# TRACER, a time-to-event EHR foundation model for disease detection across health systems

**Authors:** Mara Pleasure, Ekaterina Redekop, Vedrana Ivezic, Rushikesh Kulkarni, Aaron Chin, William Speier, Corey W. Arnold  
**Lab:** University of California, Los Angeles · **Published:** 29 Oct 2026  
**Links:** [arXiv](https://arxiv.org/abs/2610.40536) · [PDF](https://arxiv.org/pdf/2610.40536) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2610.40536.html)

## Key numbers

- **95%**: We found that TRACER retained its discrimination in external health systems, and at 95% specificity

## Abstract

Foundation models trained on longitudinal electronic health records (EHRs) can predict clinical diagnoses years in the future, making them a potential tool for disease screening. We developed TRACER (Temporal Risk Assessment with Clinical EHR Representations), a decoder-based foundation model pretrained on data from over 4.5 million patients from four health systems, and evaluated it on 16 incident diseases at 1-, 2-, and 5-year horizons in more than 2 million patients from two health systems excluded from model development. In contrast to typical evaluations that focus on global metrics (e.g., AUROC), we measured performance at the low false-positive rates that are realistic for screening. We found that TRACER retained its discrimination in external health systems, and at 95% specificity, the patients it flagged were up to eight times more likely to be diagnosed within a year than unselected patients. Evaluating at this operating point also revealed what global metrics alone did not: detection and false-alert burden varied across diseases and health systems, even when discrimination appeared stable. Our results illustrate the potential of foundation models for screening and underscore the need for evaluation in health systems excluded from development to assess disease detection and false-alert burden at levels appropriate for intended clinical use.}

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