Apply Prediction Models for Clinical Decision Support in a Real-World EMR in a Low-Resource Setting

Applying Prediction Models for Clinical Decision Support within a Real-World EMR in a Low-Resource Setting: Lessons Learned on Using Haiti’s iSanté EMR to Support Differentiated HIV Care. Electronic medical records (EMRs) are promising tools for supporting differentiated HIV care, which matches intensity of services to patient needs, in low- and middle-income settings. Haiti’s national EMR, called iSanté, represents an excellent test case for studying the utility of EMR-based clinical decision support in low- and middle-income countries. We describe a trial of an EMR-based clinical decision support feature to identify patients at high risk of HIV treatment failure, as well as on-going work to optimize prediction models within EMRs such as iSanté.

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