Neal Lesh

Computer science applications to improve health delivery in low-income countries. Neal Lesh

My Story

Mid-thirties computer researcher seeks more fulfilling career. Goes back to school then off to Africa. Discovers things are more simple and more complex than he originally imagined. Can't imagine doing anything else...

Outline

Background

The simplicity and complexity of global inequity

Two examples

Patient record systems for AIDS treatment

Medical algorithms on handhelds

Conclusion

Risk Factor

for surviving the Titanic.

% survived Poverty as a

Global Health

Simple Story $$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$ $ Infant mortality: 5 per 1,000 births Maternal mortality: 8 per 100K births Life expectancy: 78 years Infant mortality: 95 per 1,000 births Maternal mortality: 500-1000 per 100K Life expectancy: 45 years 300-540 57 69

Simple Story $$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$ $

Simple Story $$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$ $$$$$$$$$$$$$ $$$$ Infant mortality: 95 per 1,000 births Maternal mortality: 500-1000 per 100,000 b Life expectancy: 45 years

Simple Story “ We are the first generation that can end poverty. ” - Eveline Herfkens, UN Millennium Campaign

Complexity

Corruption, careerism, tax write-offs

5-star poverty alleviation meetings

Unintended consequences, e.g., paying volunteers

Imperialism & foreign experts

“ If you want to build a ship, don't drum up people to collect wood and don't assign them tasks and work, but rather teach them to long for the endless immensity of the sea.”

Antoine de Saint-Exupery

Information as Care

Study: rigorous application of standard treatment protocols reduced in-hospital mortality in children’s malaria cases by 50%

Clinician’s complaint: where are my lab results?!

Patient Knowledge Example: five danger signs for seeking care during and after labor.

Outline

Background

Two examples

Conclusion

One year later

AIDS Treatment in Rural Rwanda

One year later

Improving Health Systems

One year later

Connecting to the Internet

Electronic Medical Record (EMR) Patient Monitoring Reports Clinicians & Patients Managers EMR Staff Paper forms Program Monitoring Reports Funder & government reports $ Re-allocate resources

Patient Monitoring

Missed-Visit List

ICT task: satisfy reporting requests

OpenMRS

Open source framework for medical record systems

www.openmrs.org

Data Quality

Mistyped IDs

Missing & conflicting data

Backlog

Potential solution: point-of-care systems

Challenges & Opportunities

Keep up with demand

Increased impact on decision making

Inform to Improve (I2I) teams

Integration of lab and pharmacy components

Detecting important trends in data

Outline

Background

Two examples

Conclusion

Rural Dispensary in Tanzania

Standardized Care (IMCI)

Tanzania: underfive mortality was 13% lower in the two IMCI districts Source: Schellenberg J et al Full IMCI in HF End of study 13% difference 95% CI: -7%, 30% Significant impact on stunting

Deploying IMCI

IMCI

Shown to reduce mortality and morbidity

Adopted by ~100 countries

But uptake not as good as hoped

Training expensive

Correct use tapers off over time

Supervision challenging

Why Automate IMCI?

Improve adherence

Improve supervision

Easier to update

More sophisticated protocols

Reduced training

Field Work Results to be published in CHI’08

How Automate IMCI?

Exploratory Study

Pretesting & rapid iteration

Structured interviews

Observed trials w/ additional clinician to:

Ensure safety

Record adherence to IMCI

Record time

Viral Training

Key Findings

Must be

Fast

Flexible

Improve adherence to IMCI

Must address intentional deviation from IMCI

Temperature, respiratory rate

Advice

Adherence Results 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 the 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

Triaging patients on treatment for AIDS (Study ongoing in South Africa)

e-CTC for HIV screening Counselors ask a series of questions leading to a patient assessment.

CommCare Start House Hold Visit Plan Day Explore Data Exit

House Hold Visit (Task Queue) 0 0 0:04:56 Register Birth Investigate Diarrhea of Sick Child Review malaria bed nets Topic of month: nutrition during pregnancy END VISIT

Day Planning MKWERA : TB Referral (2 wks) MKEA: Severe diarrhea (3 days) CHUMA: late HH visit (3 months) KAIGILE: routine HH visit MGANDA: routine HH visit EXIT

Outline