Since 2019, Healthy Learners and THINKMD in partnership with the Zambian Ministries of Health and Education, has deployed THINKMD’s clinical intelligence platform into 491 schools and into the hands of 5215 trained School Health Workers (SHWs). During every patient assessment with THINKMDs clinical decision support and data analysis platforms, key clinical and disease risk data is captured that can be leveraged for program and population health decision makers. The THINKMD platform has captured over 1,16M patient encounters, performed 16,7M clinical risk assessments and captured 17,3M number of clinical data points – an incredibly rich data set.
In October 2023, a cholera outbreak surfaced in Lusaka, Zambia, with over 18,000 reported cases and more than 400 fatalities by mid-February. Leveraging the THINKMD dataset, which coincided geographically with the worst-affected regions (mainly around the capital, Lusaka), THINKMD’s data science experts utilized machine learning techniques to investigate if the data exhibited syndromic signals related to diarrheal illnesses that could have forewarned of the cholera outbreak risk.
Key Highlights
- THINKMD’s clinical intelligence platform has captured over 1,16M patient encounters via 491 schools in Zambia, generating key clinical and disease risk data that can be leveraged for program and population health insight.
- In October 2023, a cholera outbreak surfaced in Lusaka, Zambia. Machine learning techniques were used to investigate if THINKMD’s data exhibited syndromic signals related to diarrheal illnesses that could have forewarned of the cholera outbreak risk.
- Results demonstrate that meaningful syndromic clinical signals could power early warning syndromic and disease surveillance system for conditions conducive to facilitating outbreaks such as cholera.
- Such an early warning system would allow public health decision makers to take proactive measures such as improving water and sanitation service delivery, community messaging, and ultimately, prevent the unnecessary loss of life.
