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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Communication

Background:

  • Increasing volume of patient-provider electronic messages necessitates efficient management.
  • Large Language Models (LLMs) offer potential for automating clinical messaging tasks.
  • Effective message triage is crucial for successful LLM implementation in healthcare.

Purpose of the Study:

  • To analyze the utility of LLMs in addressing knowledge-based questions within patient-provider messages.
  • To characterize the challenges of using LLMs for message triage in real-world clinical data.
  • To evaluate a rule-based Syntactic Question Detector for triaging messages.

Main Methods:

  • Analysis of over 4 million patient-provider messages from Electronic Health Records.
  • Implementation and evaluation of a rule-based Syntactic Question Detector on 500 messages.
  • Comparison of the detector's performance with LLM capabilities for question detection.

Main Results:

  • Detecting questions in informal clinical text is difficult due to implicit requests and varied language.
  • A Syntactic Question Detector showed limitations in accurately identifying all questions.
  • 25% of MyChart messages containing questions were not responded to by the clinical team.

Conclusions:

  • Real-world clinical data presents significant challenges for automated message analysis.
  • Accurate question detection is a non-trivial but essential step for effective LLM deployment in healthcare.
  • A structured pipeline is suggested for integrating LLMs into clinical messaging workflows.