e-IMCI: Improving Pediatric Health Care in Low-Income Countries

1. e-IMCI: Improving Pediatric Health Care in Low-Income Countries Brian DeRenzi Quals Talk November 19, 2007 University of Washington

2. 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

3. Outline • Motivation • Introduction • Background on Project • Integrated Management of Childhood Illness (IMCI) • e-IMCI • Field Study • Results • Future work • Acknowledgements

4. 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

5. Motivation • Every 6 seconds a child dies unnecessarily

6. 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

7. 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

8. IMCI

9. 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

10. 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

11. 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

12. Background How the project started and how I got involved.

13. 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

14. 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

15. 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

16. South Africa

17. Tanzania • Worked with IHRDC • Met with the Tanzanian government and other NGOs

18. IMCI Integrated Management of Childhood Illness.

19. IMCI Example

20. IMCI Example

21. IMCI Example

22. IMCI Example

23. IMCI Example

24. e-IMCI Electronic delivery of IMCI.

25. e-IMCI Interface

26. 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

27. Field Study Real clinicians. Real patients. Real world.

28. Mtwara, Tanzania • Worked with IHRDC in Mtwara, Tanzania • Southern Tanzania • Rural • Subsistence farming • Fishing • Piloted e-IMCI at a dispensary

29. 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

30. 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

31. 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

32. Study Design • Real Patients, not actors • Used same data collection forms for current practice and e-IMCI • Pairwise design • Basic pilot, no randomization

33. Trials per Clinician Clinician

34. Results Numbers, reactions and lessons.

35. 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

36. Adherence: The Numbers

37. Adherence: Advice Numbers

38. Timing • No substantial increase in patient visit time † unpaired t-test, ‡ paired t-test of 18 trials

39. Clinician Reaction • Unanimously cited e-IMCI as easier to use and faster than following the chart booklet

40. 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

41. Lessons Learned • Limitations • Question Grouping • Threshold Problem • Requirements • Flexibility • Incorrect IMCI • otitis externa • Local Preference • Antibiotic • Lab use

42. 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

43. Future Work Where we’re going.

44. 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

45. 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

46. Deploying Protocols • Interfaces for tutor, guided and expert modes • Automatically generate interfaces for different platforms • Maintain consistent look and feel

47. Community Outreach • Take e-IMCI outside of the health facility • Travel village-to-village to collect health census information and deliver care

48. 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.

49. Questions

50. Extra Slides Just in case.