Learning to Identify Locally Actionable Health Anomalies

Local information access (LIA) programs tap into existing public health data flows, and present data in simple and useful ways to ground staff. LIAs hold great potential for improving rural health systems in developing regions; benefits include more evidence-based decision making and optimizations at a local scale, as well as improved service delivery and data quality. Our fledgling LIA program in rural Uganda currently provides clinicians with a small set of static data visualizations for discussion. To increase the program’s effectiveness, we want to automatically identify relevant data visualizations. We propose an adaptive tool that learns from local clinicians’ decision-making processes to predict and generate visualizations that show actionable anomalies.