Clinical utility of EHR-based predictive models for identifying high-risk individuals in early cancer detection

Abstract Electronic health records (EHRs) offer a promising, scalable approach for identifying individuals at high risk for targeted cancer screening, but the absence of clinical benchmarks has limited their adoption. We evaluated the clinical utility of EHR-based predictive models for 12-month cancer risk across eight major cancers—breast, lung, colorectal, prostate, ovarian, liver, pancreatic, and stomach—using longitudinal data from over 865,000 participants in the All of Us Research Program, which uniquely integrates EHR, genomic, and survey data. Compared to traditional risk factors (e.g., age, family history, genetic variants), EHR-based models significantly improved identification of high-risk groups. The models achieved a 3- to 6-fold increase in risk enrichment for breast, colorectal, pancreatic, and stomach cancers relative to traditional risk factors alone. For liver cancer, the model achieved the highest absolute lift (27.6-fold compared to the general population), although the relative improvement over known risk factors was more modest (1.68-fold). These findings establish practical benchmarks for EHR-based cancer risk prediction and provide insights for integrating such models into clinical workflows to enable more precise and scalable early detection strategies.