ELECTRONIC HEALTH RECORD-BASED PREDICTIVE APPROACHES FOR HOSPITAL READMISSION: A STRUCTURED REVIEW OF SOCIOECONOMIC VARIABLES AND ALGORITHMIC EQUITY
TL;DR
To make predictive healthcare tools truly useful in practice, it is not enough to focus only on accuracy, they also need to account for fairness, real clinical context, and the actual consequences of their use in everyday healthcare settings.
Abstract
Background: Hospital readmission rates are often used to judge the quality of care and how well healthcare systems are working. Predictive models based on electronic health records (EHRs) can help estimate which patients are at higher risk, but when they don’t properly reflect socioeconomic realities, they can unintentionally reinforce existing inequalities (Obermeyer et al., 2019). This review looks at how well these EHR-based models predict 30-day readmissions, how Social Determinants of Health (SDOH) are included, and what this means for fairness and health equity in real-world use. Methodology: A structured literature review was conducted using PubMed/MEDLINE and Google Scholar. It included peer-reviewed studies published between 2018 and 2026, focusing on model validation studies, retrospective cohort analyses, and research on bias and fairness in predictive healthcare models. Results: Synthesis reveals that incorporating unstructured and structured SDOH variables yields mixed impacts on discriminatory performance (Area Under the Receiver Operating Characteristic curve changes ranging from -0.01 to +0.04), yet significantly rectifies systemic algorithmic underestimation of risk for marginalized demographics. Unadjusted models consistently show racial, ethnic, and socioeconomic bias, often because they rely on proxy variables embedded in historical healthcare data (Gianfrancesco et al., 2018). As a result, preventive transition-of-care resources can be systematically directed away from the patients who need them most (Pierson et al., 2021). Conclusion: To make predictive healthcare tools truly useful in practice, it is not enough to focus only on accuracy. They also need to account for fairness, real clinical context, and the actual consequences of their use in everyday healthcare settings (Wiens et al., 2019). Policy-level interventions must mandate standardized SDOH extraction alongside equity-focused calibration algorithms before widespread clinical deployment (Ghassemi et al., 2021).