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

TL;DR

This paper reviews how Generative AI, especially Large Language Models, can help clinicians manage complex, combined EHR and RPM data, improving efficiency and decision-making while addressing key challenges.

Contribution

It provides the first comprehensive overview of GenAI techniques specifically for reducing clinician data overload from integrated RPM and EHR data.

Findings

GenAI can enhance navigation of longitudinal patient data

LLMs offer actionable clinical decision support

Identifies critical challenges like data integration and privacy

Abstract

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.