Enhancing the prediction of hospital discharge disposition with extraction-based language model classification.

ABSTRACT: Early identification of inpatient discharges to skilled nursing facilities (SNFs) facilitates care transition planning. Predictive information in admission history and physical notes (H&Ps) is dispersed across long documents. Language models adeptly predict clinical outcomes from text but have limitations: token length constraints, noisy inputs, and opaque outputs. Therefore, we developed extraction-based language model classification (ELC): generative language models distill H&Ps into task-relevant categories ("Structured Extracted Data") before summarizing them into a concise narrative ("AI Risk Snapshot"). We hypothesized that language models utilizing AI Risk Snapshots to predict SNF discharges would perform the best. In this retrospective observational study, nine language models pred

SUBMITTER: Small WR 

PROVIDER: S-EPMC12789015 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature