Retrospective Supervision Reshapes EHR Patient States Beyond Future Prediction

Electronic health record (EHR) foundation models compress longitudinal records into patient states that are reused across downstream tasks, yet these representations are evaluated mainly by how well they predict future outcomes. This leaves unmeasured how much of the patient's history remains accessible and which patients the representation places near one another. We introduce Chronos, a patient-state model trained with prospective and retrospective objectives: predicting future clinical events and reconstructing a structured fingerprint of prior history. On MIMIC-IV, frozen Chronos states achieve 0.871 mean AUROC across 16 clinical prediction tasks, compared with 0.862 for the strongest existing model, and 0.701 mean AUROC across eight target-masked phenotype-retrieval cohorts, outperforming the best learned baseline by 0.06. In a matched ablation, retrospective supervision leaves future-task performance essentially unchanged while improving retrieval from 0.651 to 0.697 and making both observed and omitted history more recoverable. These effects replicate across independently trained models and persist under projection and record-footprint controls. Our results show that prospective predictiveness, retrospective accessibility, and patient-similarity geometry capture distinct properties of reusable EHR representations.