Beyond Prediction: Longitudinal Reasoning in EHR-Integrated Clinical AI

• URL: http://arxiv.org/abs/2606.08413v1
• Date: Sun, 07 Jun 2026 02:26:23 GMT
• Title: Beyond Prediction: Longitudinal Reasoning in EHR-Integrated Clinical AI
• Abstract summary: We develop a coding framework that captures technical integration strategies and reasoning-relevant representational features.<n>Our analysis shows that while many systems incorporate EHR data, they predominantly operate on encounter-level or aggregated representations.<n>We argue that current approaches treat EHR data as a static input rather than a substrate for ongoing clinical reasoning.
• Score: 0.79827881560134
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Clinical Reasoning in the Age of AI: Longitudinal Cognition and Human-AI Collaboration [0.7588418803784354]
  Understanding how doctors reason through clinical problems in real-world settings is critical for design of effective AI reasoning systems.<n>This study offers a unified framework for understanding clinical reasoning as a context-sensitive, temporally extended process.
  arXiv  Detail & Related papers  (2026-06-07T03:51:15Z)
• From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction [61.12883122613684]
  This Review focuses on intervention-aware disease trajectory modeling in clinical AI--methods estimating patient-specific longitudinal disease evolution and assessing trajectory changes under alternative treatments.<n>We organize the field around six linked components: three decision tasks (factual forecasting, counterfactual estimation, policy evaluation) and three data-generating mechanisms (disease evolution, treatment assignment, observation process) that determine identifiability.<n>We present the first unified framework bridging forecasting, counterfactual trajectories, and policy evaluation across discrete/continuous time, explicitly addressing treatment assignment, time-varying confounding, and observation bias.
  arXiv  Detail & Related papers  (2026-05-16T10:45:26Z)
• Medical Model Synthesis Architectures: A Case Study [72.46211022258122]
  Current AI systems struggle with calibrated reasoning under uncertainty.<n>We propose a framework for AI systems that can make practically useful but formally transparent clinical predictions under uncertainty.<n>We show how an initial proof-of-concept of this framework can be used for differential diagnosis.
  arXiv  Detail & Related papers  (2026-05-10T19:30:16Z)
• Integrating Genomics into Multimodal EHR Foundation Models [56.31910745104141]
  This paper introduces an innovative EHR foundation model that integrates Polygenic Risk Scores (PRS) as a foundational data modality.<n>The framework aims to learn complex relationships between clinical data and genetic predispositions.<n>This approach is pivotal for unlocking new insights into disease prediction, proactive health management, risk stratification, and personalized treatment strategies.
  arXiv  Detail & Related papers  (2025-10-24T15:56:40Z)
• Retrieval-Augmented Framework for LLM-Based Clinical Decision Support [0.19999259391104388]
  This paper proposes a clinical decision support system powered by Large Language Models (LLMs) to assist prescribing clinicians.<n>The framework integrates natural language processing with structured clinical inputs to produce contextually relevant recommendations.<n>We outline the system's technical components, including representation representation alignment and generation strategies.
  arXiv  Detail & Related papers  (2025-10-01T18:45:25Z)
• Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications [59.721265428780946]
  Large Language Models (LLMs) in medicine have enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, and verifiable reasoning.<n>This paper provides the first systematic review of this emerging field.<n>We propose a taxonomy of reasoning enhancement techniques, categorized into training-time strategies and test-time mechanisms.
  arXiv  Detail & Related papers  (2025-08-01T14:41:31Z)
• CliCARE: Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Records [30.100759175769454]
  Large Language Models (LLMs) hold significant promise for improving clinical decision support and reducing physician burnout.<n>We propose CliCARE, a framework for Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Records.<n>We validated our framework using large-scale, longitudinal data from a private Chinese cancer dataset and the public English MIMIC-IV dataset.
  arXiv  Detail & Related papers  (2025-07-30T10:02:16Z)
• Time-Aware Attention for Enhanced Electronic Health Records Modeling [8.4225455796455]
  TALE-EHR is a Transformer-based framework featuring a novel time-aware attention mechanism that explicitly models continuous temporal gaps.<n>Our approach outperforms state-of-the-art baselines on tasks such as disease progression forecasting.
  arXiv  Detail & Related papers  (2025-07-20T07:32:41Z)
• From EHRs to Patient Pathways: Scalable Modeling of Longitudinal Health Trajectories with LLMs [38.49879425944787]
  We propose a novel approach to patient pathway modeling by transforming diverse electronic health record (EHR) data into a structured representation.<n>We introduce a novel summary mechanism that embeds long-term temporal context into topic-specific summary tokens, improving performance over text-only models.
  arXiv  Detail & Related papers  (2025-06-05T09:54:01Z)
• Temporal Entailment Pretraining for Clinical Language Models over EHR Data [9.584923572354045]
  We introduce a novel temporal entailment pretraining objective for language models in the clinical domain.<n>Our method formulates EHR segments as temporally ordered sentence pairs and trains the model to determine whether a later state is entailed by, contradictory to, or neutral with respect to an earlier state.
  arXiv  Detail & Related papers  (2025-04-25T07:30:38Z)
• Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes [50.8044927215346]
  We consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state. We employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability. Our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
  arXiv  Detail & Related papers  (2023-02-11T18:07:11Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.