Using Applied Machine Learning to Predict Healthcare Utilization Based on Socioeconomic Determinants of Care

This study demonstrates that it is possible to generate a highly accurate model to predict inpatient and emergency department utilization using data on socioeconomic determinants of care. ABSTRACT Objectives: To determine if it is possible to risk-stratify avoidable utilization without clinical data and with limited patient-level data. Study Design: The aim of this study was to demonstrate the influences of socioeconomic determinants of health (SDH) with regard to avoidable patient-level healthcare utilization. The study investigated the ability of machine learning models to predict risk using only publicly available and purchasable SDH data. A total of 138,115 patients were analyzed from a deidentified database representing 3 health systems in the United States.