TIMER: temporal instruction modeling and evaluation for longitudinal clinical records
Hejie Cui, Alyssa Unell, Bowen Chen, Jason Fries, Emily Alsentzer, Oluwasanmi Koyejo, Nigam H. Shah
Abstract
Electronic health records (EHRs) contain rich longitudinal information for clinical decision-making, yet LLMs struggle to reason across patient timelines. We introduce TIMER (Temporal Instruction Modeling and Evaluation for Longitudinal Clinical Records), a method to improve LLMs' temporal reasoning over multi-visit EHRs through time-aware instruction tuning. TIMER grounds LLMs in patient-specific temporal contexts by linking each instruction-response pair to specific timestamps, ensuring temporal fidelity throughout the training process. Evaluations show that TIMER-tuned models outperform conventional medical instruction-tuned approaches by 6.6% in completeness on clinician-curated benchmarks, with distribution-matched training demonstrating advantages up to 6.5% in temporal reasoning. Qualitative analyses reveal that using TIMER enhances temporal boundary adherence, trend detection, and chronological precision, necessary for applications such as disease trajectory modeling and treatment response monitoring. Overall, TIMER provides a methodological basis for developing LLMs that can effectively engage with the inherently longitudinal nature of data for patient care. Code is available at TIMER .