Predicting Deprescribing of High-Risk Medications Using Provider EHR Use and Patient Characteristics

Potentially inappropriate medications expose older adults to preventable harm, yet deprescribing remains difficult to implement consistently. Although electronic health record (EHR) interventions can reduce prescribing, health systems lack clear evidence about which routinely captured patient, primary care provider (PCP), and intervention-design factors predict medication discontinuation or dose tapering. Understanding these determinants is essential for targeting and scaling deprescribing support. In this study, we conducted the first machine-learning analysis of these trial data. We analyzed 2,979 adults aged 65 years or older and 158 structured EHR features spanning patient characteristics, PCP characteristics and EHR-use behaviors, and deprescribing-tool design. We compared eight models for predicting medication discontinuation or dose tapering and used SHAP to examine feature importance. TabPFN achieved the highest positive predictive value (67.71%), AUROC (74.30%), and AUPRC (60.95%), although overall predictability was moderate. Our findings show that even rich structured EHR data only moderately predict deprescribing, suggesting that important clinical determinants are not captured in routine fields. PCP EHR-use measures accounted for 19 of the 25 highest-ranked TabPFN features, although they also constituted most candidate predictors. The study provides the health system with an informative reference for predicting high-risk medication deprescribing. Future models should incorporate richer clinical context and undergo external validation before informing personalized deprescribing support.