Multimorbidity Measurement Strategies for Predicting Hospital Visits

Abstract Introduction : Multimorbidity is a known driver of adverse clinical outcomes and increased healthcare utilization. Although data from Electronic Health Records (EHRs) might enable risk prediction efforts, optimal multimorbidity measurement methods remain unclear. We compared multimorbidity measurement approaches to predict healthcare outcomes beyond demographics and prior healthcare utilization. Study Design and Methods : This is a retrospective cohort study using 15-year EHR data (650,651 patients, 9.4 million visits). Three phenotyping methods and five multimorbidity indices were evaluated across five outcomes and four time horizons, using logistic regression models. Best performing multimorbidity measurements were further trained with XGBoost and feature importance analysis was performed. Results : Including multimorbidity improved prediction over demographic and prior healthcare utilization features for all evaluated outcomes, particularly for rarer outcomes like inpatient mortality and unplanned admissions. Comorbidity index performance varied by outcome: Charlson Comorbidity Index was superior for mortality and readmissions, while Multimorbidity Weighted Index best predicted unplanned admissions. Clinically-curated phenotyping rules consistently outperformed standardized code-based approaches. Previous admissions and multimorbidity were top predictors of most outcomes while increased outpatient visits correlated with fewer unplanned admissions. Conclusions : Our findings show that multimorbidity measurement should be outcome-specific, with clinically-curated phenotyping methods outperforming standardized coding approaches. Healthcare systems should integrate both multimorbidity and prior utilization patterns for risk prediction models to better identify patients at highest risk for adverse outcomes.