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Using Large Language Models to Analyze Symptom Discussions and Recommendations in Clinical Encounters.

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Summary

Large language models (LLMs) can accurately analyze patient-provider interactions, showing strong agreement with human coders. This technology offers a feasible tool for improving healthcare quality and communication by analyzing clinical encounters.

Keywords:
large language modelspatient–provider interactionssymptom discussions

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Clinical Communication Research

Background:

  • Analyzing patient-provider interactions is crucial for assessing care quality but is often hindered by time and methodological challenges.
  • Large language models (LLMs) present a potential solution for analyzing these interactions, but their accuracy needs validation.
  • Existing methods for evaluating clinical communication are resource-intensive, limiting their widespread application.

Purpose of the Study:

  • To evaluate the accuracy and reliability of a large language model (LLM) in analyzing patient-provider communication within clinical encounter transcripts.
  • To compare the coding performance of an LLM against human coders on key aspects of symptom discussions.
  • To determine the feasibility of using LLMs as a research tool for analyzing patient-provider interactions.

Main Methods:

  • A large language model (GPT-4) was used to code 236 potential symptom discussions from 92 cancer patient clinical transcripts.
  • Human coders independently analyzed the same transcripts to identify symptom discussion, initiation, and recommendations.
  • Cohen's kappa (κ) was calculated to measure interrater agreement between the LLM and human coders.

Main Results:

  • The LLM demonstrated strong to moderate interrater reliability with human coders across all measures.
  • Highest agreement was observed for symptom discussion (κ = 0.89), followed by initiation (κ = 0.82) and recommendations (κ = 0.78).
  • Disagreements regarding recommendations occurred in 16% of cases, categorized into nine distinct types.

Conclusions:

  • LLMs show comparable analytical abilities to humans in evaluating patient-provider interactions from clinical transcripts.
  • The use of LLMs can significantly enhance the feasibility of analyzing patient-provider communication for research purposes.
  • LLM-driven analysis holds potential for broader applications in assessing care quality, identifying inequities, and improving communication.