Optimization of Population-Health Interventions Leveraging Geospatial and Predictive Analytics to Promote Care Equity

Abstract

Members of populations experience health inequities in spite of dramatic improvements in clinical care and overall health care and are indicative of imbedded differences in both social determinants of health, environmental exposures, accessibility of healthcare, and the allocation of resources. Conventional population-health initiatives generally depend on aggregate indicators and ex post analysis and thereby are less effective in identifying localized vulnerability, predicting exceptional risks and fairly distributing services. The paper focuses on the problem of population-health intervention optimization by the integrated application of geospatial analytics and predictive analytics as the way to proactively advance care equity. The given approach utilizes the high-resolution geospatial data coupled with predictive analytics to identify spatial, temporal and demographic patterns of health risk and service use. Geospatial techniques allow accurate mapping of disparities at small geographic levels by combining different streams of data, such as census and socioeconomic data, electronic health records, environmental and climatic data, mobility data, and healthcare infrastructure data. Through these analyses, clusters of unmet need, structural impediments to access and contextual factors that affect health outcomes have been identified and usually remain hidden in conventional population-level analyses.