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| Name | Apply Prediction Models for Clinical Decision Support in a Real-World EMR in a Low-Resource Setting |
| Video Uploader | Video From Advance-CTR |
| Upload Date | This Video Uploaded At 19-04-2021 17:57:57 |
| Video Discription | “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” Nancy Puttkammer is an Assistant Professor in the Department of Global Health at University of Washington, and the faculty Co-Lead of the Digital Initiatives Group at the International Training and Education Center for Health (DIGI/I-TECH). Dr. Puttkammer is trained as a health services researcher, specializing in using observational, routinely-collected data from electronic medical records (EMRs) to strengthen HIV care and treatment programs. Tracy Sandifer is an epidemiologist interested in application of machine learning to health information systems for clinical and public health goals in surveillance and optimization of services. She has led design, development, implementation, and evaluation of several large, multi-source population-based health information systems in the United States and internationally, including Malawi, Botswana, Zimbabwe, Ukraine, India, and Haiti. 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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