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e-IMCI: Improving Pediatric Health Care in Low-Income Countries. Brian DeRenzi Quals Talk November 19, 2007. University of Washington. e-IMCI. Project PDA-based decision support for clinicians at the point of care Increase quality of care delivered Result
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e-IMCI: Improving Pediatric Health Care in Low-Income Countries Brian DeRenzi Quals Talk November 19, 2007 University of Washington
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 to 0.5% 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 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
Background How the project started and how I got involved.
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 US 1 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
Tanzania • Worked with IHRDC • Met with the Tanzanian government and other NGOs
IMCI Integrated Management of Childhood Illness.
e-IMCI Electronic delivery of IMCI.
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
Field Study Real clinicians. Real patients. Real world.
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-IMCI increases adherence • Determine how e-IMCI affects patient 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 Clinician
Results Numbers, reactions and lessons.
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
Timing • No substantial increase in patient visit time † unpaired t-test, ‡ paired t-test of 18 trials
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
Future Work Where we’re going.
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.
Extra Slides Just in case.