Operation
Smile
Medical

Records
Form
5).

During
surgery,
anesthesiologists
document
operative
time
and

details
of
anesthesia
including
medication
administration.
(See
Appendix
IV:

Screening
Patient Flow
Screening Candidate Evaluation Schedule
Physical
Exam
Plastics
Eval
Pediatric
Eval
Anesthesia
Eval
Speech
Eval
Dental
Eval
Schedule
Surgery
Triage
Complete
Pediatrician
or
Anesthesiologist
Surgeon
Anesthesiologist
Pediatrician
Speech
Pathologist
Dentist
Yes
No
Will Patient Receive
Surgery This Mission?
Form
1
Form
3
Form
1
Form
1
Form
4
Form
14
Form 1
Clinical Care Team

15

Operation
Smile
Medical
Records
Form
7).
Immediately
postoperatively,
surgeons

collect
data
on
surgical
technique
and
method
of
wound
closure
and
complete

postoperative
orders.
(See
Appendix
IV:
Operation
Smile
Medical
Records
Form

6,11).
The
PITs
obtain
standardized
on-‐table
photographs
preoperatively
and

postoperatively.
(Figure
4)

Figure
4:
Flow
of
information
during
surgery

Surgery
Admitting
Immediately
Post Operative
Vitals
Collected
Nurse
Preoperative
Orders
Surgeon
or
Pediatrician
Form
11
Form
11
Operating Room Nurse
Surgeon
Patient Kept
Overnight
WHO
Surgical Safety
Checklist
Pre-Operative
Anesthesiologist
Patient
Imaging
Technician
On Table
Photographs
Patient
Imaging
Technician
On Table
Photographs
Operative Time
Anesthesia Details
Surgical
Data
Form
5
Form
6
Form
7
Ph
oto
Ph
oto
Ph
oto
Ph
oto
Patient Flow
Patient
Transferred to
Operating Room
Patient
Surgery
Patient
Transported
to Recovery
Room

16

(d) Post
Operative

After
surgery,
patients
are
transported
to
a
recovery
room
where
they
are
closely

monitored
in
the
immediate
postoperative
period.
Postsurgical
complications
are

more
common
during
this
time,
so
individual
nurses
are
assigned
to
monitor
to
each

patient
until
that
patient
is
stable
for
transfer
to
the
hospital
floor.
Recovery
nurses

document
vital
signs,
patient
status
indicators
such
as
intake
and
output,
and

medications
given
in
the
recovery
room.
(See
Appendix
IV:
Operation
Smile
Medical

Records
Form
8,
10).

After
patients
meet
transfer
criteria,
they
are
transferred
to
the
hospital
floor,

where
individual
ward
nurses
monitor
large
groups
of
patients.
Floor
nurses

document
vital
signs,
patient
status
indicators,
and
medications
given
on
the

hospital
floor.
(See
Appendix
IV:
Operation
Smile
Medical
Records
Form
9,10).

17

Pediatrician
and
surgeons
then
determine
when
a
patient
is
stable
enough
to
leave

the
hospital.
At
this
time,
physicians
document
discharge
orders
for
each
patient,

including
medications,
wound
care
directions,
and
diet.
A
free-‐text
form
is
provided

for
any
additional
notes
(See
Appendix
IV:
Operation
Smile
Medical
Records
Form

12,13).
Discharge
instructions
are
explained
to
the
patients
–
with
the
help
of

translators
if
needed
–
and
then
nurses
document
that
appropriate
discharge

instructions
and
medications
were
provided.
(See
Appendix
IV:
Operation
Smile

Medical
Records
Form
9)
(See
Figure
5)

Figure
5:
Flow
of
information
during
postoperative
period

Post-Op
Recovery Room Discharge Hospital Floor
Patient
Status
Recovery
Nurse
Form
8
Vital Signs,
Status,
Medications
Nurse
Form
Form
Form
9
Discharge
Pediatrician
or
Surgeon
Form
12
Patient Flow
Transferred to
Hospital Floor

18

(e) Follow
up

Follow
up
documentation
is
performed
by
a
plastic
surgeon
at
one
week,
six
months,

and
one
year
postoperatively.
Information
is
collected
regarding
the
presence
or

absence
of
a
complication.
Complications
are
described
if
present,
and
photographs

are
obtained.
(See
Appendix
IV:
Operation
Smile
Medical
Records
Form
15).
(Figure

6)

Figure
6:
Flow
of
information
during
patient
follow-‐up

Follow-Up
One Week Post Surgery
Surgeon
Form
15
Follow Up
Patient
Imaging
Technician
Patient
Photographs
Ph
oto
Ph
oto
Patient Flow
1 Year Post Surgery 6 Months Post Surgery
Surgeon
Form
15
Follow Up
Patient
Imaging
Technician
Patient
Photographs
Ph
oto
Ph
oto
Surgeon
Form
15
Follow Up
Patient
Imaging
Technician
Patient
Photographs
Ph
oto
Ph
oto

19

Chapter
3. Identifying
problems

During
surgical
missions,
volunteers
have
the
opportunity
to
collect
a
large
amount

of
data
in
paper
charts
as
a
part
of
the
routine
mission
workflows
outlined
in
the

previous
chapter.
Operation
Smile
has
long
sought
to
demonstrate
accountability
by

better
using
mission
data
to
audit
surgical
outcomes
and
improve
quality
of

care.(Bermudez,
Carter,
Magee,
Sherman,
&
Ayala,
2009)
To
that
end,
the

organization
has
created
the
Research
and
Outcomes
Department
and
has
instituted

a
system
of
homegrown
electronic
databases
and
data
entry
methods.
As
Operation

Smile
endeavors
for
increasingly
high-‐quality
research,
however,
the
limitations
of

the
existing
approach
have
become
apparent.

This
chapter
provides
a
general
description
of
the
organization’s
electronic
data

management
methods,
and
evaluates
these
methods,
highlighting
critical
problems.

The
shortcomings
of
paper
form
design,
data
entry
methods,
and
database
design

are
discussed
as
they
pertain
to
research
and
reporting.
This
chapter
draws
on

observations
made
while
conducting
surgical
outcomes
research
with
Operation

Smile
in
Guwahati.

Section
3.01 Current
methods
of
electronic
data
management

Operation
Smile
manages
textual
data
electronically
using
Microsoft
Access

Databases
that
were
developed
in-‐house
by
clinical
researchers
without
specialty

informatics
or
database
training.
The
organization
initially
adopted
this
approach
as

a
temporizing
measure
until
a
more
permanent
electronic
solution
could
be
found.

20

The
Access
Databases
were
designed
to
facilitate
rudimentary
reporting
on
mission

statistics,
such
as
surgical
volume.
Yet,
as
Operation
Smile
endeavored
to

demonstrate
accountability
and
the
organization’s
Division
of
Research
and

Outcomes
grew,
the
homegrown
solution
was
expanded
for
use
in
auditing
surgeon

performance
and
conducting
internal
quality
assessment.
The
current
method
of

electronic
data
management
with
Access
Databases
has
now
been
in
place
for
nearly

three
years.

During
a
surgical
mission
and
using
single
data
entry,
volunteers
with
minimal

training
and
no
data
entry
credentials
enter
basic
data
from
the
forms
in
the
paper

chart
into
an
Access
Database
created
exclusively
for
a
particular
mission
and

loaded
on
a
laptop
computer.
There
are
no
standard
data
entry
protocols.
The

information
collected
usually
includes
patient
identifying
information
and

demographics,
diagnosis,
the
name
of
the
surgeon,
and
the
type
and
date
of
any

surgical
intervention.

The
Access
Databases
are
mission-‐specific;
there
is
a
new
one
created
for
every

mission.
Separate
databases
cannot
be
merged
to
create
a
master
patient
database

and
do
not
communicate
with
each
other.
There
is
no
Operation
Smile
main
data

store.
(See
Figure
7).

21

Figure
7:
Description
of
Operation
Smile
Electronic
Data
Management
System

The
Operation
Smile
Access
Databases
work
in
parallel
with
a
second,
separate

system
of
homegrown
databases.
The
Operation
Smile
FilemakerPro
Databases
are

used
for
collecting,
archiving,
and
auditing
the
photographs
taken
on
the
missions.

Again,
these
are
mission
specific
and
cannot
be
merged
across
missions
for
patients

participating
in
multiple
missions.
During
the
mission,
Patient
Imaging

Technologists
(PITs)
take
a
series
of
pre
and
postoperative
patient
photographs.

These
are
cropped,
tagged
with
patient
identifying
information,
and
entered
into
the

FilemakerPro
Database
for
each
respective
surgical
mission.

Chart
Access
Database
Chart
Chart
Chart
Access
Database
Chart
Chart
Mission 1
Mission 2
No Communication
There is no way to share data
between missions
Photo
Photo
File Maker Pro
Image Archive
Photo
Photo
File Maker Pro
Image Archive
...
...
Operation Smile
Main Data Store
(does not exist)
No Communication
No integration between same-mission data
stores

22

Section
3.02 Limitations
of
data
management
methods

Mission
data
needs
to
be
extracted
from
paper
charts,
appropriately
coded,
and

input
into
an
electronic
database
in
order
for
meaningful
statistical
analysis
to

occur.
Sound
data
collection
tools,
data
entry
methods,
and
database
design
are
the

foundations
of
quality
research
and
faults
in
these
areas
limit
comprehensive

examination
of
data.
This
section
addresses
the
flaws
to
the
paper
form
design,
the

data
entry
approach,
and
the
database
design
that
have
been
adopted
by
Operation

Smile.
(See
Table
1)
A
comprehensive
outline
with
examples
from
the
medical

record
is
included
in
Appendix
II.

23

Problem
Limitation
to
Research

Paper
Form

Design

• Lengthy
and

cumbersome

• Redundant
data

fields

• Ambiguous

• Missing
data

• Data
inconsistencies

• Unclear
data
significance

Data
Entry

Methods

• Limited
training
for

data
entry
personnel

• No
data
entry

protocols

• Single
data
entry

• Data
entry
bias

• Poor
data
validity

• No
data
quality
control

Database

Design

• No
Master
Database

• No
communication

between
databases

• Database

incompatibilities

• Database
merging
errors

• Unclean
data

• No
data
quality
control

Table
1:
Limitations
to
current
data
management
methods

24

(a) Paper
Form
design

Poorly
designed
forms
can
result
in
missing
data
fields,
inconsistent
data,
and

confusion
on
data
entry.
In
an
attempt
to
comprehensively
capture
a
large
amount

of
mission
data,
the
Operation
Smile
medical
record
forms
were
designed
to
be
as

broad
in
scope
as
possible.
As
described
in
Chapter
2
and
Appendix
IV,
the
medical

record
incorporates
free
text
entry,
fill
in
the
blank,
check
boxes,
multiple
answer

format,
technical
drawings,
and
graphs.
The
resulting
nearly
30-‐page
patient
chart
is

difficult
to
navigate.
As
a
consequence,
data
may
be
recorded
in
incorrect
locations

or
not
recorded
at
all.

Redundancies
in
data
capture
are
inefficient
and
can
result
in
contradictory
data.

There
are
multiple
instances
in
the
chart
where
the
same
data
items
are
required
to

be
recorded
more
than
once.
Ambiguities
in
form
design,
such
as
when
null
values

are
indistinguishable
from
missing
data,
can
lead
to
confusion
on
data
entry.

(b) Data
entry

Unsound
data
entry
methods
can
introduce
errors
that
reduce
data
validity
and

decrease
quality
of
research.
Data
entry
personnel
are
volunteers
with
unclear

experience
and
data
entry
credentials.
They
undergo
minimal
training
and
have
no

prior
quality
checks,
despite
being
responsible
for
entering
mission
data
into
the

Operation
Smile
Access
Database
during
a
mission.

25

Moreover,
standardized
protocols
for
data
entry
do
not
exist.
When
conflicting
data

is
present
in
the
paper
chart,
data
entry
is
dependent
upon
the
judgment
call
of
the

untrained
data
entry
volunteer.
Free
text
availability
in
data
entry
forms
results
in

inconsistencies
and
variations
in
the
data
that
is
entered.
The
databases
are
not

used
for
research
purposes
until
after
the
surgical
mission.
At
that
time,
the
paper

charts
are
unavailable
for
data
verification
purposes.

Data
collected
by
the
volunteer
data
entry
staff
is
input
into
the
Access
Database

using
single
data
entry.
Double
data
entry
has
been
the
gold
standard
for
research
as

it
allows
databases
to
be
compared
and
data
quality
control
measures
to
be

implemented.

(c) Database
design

As
detailed
above,
merging
multiple
Access
Databases
to
create
a
master
data

repository
is
not
feasible
given
current
database
design.
Conducting
research
on

data
over
multiple
missions
is
significantly
limited
as
a
result.
While
it
is
possible
to

create
a
multi-‐mission
dataset,
flat-‐file
data
tables
must
first
be
exported
from

Access
files.
Multiple,
disparate
flat-‐file
data
tables
must
then
be
reorganized
and

standardized,
a
process
which
induces
data
merger
errors.

Following
surgical
outcomes
for
a
single
patient
over
multiple
missions
is
difficult

given
the
current
system.
Patients
do
not
receive
a
unique
numerical
identifier
that

persists
from
one
mission
to
the
next,
instead
receiving
a
different
identification

26

number
with
each
subsequent
mission.
Thus,
electronically
tracking
patient

outcomes
over
time
is
reliant
on
accurate
entry
of
demographic
data,
which
may
not

always
be
the
case.

Lastly,
the
individual
Access
databases
have
internal
data
inconsistencies
that
result

from
inadequate
data
definitions
and
uncontrolled
data
value
ranges
on
data
entry.

Most
research
on
mission
data
is
performed
after
the
conclusion
of
the
surgical

mission
when
the
hardcopy
paper
chart
is
not
available
for
data
validation
and

updating
purposes.
Given
the
potential
data
entry
errors
and
the
numerous
internal

data
inconsistencies,
a
lack
of
data
quality
control
and
data
cleaning
measures
poses

a
critical
limitation
to
carrying
out
valuable
research.

Section
3.03 Summary

Operation
Smile
has
deficiencies
in
data
handling
methods
that
limit
translating

data
from
a
paper
chart
to
a
format
that
can
be
used
for
high-‐quality
research.

Improving
information
management
strategies
could
have
a
profound
effect
on
the

organization’s
accountability
and
internal
quality
assessment.
Data
entry
at
point
of

care
with
handheld
electronic
devices
offers
some
distinct
advantages
over

traditional
paper-‐based
methods
and
is
further
detailed
in
Appendix
III.

Incorporating
this
technology
into
the
surgical
mission
would
facilitate
the

collection
of
information
that
is
useful
to
volunteers
in
the
field
and
to

biostatisticians
and
researchers
at
home.
Appropriate
electronic
form
design
could

prevent
data
duplication
and
limit
missing
data
fields.
Data
quality
assurance

27

measures,
such
as
data
validation
and
automatic
check
functions,
could
be

incorporated
into
software,
thereby
limiting
data
entry
errors.
Sound
database

design
could
result
in
a
master
data
store
and
seamless
data
syncing
and

integration.

By
adopting
and
implementing
electronic
data
collection
instruments
globally,
and

with
proper
software
design
and
database
architecture,
Operation
Smile
could

create
a
comprehensive
dataset
that
reflects
international
cleft
practices
and
burden

of
disease.
Surgical
mission
data
could
be
used
for
studies
investigating
etiology
and

outcomes
in
underserved
populations
around
the
world,
where
data
is
extremely

limited.
With
the
correct
tools,
Operation
Smile
could
lead
the
international
surgical

community
towards
advancing
understanding
of
global
surgical
disease

epidemiology
and
translating
high
quality
evidence
into
best-‐practice
guidelines
and

surgical
protocols.

28

Chapter
4. Addressing
needs
-‐
A
proposed
solution

Operation
Smile
has
made
efforts
in
the
past
to
transition
from
a
paper
chart

medical
record
to
a
field-‐based
EHR,
but
these
efforts
have
not
been
successful
for

several
reasons.
First,
due
to
the
intermittent
nature
of
volunteer
participation
and

the
generally
short
duration
of
missions,
there
has
never
been
adequate
time
to

train
volunteers
on
how
to
use
an
EHR
for
data
collection.
As
slow
or
erroneous
data

entry
could
result
in
decreased
mission
efficiency
or
medical
errors,
training

volunteers
in
EHR
use
has
never
been
worth
the
time
needed.
Second,
the
hardware

required
to
support
an
EHR
has
historically
been
too
difficult
to
transport
to

missions
and
has
required
extensive
setup
and
takedown
and
potential

troubleshooting.
Lastly,
the
EHR
solutions
that
have
been
evaluated
were
found
to

be
prohibitively
expensive
and
would
have
required
a
large
up
front
cost
for
the

software
and
the
hardware.

Potential
solutions
are
available
to
address
these
limiting
factors,
except
for
the

feasibility
of
taking
time
out
of
a
mission
to
train
volunteers
on
software
use.

Technical
support
issues,
hardware
portability,
and
cost
can
be
overcome
with

reliable
software,
tablet
devices,
and
improved
fundraising.
Ease
of
software
use

remains
as
the
primary
driver
of
product
design.

This
thesis
proposes
an
intuitive
forms-‐based,
mobile
charting
software
application

that
employs
the
most
up-‐to-‐date
hardware
and
networking
strategies
to
meet
the

29

unique
needs
of
Operation
Smile.
No
such
solution
is
currently
available
in
the

market.
The
specific
aim
of
this
software
program
is
to
electronically
replicate
the

look
and
feel
of
the
existing
Operation
Smile
patient
chart
on
a
tablet
device.
(See

figure
8)

Figure
8:
Portrait
and
landscape
modes
of
proposed
solution
that
demonstrate
look

and
feel
of
patient
chart

The
application
should
be
usable
by
someone
who
has
little
to
no
computer
training.

The
end
result
proposed
is
a
mobile
application
for
wireless
collection,
storage,
and

retrieval
of
patient
information
that
optimizes
data
for
use
both
in
the
mission
and

for
research
and
reporting
purposes.
The
solution
is
described
below
in
terms
of

software,
hardware,
and
networking.

30

Section
4.01 Software

While
specifics
of
software
design
are
beyond
the
scope
of
this
thesis,
this
section

will
describe
the
fundamental
concepts
of
the
software
application,
with
a
focus
on

usability.

Electronic
forms
need
to
be
established
into
the
software
that
have
a
one
to
one

relationship
to
the
paper
forms
that
are
currently
used
by
Operation
Smile

today.

The
use
of
display
formats
that
mimics
their
real-‐life
counterparts
will
breed

a
familiarity
with
the
software
so
that
the
end
user
will
already
have
an

understanding
of
the
data
entry
process.
(See
figure
9)

Figure
9:
Paper
forms
become
electronic

31

Some
forms
are
necessarily
cluttered
in
order
to
maximize
the
amount
of
content

that
can
fit
on
an
8”
x
11”
piece
of
paper.
The
available
real
estate
on
electronic

forms
is
unlimited.
Thus
forms
can
now
be
restructured
in
an
electronic
format
in
a

more
logical
way
and
without
physical
spatial
constraints.
(See
figure
10)

Figure
10:
Electronic
forms
are
restructured
and
optimized
without
spatial

constraints

The
Graphical
User
Interface
(GUI)
will
be
modeled
after
the
successful
Apple
iOS

interface.
This
interface
has
proven
highly
successful
and
eminently
usable.
Apple

handheld
devices
have
captured
a
large
percentage
of
the
market
space,
largely
as
a

consequence
of
ease
of
device
use.
The
GUI
of
this
solution
will
follow
application

32

design
principles
set
forth
by
Apple
including:
aesthetic
integrity,
consistency,
and

user-‐centric
terminology.(“iOS
Human
Interface
Guidelines:
Introduction,”
n.d.)

The
data
that
is
captured
in
the
forms
will
be
stored
as
discrete
data
elements
in
a

back
end
database
and
will
be
mapped
to
established
clinical
standards
for
report

based
research.
Multimedia
data
elements
will
be
incorporated,
including

photographs,
audio,
video,
and
geographical
location.
Data
that
is
collected
will
be

stored
with
a
timestamp
and
data
quality
assurance
will
be
improved
by

incorporating
techniques
of
electronic
survey
methodology
into
the
electronic
forms

such
as:
radio
buttons,
Boolean
switches,
and
multiple-‐choice
selectors.
Free
text

responses
will
be
limited.

In
commercially
available
EHRs,
clinical
workflows
are
adapted
and
restructured
to

fit
the
abilities
of
the
EHR.
This
model
proposes
that
the
software
solution
employ

workflows
that
have
a
one-‐to-‐one
relationship
with
the
Operation
Smile
mission

processes
that
have
been
established
and
refined
over
years
of
experience.
In
doing

so,
the
program
should
function
to
allow
Operation
Smile
volunteers
to
collect
the

information
presented
in
the
forms
in
the
same
way
they
do
today,
only

electronically.

Operation
Smile
is
a
constantly
evolving
organization.
As
the
organization
grows

and
changes
in
scope,
so
may
its
mission
processes
and
data
collection

requirements.
For
example,
forms,
workflows,
procedures
and
processes
change.
To

33

adhere
to
this
need,
it
is
crucial
that
the
forms
and
workflows
designed
within
the

application
have
the
ability
to
grow
and
be
refined
organically
as
the
organization

and
mission
processes
evolve.

To
allow
for
this
change,
this
proposal
would
include
a
form
and
workflow
designer

along
with
the
end-‐user
clinical
charting
solution.
Administrators
and
program

coordinators
can
use
this
feature
to
customize
the
software
workflows
and
forms
to

correspond
to
the
real-‐life
processes
in
the
field.
Relative
to
the
needs
of
this

solution,
a
workflow
is
defined
as
a
set
of
one
to
many
steps.
Every
step
has
one
to

many
tasks
and
each
task
has
a
one
to
one
relationship
with
a
form.
Specified

required
tasks,
and
thus
forms,
must
be
completed
in
order
to
complete
a
step
or

complete
a
workflow.

Workflows,
when
linked
together,
should
correspond
to
the
flow
of
information
that

constitutes
the
surgical
mission.
Hence,
the
sum
total
of
workflows
will

electronically
incorporate
all
the
forms
that
comprise
the
medical
record.

Accordingly,
a
user
should
be
able
to
quickly
and
intuitively
navigate
through
the

processes
that
are
a
part
of
the
Operation
Smile
surgical
mission.

Software
usability
will
be
further
enhanced
by
assigning
a
Role
to
each
member
of

the
surgical
mission.
A
Role
provides
the
user
with
a
set
of
permissions
that

sanction
data
entry,
editing,
and
viewing.
User
Roles
also
serve
to
associate
the
user

34

with
forms
and
workflows,
as
well
as
facilitate
data
entry
when
the
user
is

automatically
guided
to
the
appropriate
form.

Patient
identification
and
tracking
will
be
improved
through
the
use
of
barcode

bracelets.
Native
device
technology,
such
as
barcode
scanners
on
the
tablet
device,

can
be
used
to
identify
patients
and
document
their
progression
through
the

surgical
mission
process.
Software
design
will
feature
alerts
or
notifications
that

indicate
when
a
patient
has
transitioned
into
a
new
area
of
care
such
as
from
the

recovery
room
to
the
nursing
floor.

Once
data
has
been
electronically
collected
by
mission
volunteers,
it
will
be

immediately
available
for
reporting
purposes.
Consequently,
limited
resources
can

be
allocated
more
efficiently
during
the
mission.
Applying
surgical
priority
patient

data
via
a
built-‐in
scheduling
program
will
streamline
the
mission
coordinator’s

ability
to
schedule
surgeries.

Additional
quality
control
features
will
be
incorporated
into
the
software
design.

These
will
include
medication-‐dosing
calculators
based
on
patient
weight.

Similarly,

the
WHO
surgical
safety
checklist
will
be
customized
for
incorporation
into
the

mission
workflow.
Postoperative
protocols
will
be
introduced
and
orders

prepopulated
from
data
collected
on
surgical
diagnosis
and
technique.

35

Section
4.02 Hardware

Hardware
requirements
are
broken
into
two
major
categories:
client
and
server.

Within
these
categories,
four
major
factors
need
to
be
considered:
storage
capacity,

transportability,
durability,
and
cost.

The
client
is
the
device
that
acts
as
the
interface
between
the
server
and
the
end

user.
This
device
will
be
used
by
the
mission
volunteer
to
enter
patient
information.

The
Apple
iPad,
a
relatively
low-‐cost
handheld
tablet
device
that
is
extremely

portable
and
easy
to
use,
is
an
ideal
client
device.
While
durability
may
be
a
concern,

there
are
aftermarket
cases
that
can
be
purchased
that
prevent
wear
and
tear
on
the

device.

The
iPad
makes
use
of
exceptional
built
in
native
device
application
that
includes

front
and
back-‐facing
cameras
capable
of
recording
high-‐definition
still

photographs,
audio,
and
video.
The
cameras
also
support
barcode
scanning
which

will
be
used
to
improve
patient
identification.

Additionally,
Geo-‐tagging
and
Global

Positioning
System
services
are
supported
in
the
presence
of
an
Internet

connection.

Another
major
point
of
consideration
is
the
iOS
development
libraries
that
allow
for

rapid
development
and
deployment
of
software.
As
mentioned
previously,
this

software
can
leverage
the
architecture
as
well
as
the
look
and
feel
of
native
iOS

36

applications,
which
are
major
reasons
why
the
iPad
has
captured
over
a
60%
share

of
the
tablet
computer
market
worldwide.

By
the
nature
of
a
seven
to
ten
day
mission,
the
storage
requirements
are
not

required
to
be
on
the
scale
of
a
permanent,
on-‐site
EHR
server
solution.

Analysis
of

data
captured
from
patient
visits
on
a
typical
mission
and
then
doubled
gives
us
a

minimum
requirement
of
storage
needs.

A
server
with
a
capacity
of
four
gigabytes

of
space
for
discrete
data
and
another
40
gigabytes
of
data
for
photo
and
media

storage
will
more
than
meet
the
requirements
for
a
mission.
Consideration
has
to
be

made
for
the
physical
footprint
of
the
server
hardware.
A
typical
server
is
too
large

and
may
use
a
hard
disk
that
in
transport
may
be
damaged.

Additionally,
attention

must
be
paid
to
the
different
electricity
requirements
in
the
location
where
the

mission
is
to
take
place.

An
Apple
Mac-‐Mini
server
at
2.5GHz
dual
core
processor
with
8G
of
memory
and
a

256G
solid-‐state
drive
is
suggested
as
a
preferred
solution.

This
configuration
will

support
up
to
40
concurrent
clients,
has
the
storage
capacity
to
support
an
entire

mission,
includes
the
durability
of
solid-‐state
hardware
technology
and
is
extremely

portable
for
transport.
In
addition
Mac-‐Mini
server
can
be
operated
“headless”
(no

keyboard,
mouse
or
display)
for
minimal
physical
impact
in
the
mission

environment.
By
employing
technology
that
is
in
the
same
technological
eco-‐system

as
the
iPad
other
benefits
can
be
realized,
such
as
remote
control
and
remote

network
administration.

37

Section
4.03 Networking

The
solution
is
designed
to
function
in
three
environments:
offline,
on
a
local

wireless
network
without
Internet
connectivity,
and
on
a
wireless
network
with
an

Internet
connection.
In
the
first
circumstance,
data
is
collected
and
stored
on
the

tablet’s
local
database
and
is
available
for
use
on
that
device.
This
may
be
the
case

when
a
patient
is
being
pre-‐screened
prior
to
the
start
of
a
mission.
Whenever
a

tablet
device
is
in
range
of
an
approved
wireless
network,
the
device
will

communicate
securely
with
the
server
application
that
is
running
and
begin
the
data

syncing
process.
Data
will
then
be
available
for
use
by
any
device
that
is
connected

to
the
approved
network.

During
a
mission
environment,
an
ad-‐hoc
wireless
network
will
be
deployed
that

serves
to
synchronize
data
from
all
tablet
devices
that
are
within
range.
In
this
way,

data
collected
during
the
mission
will
be
available
at
any
time
on
any
device
with

appropriate
user
permissions.
(See
figure
11)

38

Figure
11:
The
networking
strategy
of
the
proposed
solution

In
the
event
that
Internet
connectivity
is
available,
patient
health
information
and

mission
data
will
be
returned
to
a
centralized
global
database
where
comprehensive

reporting
can
be
performed.
If
there
is
no
Internet
connection
available
during
the

surgical
mission,
the
server
for
the
local
ad-‐hoc
network
will
wait
until
one
becomes

available
to
sync
with
the
centralized
global
database.

Section
4.04 Summary

The
solution
proposed
above
offers
the
ability
to
collect
high
quality
data
at
the

point
of
care.
This
data
is
immediately
usable
by
multiple
mission
volunteers
in
the

field,
as
opposed
to
data
collected
on
a
paper
based
medical
record.
Data
is
collected

Mission
Network
Internet
(if available)
Wireless Router
Application Server

39

more
efficiently
and
with
fewer
data
duplications.
Electronic
data
quality
assurance

measures
are
included
that
improve
the
quality
of
data
collected.
Quality
measures

can
be
incorporated
into
the
solution
to
prevent
medical
errors.

40

Chapter
5. Conclusion
–
Beyond
Operation
Smile

Surgical
missions
occur
under
time
constraints
in
difficult
environments
with

limited
resources
and
personnel.
The
traditional
focus
of
international
aid

organizations
has
been
to
provide
as
many
safe
surgeries
as
possible
while
working

within
the
bounds
of
this
challenging
scenario.
Thorough
medical
record
keeping

has
not
historically
been
prioritized
as
it
is
thought
to
consume
valuable
time
and

resources.
Thus,
many
humanitarian
groups
are
not
capitalizing
on
the
opportunity

to
collect
valuable
data
about
surgical
care
in
the
developing
world.

As
the
global
health
community
increases
its
attention
on
the
delivery
of
surgical

care
to
those
in
need,
ISHOs
must
respond
by
embracing
an
ethos
of
responsible

transparency
that
gives
precedence
to
comprehensive
collection,
analysis,
and

sharing
of
mission
data.
Operation
Smile
has
demonstrated
a
dedication
to

accountability,
quality
control,
and
research.
Until
now,
it
has
lacked
the
appropriate

tools
for
optimizing
data
for
use
outside
the
surgical
mission.
This
thesis
was

prepared
with
focus
on
the
goals
and
problems
faced
by
Operation
Smile
regarding

data
management.
Other
ISHOs
could
benefit
from
similar
introspection
and,

possibly,
from
an
electronic
solution
like
the
model
proposed
above
for
Operation

Smile.
In
order
to
assist
these
organizations
in
conducting
an
internal
assessment,

this
thesis
concludes
with
some
general
recommendations.

41

Following
the
same
framework
set
out
above
in
assessing
Operation
Smile,
surgical

organizations
should
follow
three
steps.
First,
understand
the
scope
of
the

organization
and
its
operational
environment.
Second,
understand
the
past
and

current
methods
of
medical
record
keeping.
This
includes
goals
and
limitations
of

data
use.
Third,
understand
the
flow
of
information.
Following
these
framework

recommendations
would
help
an
ISHO
in
planning
strategies
that
address
their
data

management
needs.

To
understand
the
scope
and
operational
environment,
aid
groups
should
begin

with
a
statement
of
case
volume
that
includes
and
estimated
number
of
surgical

evaluations,
surgeries,
and
follow
up
visits.
The
number
of
missions
per
year
and

locations
of
those
missions
should
be
detailed.
Additionally,
the
ISHO
should
include

a
description
of
its
annual
budget
including
how
much
could
be
devoted
to

improving
data
collection
and
management.

Understanding
the
environment
where
the
humanitarian
group
is
conducting

fieldwork
is
also
essential.
The
availability
of
important
infrastructure,
such
as

electricity,
wireless
data
networks,
or
Internet
access
could
impact
data

management
solutions
as
could
the
permanence
or
transience
of
the
mission.

Similarly,
the
qualities
of
the
workforce
would
need
to
be
evaluated.
A
small
core
of

dedicated,
long-‐term
workers
that
reside
permanently
in
the
field
would
lend
itself

to
a
different
solution
than
would
a
workforce
consisting
of
many
transient,
short-‐
term
volunteers.

42

In
assessing
past
and
current
medical
record
keeping
strategies,
ISHOs
should
begin

with
an
evaluation
of
the
purpose
of
data
collection.
The
organization
should
define

what
it
wants
to
do
with
the
data
that
it
collects
as
part
of
the
medical
record.
Then,

ISHOs
should
focus
on
the
alternative
strategies
they
have
considered
and
the

limitations
of
those
strategies.
Understanding
past
failures
is
essential
in
avoiding

future
ones.

Ultimately,
ISHOs
should
seek
to
describe
the
flow
of
information
during
a
mission.

As
outlined
in
this
thesis,
a
top-‐down
workflow
diagram
should
be
similar
across
all

surgical
charities
and
would
include
five
mandatory
steps:
patient
intake,
patient

screening,
surgery,
postoperative
care,
and
follow-‐up.
Within
each
step,
the
ISHO

should
elaborate
on
the
methods
used
for
collection
of
data
including:
who
collects

the
data,
what
data
is
collected,
the
medium
in
which
the
data
is
recorded,
and
the

purpose
of
collecting
the
data.
A
copy
of
the
existing
medical
record
would
augment

this
flow
diagram
and
help
to
understand
the
process
of
data
collection.

Section
5.01 Future
research
and
directions

(a) Development,
field-‐testing
and
implementation

Though
the
solution
elaborated
above
will
meet
Operation
Smile’s
data
collection

needs
in
theory,
this
solution
has
yet
to
be
programmed
or
implemented,
a
not

insignificant
hurdle.
Once
this
software
is
programed
and
debugged,
pilot
testing

43

would
need
to
be
done
to
ensure
usability.
Focus
groups
would
be
formed
to
discuss

flaws,
solutions,
and
additional
features.

Even
after
testing,
troubleshooting,
and
final
product
development,
the
solution

would
need
to
be
implemented
worldwide:
a
full
transition
away
from
paper

charting
methods.
Given
the
scope
of
Operation
Smile
and
the
number
of
sites

where
the
organization
conducts
missions,
the
implementation
strategy
would
have

to
be
carefully
planned.
Metrics
for
success
and
barriers
to
implementation
would

have
to
be
discussed
and
defined.

(b) Defining
global
data
standards
and
global
database

To
achieve
maximum
benefit
from
a
move
to
electronic
data
collection
and
data

warehousing,
the
global
surgical
humanitarian
community
would
need
to
define
and

adopt
standards
of
data
exchange.
Traditionally
diagnostic
and
treatment
codes,

such
as
International
Classification
of
Diseases
9
th

Revision
(ICD-‐9)
and
Current

Procedural
Terminology
(CPT),
exist
for
billing
purposes.
They
also
serve
as

definitions
that
allow
effective
communication
of
information
among
electronic

sources.
There
are
other
sources
of
clinical
healthcare
terminology
that
facilitate

data
exchange
and
allow
interoperability
among
healthcare
information
technology,

including
the
Systematized
Nomenclature
of
Medicine
–
Clinical
Terms
(SNOMED-‐
CT)
and
Logical
Observation
Identifiers
Names
and
Codes
(LOINC).
In
order
to

develop
a
globalized
database
into
which
ISHOs
could
import
de-‐identified
patient

44

data
for
research
purposes,
the
global
surgical
humanitarian
community
would

need
to
first
settle
on
data
coding
standards.

Lastly,
the
surgical
humanitarian
community
should
define
global
standards
of

patient
privacy.
Until
that
time,
it
would
be
incumbent
upon
aid
organizations
to

adhere
to
patient
privacy
standards
that
reflect
the
country
in
which
mission
work

occurs.
Thus,
software
solutions
should
incorporate
the
most
rigorous
privacy

standards
into
their
design.
The
adoption
of
global
standards
could
obviate
the
need

for
country
specific
privacy
standards.

45

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53

Appendix
A
–
Surgical
Outcomes
Research
in
Guwahati,
India

Study
Purpose

The
primary
purpose
of
this
study
is
to
report
the
overall
rate
of
early
postoperative

complications
after
cleft
lip
repair
in
a
mission
based
setting
in
the
developing

world.
A
secondary
purpose
is
to
evaluate
the
relationship
between
surgical
process

variables
and
early
surgical
outcomes.

Materials
and
Methods

Between
November
2010
and
February
2011,
654
patients
received
surgical

procedures
for
cleft
lip
deformities
in
a
single
institution
in
Guwahati,
India.

All

procedures
were
performed
at
Mahendra
Mohan
Choudhury
Hospital
during
two

large
surgical
missions
conducted
by
Operation
Smile.

Inclusion
criteria
were
set:
any
age,
any
cleft
type,
received
a
detailed

multidisciplinary
preoperative
assessment,
received
an
operation
for
repair
of
a

primary
or
secondary
cleft
lip
deformity,
and
returned
for
follow-‐up
examinations

at
one
week
after
surgery.
Included
in
this
retrospective
cohort
study
are
471

consecutive
cleft
lip
patients.

The
main
outcome
measure
was
presence
of
an
early
post-‐operative
complication.
A

complication
was
defined
as
the
presence
of
a
lip
infection
or
lip
dehiscence
at
the

54

time
of
one-‐week
follow
up.
Data
on
two
surgical
process
variables
was
collected
for

evaluation.
The
first
was
the
use
of
tissue
adhesives
for
wound
closure
after
cleft
lip

repair.
The
second
was
the
prescription
of
a
course
of
oral
prophylactic
antibiotics

on
discharge
after
cleft
lip
repair.
Data
collected
for
other
variables
included
age,

gender,
and
type
of
cleft.

Data
on
duration
of
anesthesia,
type
of
anesthesia
(local
vs.
general),
surgical

technique,
type
of
dermal
suture,
and
type
of
skin
suture
was
collected
but
not

analyzed
due
to
incomplete
or
missing
data.

The
primary
outcomes
measure
was
presented
as
an
overall
rate
of
complication.

Risk
Ratios
and
95%
Confidence
Intervals
for
secondary
variables
were
calculated

using
an
open-‐source
web-‐based
statistics
program,
OpenEpi.(“OpenEpi-‐-‐
Epidemiologic
Calculators,”
n.d.)

Institutional
review
board
approval
was
granted
through
University
of
Southern

California
and
through
the
Operation
Smile
India
Institutional
Ethics
Committee.

55

Summary
of
results

Characteristics
of
the
study
population
are
provided
in
Table
2.

Characteristics
of
Study
Population

Study
Subjects,
n
471

Age,
mean
y
(Median)
12.0
(8)

Gender

F,
n
(%)
204
(43.3%)

M,
n
(%)
267
(56.7%)

Cleft
Type

Primary
Complete,
n
(%)
234
(49.7%)

Primary
Incomplete/Secondary,
n
(%)
237
(50.3%)

Table
2:
Characteristics
of
Study
Population

56

In
total,
471
patients
met
criteria
for
inclusion
in
data
analysis
of
complication
rates.

427
of
471
(90.7%)
patients
had
an
unproblematic
postoperative
course,
and
44
of

471
patients
suffered
early
post-‐operative
complications,
resulting
in
an
overall

complication
rate
of
9.3%.
The
data
is
summarized
in
Table
3.

Title

Complicati
on

No

Complicat
ion

Complicati
on

Rate

Risk
Ratio

(95%
CI)

Overall,
n
44
427
9.30%

Tissue
adhesive
used,
n
22
169
11.50%

1.67
(0.93-‐
2.99)

NO
Tissue
Adhesive
used,
n
21
257
6.90%

Post-‐Operative
Antibiotics

Given,
n
13
99
11.60%

1.37
(0.74-‐
2.54)

NO
Post-‐Operative
Antibiotics

Given,
n
30
325
8.50%

Table
3:
Study
Results

The
use
of
skin
adhesive
could
not
be
determined
in
5
cases-‐
3
with
complications

and
2
without.
These
5
cases
were
excluded,
leaving
466
patients
for
analysis
for

use
of
skin
adhesive.
Skin
adhesive
was
used
in
41.0%
(191/466)
of
all
cases,
and
no

skin
adhesive
was
used
in
59.0%
(275/466).
The
rate
of
early
post-‐operative

complications
among
patients
who
received
tissue
adhesive
was
11.5%
(22/191),

57

whereas
the
rate
of
complications
in
patients
who
did
not
receive
a
skin
adhesive

was
6.9%
(19/275).
While
the
absolute
risk
increase
of
an
early
post-‐operative

complication
after
application
of
a
tissue
adhesive
for
wound
closure
was
4.6%,
the

risk
ratio
was
not
significant,
Risk
Ratio=1.67
(95%
CI
0.93-‐2.99).

The
use
of
postoperative
antibiotics
could
not
be
determined
for
4
cases
-‐
1
with

complications
and
3
without.
These
4
cases
were
excluded,
leaving
467
cases
for

analysis
for
the
use
of
postoperative
antibiotics.
A
course
of
postoperative

antibiotics
was
prescribed
in
24.0%
(112/467)
of
all
cases,
and
no
antibiotics
were

prescribed
in
76.0%
(355/467).

Unexpectedly,
the
rate
of
early
post-‐operative

complications
among
patients
who
were
prescribed
antibiotics
was
11.6%

(13/112),
whereas
the
rate
of
complications
in
patients
who
were
not
prescribed

antibiotics
was
8.5%
(30/355).
While
the
absolute
risk
increase
of
an
early
post-‐
operative
complication
after
receiving
a
prescription
for
postoperative
antibiotics

was
3.1%,
the
risk
ratio
was
not
significant,
Risk
Ratio=1.37
(95%
CI
0.74-‐2.54).

58

Appendix
B
–
Operation
Smile
Data
Management
Limitations

Data
collection
during
the
mission

Challenges

• Language
/
Culture

o Language
barriers
between
patients
and
volunteers
can
result
in

translation
errors
and
incorrect
data
being
captured.

o Cultural
misunderstandings
can
lead
patients
to
give
misleading

information
if
they
think
there
is
a
“right
or
wrong”
answer
or
if
they

feel
that
their
answer
will
improve
the
chances
that
their
child
gets

surgery.

o Patients
may
not
know
what
we
perceive
as
“common
everyday

knowledge”
like
their
birthday
or
age.

• Volunteer
Turnover

o In
a
mission
environment
there
is
a
high
volunteer
turnover
rate.
New

volunteers
are
not
formally
trained
in
medical
record
keeping

processes.

• Medical
Record
Design

o The
30-‐page
Operation
Smile
Medical
Record
is
difficult
to
navigate.

o Data
that
takes
too
long
to
record
may
be
neglected

o Patient
health
information
can
be
recorded
in
unexpected
and

unauthorized
locations.
(See
figure
12).

59

Figure
12:
Important
patient
health
information
is
recorded
in
pen
on
the
front
of
the
chart

• Misunderstanding
of
importance
of
data
collection

o Volunteers
do
not
expect
someone
to
use
data
for
research
purposes.

o Collect
data
that
is
of
primary
importance
to
patient
care

• Mission
Dynamics

o Volunteers
fail
to
collect
data
due
to
time
limitations
and
competing

clinical
duties

• Lack
of
chart
availability

o Charts
move
with
the
patient

60

o Once
the
patient
and
their
chart
have
moved
outside
of
the
realm
of

care
of
a
mission
volunteer,
the
opportunity
to
record
data
may
be

lost.

Data
quality

• Missing
data
fields

o Operation
Smile
uses
a
forms-‐based
paper
chart
that
incorporates
free

text
entry,
fill
in
the
blank,
check
boxes,
multiple
answer
format,

technical
drawings,
and
graphs.
Due
to
the
challenges
mentioned

above,
there
are
often
missing
data
fields.
Fully
completed
Operative

notes
were
rare,
though
this
was
not
quantified.

• Ambiguity

o Form
design
and
missing
data
can
lead
to
confusion.
(See
figure
13)

Figure
13:
Form
ambiguity.
A
blank
value
for
B.
and
C.
is
unclear.

• Redundancy

o Duplicated
data
is
inefficient
and
can
result
in
contradictions.

• Free
text
entry

61

o Free
text
data
is
difficult
to
codify
and
enter
in
a
database

o Data
recorded
in
the
medical
record
outside
of
the
form
would
likely

be
missed
during
data
entry.
(See
figure
14)

Figure
14:
Free
text
entry
outside
of
forms

• Legibility
of
handwriting

o Illegible
handwriting
results
in
unusable
data.
(See
Figure
15)

Figure
15:
Illegible
handwriting

62

Data
entry
and
database
management

Challenges

• Chart
availability

o Charts
can
be
unavailable
due
to
patient
care
priorities.
Paper
charts

must
be
located
and
organized
before
data
entry
begins.

• Missing
portions
of
medical
record

o When
the
chart
is
available,
the
medical
record
may
be
missing
pages,

or
pages
in
the
medical
record
may
be
out
of
order.

• No
data
entry
protocols

o Data
is
recorded
in
multiple
locations
in
the
chart.
Standardized

protocols
for
data
entry
do
not
exist.
When
conflicting
data
is
present,

it
is
a
judgment
call
which
data
is
entered
by
the
volunteer.

• Cost

o Volunteers
must
be
sent
on
missions
for
the
specific
purpose
of
data

entry.
Devoting
personnel
specifically
to
this
task
prevents
these

volunteers
from
assisting
in
patient-‐oriented
care.
This
is
resource

intensive
and
requires
expenditures
by
Operation
Smile.

• Technology
failure

o Volunteers
are
usually
not
information
technology
or
data
entry

specialists,
yet
they
are
required
to
set
up
and
enter
data
into
a

Microsoft
Access
Database.
They
may
be
unable
to
troubleshoot

software
or
hardware
failures,
should
those
arise.

63

• Poor
Form
Design

o In
an
effort
to
attempt
to
comprehensively
capture
a
large
amount
of

mission
data,
the
electronic
forms
were
designed
to
be
as
broad
in

scope
as
possible.
As
a
result,
these
forms
can
be
overwhelming
and

confusing
for
data
entry
volunteers.
Rather
than
promote
thorough

collection
of
important,
high
quality
data,
poor
form
design
leads
to

variability
in
the
amount
and
type
of
data
collected,
especially
when

the
forms
allow
free
text
entry.

Data
Quality

Current
data
entry
methods
that
occur
during
the
surgical
mission
have
resulted
in

flawed
databases
that
are
of
little
use
for
research
purposes.

• Data
entry
personnel

o Volunteers
with
unclear
experience
and
data
entry
credentials

entered
the
data
entered
into
the
Operation
Smile
Access
Database.

They
underwent
minimal
training
and
had
no
prior
quality
checks.

The
validity
of
the
data
entered
by
untrained
staff
is
questionable.

• Single
data
entry

o The
data
was
input
into
the
database
by
single
entry.
Double
data

entry
has
been
the
gold
standard
for
research
as
it
allows
data
quality

control
measures
to
be
implemented
and
databases
to
be
compared.

In
the
absence
of
double
data
entry,
many
important
data
quality

control
measures
cannot
be
instituted.

64

• No
data
cleaning

o Free
text
entry
results
in
poor
quality
data
that
requires
subsequent

data
cleaning.
The
databases
are
not
usually
used
for
research

purposes
until
after
the
surgical
mission.
At
that
time,
the
paper

charts
may
be
unavailable
for
data
verification
purposes.

Data
Utilization

Challenges

• Multiple
databases

o A
new
Operation
Smile
Access
Database
is
created
separately
for
each

mission.
To
conduct
research
over
multiple
missions,
databases
had
to

be
combined.
Merging
the
data
sets
required
exporting
the
Access

database
to
a
Microsoft
Excel
file
and
then
merging
multiple
files.

After
export,
the
Excel
files
had
to
be
reorganized
and
standardized

prior
to
being
merged,
a
time
consuming
task.

• Field
irregularities

o Data
entry
fields
were
not
standardized.
As
a
result,
sorting
and

filtering
data
was
difficult

• Unwieldy
databases

o Databases
that
were
exported
as
Excel
files
were
large
and
with

nearly
200
columns.
Many
columns
contained
data
that
was
not

complete
or
not
needed.

65

Quality

• Database
merging
errors

o On
merging
of
multiple
Excel
files,
data
could
be
merged
into
the

wrong
column
or
row.

66

Appendix
C
–
Electronic
Health
Records
and
Mobile
Health

Technology

I
have
proposed
that
applying
information
technology
during
surgical
missions
is

feasible
and
will
improve
quality
of
data
collected.
Here
I
will
elaborate
on
the

benefits
EHRs
and
discuss
their
use
in
developing
countries.
I
will
further
describe

the
use
of
Mobile
Health
(mHealth)
technology
and
demonstrate
how
these
devices

have
functioned
to
improve
data
collection
methods
in
the
challenging

environments
of
a
surgical
mission
in
the
developing
world.

Electronic
Health
Record

The transition from paper charting to an EHR in high-income countries is
associated with improvements in quality of care and reduction in medical
errors.(Menachemi & Collum, 2011) With an EHR, there is an increased capacity
for structured and coded clinical data collection that can be subsequently used for
clinical decision support and analysis.(Lobach et al., 2005) However, in resource
poor environments, EHR implementation is difficult and faces substantial barriers.
These include a dearth of funding for upfront costs, inconsistent supply of
electricity, poor computer literacy among staff, and lack of information technology
support.(Fraser & Blaya, 2010; Mohammed-Rajput et al., 2011) Yet,
there is
burgeoning evidence that, despite the substantial barriers to implementation, the

67

use of EHR in the developing world is feasible and has many of the same benefits
to patient care and data quality.(Blaya et al., 2010; Tomasi, Facchini, & Maia,
2004)

Medical humanitarian organizations, such as Partners in Health, have adopted
customizable open source EHR software to help with their medical record keeping
needs.(“Medical Informatics | Partners In Health,” n.d.) OpenMRS is “a
community developed, open source, enterprise electronic medical record system
platform.”(“OpenMRS » Open source health IT for the planet,” n.d.)
This
solution

has
been implemented in over 24 countries worldwide finding use in primary care
and in the management of chronic diseases such as HIV, tuberculosis, and cardiac
disease.(Mohammed-Rajput et al., 2011; Thompson, Castle, Lubeck, & Makarfi,
2010) Many of these EHR solutions have the advantage of being deployed from
fixed facilities where local infrastructure and long term staff availability can be
leveraged to implement long term solutions. Authors have emphasized the
importance of training medical staff to work with the EHR software and rely on
highly trained information technology staff for support.(Fraser & Blaya, 2010;
Were et al., 2010)

There are no reports of EHRs being used in the field at the point of care by ISHOs.

68

Mobile
Health
Technology

Mobile
phones,
smart
phones,
Personal Digital Assistants (PDAs)
and
lightweight

tablet
computers,
such
as
the
iPad,
are
seeing
increased
use
in
the
healthcare

environment.
Applications
in
the
field
of
mHealth
technology
allow
clinicians
ever

increasing
access
to
healthcare
information
from
various
locations
and
provide

more
and
better
options
to
collect
data.

Networking
solutions
with
faster
data
transfer
speeds
and
expansions
in
wireless

infrastructure
allow
data
collected
in
the
field
to
be
wirelessly
synced
with
a
master

database.
Native
device
technology
such
as
geotagging
with
a
Global
Positioning

System
(GPS),
barcode
scanning
or
fingerprint
identification
can
facilitate
patient

identification
and
tracking.
Built
in
cameras
with
ability
to
record
still
photographs,

audio,
and
video
bring
new
possibilities
to
the
types
of
data
that
can
be
collected

and
have
application
in
telemedicine
and
teleconferencing.

Already,
mHealth
technologies
are
finding
application
in
many
different
challenging

environments.
In
an
emergency
disaster
management,
mass-‐casualty
mock-‐scenario

where
local
infrastructure
was
devastated,
mHealth
solutions
were
applied
to

deploy
an
EHR
on
an
ad-‐hoc
wireless
network.(Chan
et
al.,
2011;
Lenert
et
al.,
2011)

Using
PDAs
and
tablet
devices,
emergency
field
workers
were
able
to
identify
and

label
disaster
victims
with
a
barcode
bracelet,
record
and
transmit
their
vital
sign

data,
and
fix
their
location
with
GPS.
This
allowed
victims
to
be
remotely
triaged

69

according
to
acuity
and
found
by
subsequent
emergency
teams.
These
researchers

demonstrated
that
the
use
of
mHealth
technology
improved
documentation
and

tracking
of
patient
status
compared
to
traditional
paper
methods.

Fingerprinting,
barcode
scanning,
geotagging
and
remote
data
entry
via
PDA
have

also
been
used
in
the
developing
world.
Studying
Dengue
virus
epidemiology
in

Nicaragua,
researchers
were
able
to
demonstrate
substantial
improvements
in

patient
care.(Avilés,
Ortega,
Kuan,
Coloma,
&
Harris,
2008)
Fingerprinting
and

barcode
scanning
led
to
improved
patient
identification,
more
rapid
localization
of

patient
charts,
and
decreased
patient
wait
times.
Geotagging
helped
logistical

planning,
and
allowed
mapping
of
dengue
cases
in
time
and
space,
and,
when
field

visits
were
needed,
helped
researches
find
the
patients’
homes.
The
use
of
PDAs
in

the
field
provided
researchers
with
faster
access
to
data,
ability
to
backup
data,

decreased
the
use
of
paper
produced,
and
eliminated
the
need
for
manual
double

data
entry.
By
integrating
mHealth
technologies
into
clinical
practice,
these

researchers
were
able
to
“overcome
many
of
the
challenges
of
performing
research

in
a
resource
limited
setting.”(Kuan
et
al.,
2009)

Handheld
devices,
such
as
PDAs
or
tablet
computers,
have
been
shown
to
be
useful

as
time-‐effective
stand-‐alone
data
collection
tools
or
to
enhance
or
replace
the
EHR

at
point
of
care.(Fahy,
2009;
Lobach
et
al.,
2010;
VanDenKerkhof,
Goldstein,
Lane,

Rimmer,
&
Van
Dijk,
2003)
Their
use
has
been
shown
to
improve
the
quality
of

clinical
data
collection
when
compared
to
paper
methods.(S.
J.
Lane,
Heddle,
Arnold,

&
Walker,
2006;
Stengel,
Bauwens,
Walter,
Köpfer,
&
Ekkernkamp,
2004)
These

70

benefits
extend
to
the
use
of
PDAs
in
the
developing
world
where
they
have
also

been
shown
to
save
time
and
cost
of
data
entry.(Bernabe-‐Ortiz
et
al.,
2008;
Missinou

et
al.,
2005;
Shirima
et
al.,
2007;
Yu,
de
Courten,
Pan,
Galea,
&
Pryor,
2009)
In
this

environment,
workers
with
little
or
no
experience
with
PDAs
were
able
to

effectively
use
the
devices
with
minimal
training.(Ali
et
al.,
2010)

Data
entry
at
point
of
care
with
handheld
devices
offers
some
distinct
advantages

over
both
paper
forms
and
use
of
an
EHR.
Data
quality
control
measures,
like
form

validation
and
automatic
check
functions,
can
be
incorporated
into
the
software

program.
Data
entered
from
handheld
devices
can
be
used
as
soon
as
the
device
is

synced
to
a
master
database,
and
the
data
does
not
need
to
be
cleaned.
Handheld

devices
have
been
demonstrated
to
be
most
beneficial
when
time
is
a
critical
factor,

when
connecting
spatially
distributed
coworkers,
and
when
data
entry
requires

flexibility
and
mobility
to
maximize
efficiency.(Prgomet,
Georgiou,
&
Westbrook,

2009)
However,
PDAs
have
been
criticized
for
their
small
screen
size,
difficult

navigation,
and
potential
for
device
failure.(Lu,
Xiao,
Sears,
&
Jacko,
2005)

MHealth
technologies
have
been
proven
to
profoundly
enhance
healthcare
data

management
in
challenging
environments.
Yet,
the
field
is
growing
rapidly,
and
the

scientific
literature
has
yet
to
catch
up
to
the
most
current
innovations.
The
impact

of
breakthrough,
game-‐changing
devices
like
the
iPad
on
information
technology
in

healthcare
is
still
to
be
determined.

71

Appendix
D
–
The
Operation
Smile
Medical
Record
(OSMR)

Figure
16:
OSMR
Form
1,
Page
1

72

Figure
17:
OSMR
Form
1,
Page
2

73

Figure
18:
OSMR
Form
1,
Page
3

74

Figure
19:
OSMR
Form
1,
Page
4

75

Figure
20:
OSMR
Form
2

76

Figure
21:
OSMR
Form
3,
Page
1

77

Figure
22:
OSMR
Form
3,
Page
2

78

Figure
23:
OSMR
Form
3,
Page
3

79

Figure
24:
OSMR
Form
4

80

Figure
25:
OSMR
Form
5

81

Figure
26:
OSMR
Form
6,
Page
1

82

Figure
27:
OSMR
Form
6,
Page
2

83

Figure
28:
OSMR
Form
6,
Page
3

84

Figure
29:
OSMR
Form
6,
Page
4

85

Figure
30:
OSMR
Form
6,
Page
5

86

Figure
31:
OSMR
Form
6,
Page
6

87

Figure
32:
OSMR
Form
6,
Page
7

88

Figure
33:
OSMR
Form
7

89

Figure
34:
OSMR
Form
8

90

Figure
35:
OSMR
Form
9,
Page
1

91

Figure
36:
OSMR
Form
9,
Page
2

92

Figure
37:
OSMR
Form
10

93

Figure
38:
OSMR
Form
11,
Page
1

94

Figure
39:
OSMR
Form
11,
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2

95

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40:
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96

Figure
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13

97

Figure
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OSMR
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14

98

Figure
43:
OSMR
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15

Abstract (if available)

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