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e-IMCI: Improving Pediatric Health Care
in Low-Income Countries
University of Washington
Brian DeRenziQuals Talk
November 19, 2007
e-IMCI
Project PDA-based decision support for clinicians at the
point of care Increase quality of care delivered
Result Significantly increased adherence to medical
protocol without substantially increasing patient visit time
Contribution Adapted code base to implement the protocol for
pediatric health care Ran two-month field study in rural Tanzania to
pilot the system and determine how it can help
Outline
Motivation Introduction Background on Project Integrated Management of Childhood
Illness (IMCI) e-IMCI Field Study Results Future work Acknowledgements
Motivation
This year almost 10 million children will die before reaching the age of 5
Most live in low-income countries 10% of infants die during
their first year, compared to0.5% in wealthy countries
Almost 2/3 could be saved by the correct application of affordable interventions
Motivation
Every 6 seconds a child dies unnecessarily
Introduction
UNICEF, WHO and others develop medical protocols e.g. Integrated Management of Childhood
Illness (IMCI) Clinical guidelines for busy facilities Easy to use for lowly-trained health
workers
Introduction - IMCI
Originally developed in 1992
Adopted by over 80 countries worldwide
Children 0-5 years old Common illness
Cough Diarrhea Fever Ear Pain Malnutrition
Eacer
IMCI
IMCI Barriers
Expense of training ($1150 -$1450) Not sufficient supervision Chart booklet
Takes a long time to use Natural tendency to be less rigorous Social pressure
Result - not often followed in health clinics
Related Work
Automating procedural tasks Using mobile devices can help under high workloads
Harvard University Program on AIDs (HUPA) Project Designing medical protocol in South Africa
Decision support in India TRACNet, OpenMRS, IHRDC study Gary Marsden Computable protocols
GLIF Artificial Intelligence
Expert systems, Probabilistic systems
e-IMCI
Put IMCI protocol on PDA Guide health workers step-by-step Potential benefits
Better adherence to protocol Easier and faster than book Data collection is a by-product of care Can handle more complex protocols Interface with other devices and EMR Reduce training time and cost Strong supervision
How the project started and how I got involved.
Background
D-Tree International
Medical algorithms on mobile devices Help over-burdened health workers Gather data from the field Work with governments to implement
sustainable programs HUPA project
HUPA Project
Started in Cape Town HIV screening
algorithm Counselors can quickly
determine if patient needs to see doctor
Huge shortage of doctors
29.1% national HIV prevalence1
Less than 1% in US1 http://www.avert.org/safricastats.htm
South Africa
Worked with Right To Care Non-profit at Helen
Joseph Hospital Second site for HUPA
project Gained experience with
the HUPA code Delivered PDAs,
established workflow Introduced to health
facilities and field work
South Africa
Tanzania
Worked with IHRDC Met with the Tanzanian government and
other NGOs
Integrated Management of Childhood Illness.
IMCI
IMCI Example
IMCI Example
IMCI Example
IMCI Example
IMCI Example
Electronic delivery of IMCI.
e-IMCI
e-IMCI Interface
e-IMCI
Implemented subset of IMCI protocol for pilot study
Contains cough, diarrhea, fever and ear pain questions and treatment
First visit, ages 2 weeks to 5 years
Real clinicians. Real patients. Real world.
Field Study
Mtwara, Tanzania
Worked with IHRDC in Mtwara, Tanzania Southern Tanzania Rural
Subsistence farming Fishing
Piloted e-IMCI at a dispensary
Study Design
Started with five clinicians Four clinicians completed study Goals:
Discover usability issues
Discover if e-IMCIincreases adherence
Determine how e-IMCI affectspatient visit
IMCI Protocol Use
Ideal case Follow paper chart booklet for every patient
between 0-5 years of age “Current practice”
Treat patients from memory, occasionally referencing the chart booklet
e-IMCI trials Treat patients using the e-IMCI software
system
Study Design
Started with some pre-trials to fix major bugs
Semi-structured interview of all clinicians
Observed 24 “current practice” IMCI sessions
27 e-IMCI sessions Exit interview for
each clinician
Study Design
Real Patients, not actors
Used same data collection forms for current practice and e-IMCI
Pairwise design Basic pilot, no
randomization
Trials per Clinician
1 2 3 4 5
Number of “current practice” trials
5 5 5 5 4
Number of e-IMCI trials 13 - 6 4 4
Clinician
Numbers, reactions and lessons.
Results
Adherence
Measured adherence using 23 items IMCI asks the practitioner to perform
e-IMCI significantly improved adherence to the IMCI protocol p < 0.01 p < 0.01
Adherence: The Numbers
Investigation
Current Practice Adherence
e-IMCI Adherence
p-value
Vomiting 66.7% (n=24)
85.7% (n=28)
-
Chest Indrawing
75% (n=20)
94.4% (n=18)
-
Blood in Stool
71.4% (n=7)
100% (n=3)
-
Measles in Last 3 Months
55.6% (n=9)
95.2% (n=21)
<0.05
Tender Ear
0% (n=1) 100% (n=5)
-
All 61% (n=299)
84.7% (n=359)
< 0.01
Adherence: Advice Numbers
Clinical Officer
Current Practice Adherence
e-IMCI Advice Adherence
p-value
1 20% (n=15)
76.9% (n=39)
< 0.01
3 26.7% (n=15)
66.7% (n=18)
< 0.05
4 80% (n=15)
100% (n=12)
-
5 100% (n=12)
73.3% (n=21)
-
All 56.9% (n=72)
77.4% (n=84)
< 0.01
Timing
Clinical Officer
Average Length of Current Practice Patient Visit (minutes)
Average Length of e-IMCI Patient Visit (minutes)
95% Confidence Interval of e-IMCI Minus Current Practice
1 16 (n=5) 13 (n=13) -2.1 to 7.9 †
3 6 (n=5) 8 (n=6) -5.5 to 1.0 †
4 7 (n=5) 9 (n=4) -5.7 to 4.7 †
5 19 (n=4) 14 (n=4) -2.1 to 13.1 †
Total 10 (n=24) 11 (n=27) -2.4 to 2.4 ‡
† unpaired t-test, ‡ paired t-test of 18 trials
No substantial increase in patient visit time
Clinician Reaction
Unanimously cited e-IMCI as easier to use and faster than following the chart booklet
Clinician Reaction
Wanted to use the system for Care Treatment Clinic
Liked being able to review answers to questions
Asked to be in future studies “Sometimes since I have experience
[with IMCI] I will skip things, but with the PDA I can’t skip.”
Would “use a combination” of current practice and the e-IMCI software and would never need to refer to the book
Lessons Learned
Limitations Question Grouping Threshold Problem
Requirements Flexibility
Incorrect IMCI otitis externa
Local Preference Antibiotic Lab use
Conclusion
e-IMCI significantly improves adherence to IMCI protocol
Does not substantially lengthen the patient visit time
Positive reaction from clinicians, but room for improvement
Large number of interesting enhancements for the future
Where we’re going.
Future Work
e-IMCI for Training
Current training lasts 11-16 days
Costs $1150 - $1450 per person
Using e-IMCI to train, could reduce time and cost
No need to train the protocol as in-depth
Tutored mode
User-Driven Model
“Expert” mode Allow users to
decide what investigations to perform
Flexibility will encourage long-term use
Merge with current system-driven approach to ensure correct care
Deploying Protocols
Interfaces for tutor, guided and expert modes
Automatically generate interfaces for different platforms
Maintain consistent look and feel
Community Outreach
Take e-IMCI outside of the health facility Travel village-to-village to collect health
census information and deliver care
Acknowledgments
Neal Lesh, Marc Mitchell, Gaetano Borriello, Tapan Parikh, Clayton Sims, Werner Maokola, Mwajuma Chemba, Yuna Hamisi, David Schellenberg, Kate Wolf, Victoria DeMenil, D-Tree International, Dimagi Inc., the Ifakara Health Research & Development Centre, the Ministry of Health in Tanzania and the clinicians in Mtwara for their support and contribution to this work.
Questions
Just in case.
Extra Slides
The vision.
Introduction
What others have done.
Related Work
IMCI in Tanzania
Adapted and adopted by Tanzania in 1996
National policy Main component is a medical protocol
followed by health workers at the point of care
Pre-Grad School
Volunteered with American Red Cross after Hurricane Katrina
Volunteered with International Service Learning to deliver medical supplies in rural Tanzania
Introduction
Digitize protocol to make it easier to use