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Mitigating Clinician Information Overload: Generative AI for Integrated EHR and RPM Data Analysis

arXiv 2509.00073 ↗First posted 26 Aug 2025cs.LGcited by 0

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Abstract (arXiv)

Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), offer powerful capabilities for interpreting the complex data landscape in healthcare. In this paper, we present a comprehensive overview of the capabilities, requirements and applications of GenAI for deriving clinical insights and improving clinical efficiency. We first provide some background on the forms and sources of patient data, namely real-time Remote Patient Monitoring (RPM) streams and traditional Electronic Health Records (EHRs). The sheer volume and heterogeneity of this combined data present significant challenges to clinicians and contribute to information overload. In addition, we explore the potential of LLM-powered applications for improving clinical efficiency. These applications can enhance navigation of longitudinal patient data and provide actionable clinical decision support through natural language dialogue. We discuss the opportunities this presents for streamlining clinician workflows and personalizing care, alongside critical challenges such as data integration complexity, ensuring data quality and RPM data reliability, maintaining patient privacy, validating AI outputs for clinical safety, mitigating bias, and ensuring clinical acceptance. We believe this work represents the first summarization of GenAI techniques for managing clinician data overload due to combined RPM / EHR data complexities.

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References · 16

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  3. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  4. 2022Large Language Models Encode Clinical Knowledgecited by 1,830
  5. 2023A Comprehensive Overview of Large Language Modelscited by 648
  6. 2023Capabilities of GPT-4 on Medical Challenge Problemscited by 638
  7. 2022Emergent Analogical Reasoning in Large Language Modelscited by 240
  8. 2024A Framework for Human Evaluation of Large Language Models in Healthcare Derived from Literature Reviewcited by 66
  9. 2023Multimodal LLMs for health grounded in individual-specific datacited by 49
  10. 2024MEIT: Multimodal Electrocardiogram Instruction Tuning on Large Language Models for Report Generationcited by 29
  11. 2024REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Modelscited by 23
  12. 2024XAI meets LLMs: A Survey of the Relation between Explainable AI and Large Language Modelscited by 23
4 more
  1. 2024EMERGE: Enhancing Multimodal Electronic Health Records Predictive Modeling with Retrieval-Augmented Generationcited by 17
  2. 2024MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysiscited by 15
  3. 2024Enhancing Clinical Efficiency through LLM: Discharge Note Generation for Cardiac Patientscited by 14
  4. 2024Large Language Model Capabilities in Perioperative Risk Prediction and Prognosticationcited by 11

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