Beyond Prediction: Longitudinal Reasoning in EHR-Integrated Clinical AI

2026-06-06

AI Summary

This study addresses a critical limitation in current clinical AI systems, which typically treat electronic health records (EHRs) as static inputs and lack capabilities for longitudinal modeling and interpretable reasoning over patient histories. The work proposes the first EHR-integrated AI framework specifically designed for longitudinal clinical reasoning, combining clinical natural language processing, cross-visit trajectory modeling, and qualitative physician interviews to systematically evaluate existing systems’ performance in temporal dynamics, information integration, and missing-data inference. Findings reveal that prevailing approaches predominantly rely on visit-level or aggregated representations, omit explicit longitudinal mechanisms, and prioritize predictive accuracy over clinical interpretability. By shifting the focus from prediction-centric paradigms toward time-sensitive, explainable decision support, this framework advances the development of clinically meaningful AI tools.

Abstract

We present a structured analysis of how contemporary clinical AI systems integrate electronic health record (EHR) data and the extent to which they support longitudinal clinical reasoning. Drawing on a curated corpus of clinical natural language processing (NLP) and EHR-integrated systems, we develop a coding framework that captures both technical integration strategies and reasoning-relevant representational features, such as trajectory modeling, cross-encounter synthesis, longitudinal analysis, and absence reasoning. We also elicited the experiences of three physicians in their EHR use, including what strengths and weaknesses they found with their institution's current EHR system(s). Our analysis shows that while many systems incorporate EHR data, they predominantly operate on encounter-level or aggregated representations, with limited support for explicit temporal reasoning across patient histories. Reasoning-relevant structures are inconsistently represented, and evaluation paradigms remain largely focused on predictive performance instead of longitudinal interpretability. We argue that current approaches treat EHR data as a static input rather than a substrate for ongoing clinical reasoning, and we outline a framework for understanding how future systems might more effectively align with the temporal and interpretive structure of clinical practice.

Problem

• longitudinal reasoning
• electronic health records
• temporal reasoning
• clinical AI
• interpretability

Innovation

• longitudinal reasoning
• EHR-integrated AI
• trajectory modeling
• temporal reasoning
• clinical interpretability