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TIMER: temporal instruction modeling and evaluation for longitudinal clinical records.

Hejie Cui1, Alyssa Unell2, Bowen Chen3

  • 1Stanford Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA.

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|September 27, 2025
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Summary
This summary is machine-generated.

TIMER enhances large language models (LLMs) for analyzing electronic health records (EHRs). This method improves temporal reasoning in LLMs, leading to better patient timeline analysis for clinical decision-making.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Informatics

Background:

  • Electronic health records (EHRs) offer valuable longitudinal patient data.
  • Current large language models (LLMs) face challenges in temporal reasoning across multi-visit EHRs.

Purpose of the Study:

  • To introduce TIMER (Temporal Instruction Modeling and Evaluation for Longitudinal Clinical Records), a novel method for enhancing LLMs' temporal reasoning capabilities.
  • To improve the ability of LLMs to process and interpret patient timelines within EHRs.

Main Methods:

  • TIMER employs time-aware instruction tuning to ground LLMs in patient-specific temporal contexts.
  • Instruction-response pairs are linked to specific timestamps to ensure temporal fidelity during training.

Main Results:

  • TIMER-tuned models demonstrated a 6.6% improvement in completeness compared to conventional medical instruction-tuned models on clinician-curated benchmarks.
  • Distribution-matched training with TIMER showed up to a 6.5% advantage in temporal reasoning.
  • Qualitative analysis confirmed enhanced temporal boundary adherence, trend detection, and chronological precision.

Conclusions:

  • TIMER provides a methodological foundation for developing LLMs adept at handling longitudinal EHR data.
  • This approach is crucial for advancing applications like disease trajectory modeling and treatment response monitoring.