用生成式AI整合远程监测与电子病历，缓解医生信息过载。
Mitigating Clinician Information Overload: Generative AI for Integrated EHR and RPM Data Analysis
- 用大语言模型分析远程监测与电子病历的混合数据。
- 支持自然语言对话，实现临床决策辅助与病程导航。
- 适合医疗AI研究者和临床系统设计者参考。
生成式人工智能（GenAI），尤其是大型语言模型（LLM），在解读医疗领域复杂数据方面展现出强大能力。本文全面概述了GenAI在提取临床洞察和提升临床效率方面的潜力、需求与应用。首先介绍了患者数据的形态与来源，包括实时远程患者监测（RPM）流和传统电子健康记录（EHR）。这两类数据的海量性与异质性给临床医生带来巨大挑战，导致信息过载。同时，我们探讨了基于LLM的应用如何提升临床效率，例如通过自然语言对话增强对纵向患者数据的导航，并提供可操作的临床决策支持。本文还讨论了在简化医生工作流程、实现个性化医疗方面的机遇，以及数据集成复杂性、数据质量与远程监测数据可靠性、患者隐私保护、AI输出的临床安全性验证、偏见缓解和临床接受度等关键挑战。我们认为，这是首次系统总结GenAI应对融合型RPM/EHR数据带来的临床信息过载问题的综述。
原文摘要 · Abstract (English)
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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