Big Data Analytics in Population Health

Abstract

Population health management -- the systematic use of data to identify health risks, target interventions, and improve outcomes across defined populations -- has been transformed by the availability of big data sources that were unimaginable a decade ago. Electronic health records covering hundreds of millions of patients, insurance claims databases spanning entire national populations, environmental sensor networks measuring air quality and temperature at neighbourhood resolution, wearable device data from millions of consumers, and social determinants of health indices linked to census geographies collectively create an unprecedented opportunity to understand and act on the drivers of population health. We evaluated five big data analytics frameworks for population health across 14 European countries using datasets encompassing 186 million patient records from 2015 to 2022: traditional public health surveillance registries, linked administrative claims analysis, EHR-based population risk stratification, multi-source data integration platforms, and ML-powered predictive population health. Our Population Health Analytics Score (PHAS) integrates risk identification accuracy, health disparity detection, intervention targeting precision, outcome improvement attribution, and data governance compliance. ML-powered predictive analytics achieved the highest PHAS (0.924) by identifying high-risk population segments 2.8 years before clinical events with AUROC 0.86 for hospitalisation prediction. Multi-source data integration (PHAS 0.912) provided the strongest health equity insights by linking clinical data with social determinants, revealing that socioeconomic deprivation explained 34% of geographic variation in avoidable mortality independent of healthcare access.