Toward Integrating Machine Learning-powered Polysocial Risk Scores into Electronic Health Record Workflows

Social determinants of health (SDoH) account for 80% of modifiable factors driving health disparities. Health systems play a critical role in addressing patients' unmet social needs essential to health outcomes. To integrate social risk management into patient health care, we developed an electronic health record (EHR)-based machine learning-powered pipeline to identify and address unmet social needs associated with hospitalization risk. By quantifying social risk via a polysocial risk score, this tool enables healthcare providers to identify patients at high social risk and prioritize targeted SDoH interventions. However, gaps exist regarding integrating our polysocial risk score tool into clinical flow. Therefore, in this study, through participatory design sessions with healthcare providers and social workers following user-centered design (UCD) principles, we initiated the integration of this predictive model into EHR workflows. This preliminary work lays the foundation for a comprehensive formal user-centered design process to enhance social risk assessment and intervention implementation.