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Evaluating Large Language Models for Drafting Emergency Department Discharge Summaries.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Informatics
  • Natural Language Processing

Background:

  • Large language models (LLMs) show potential for clinical applications, including automated text summarization.
  • The deployment of LLM-based tools like ambient AI scribes in healthcare necessitates rigorous accuracy evaluations.
  • Emergency Department (ED) discharge summaries are critical for patient care continuity.

Purpose of the Study:

  • To evaluate the performance of GPT-4 and GPT-3.5-turbo in generating ED discharge summaries.
  • To identify the prevalence and types of errors in LLM-generated discharge summaries.
  • To assess the accuracy and clinical relevance of information summarized by LLMs.

Main Methods:

  • A cross-sectional study design was employed.
  • 100 adult ED visits from 2012-2023 at University of California, San Francisco ED were randomly selected.
  • GPT-4 and GPT-3.5-turbo generated discharge summaries from ED clinician notes and were reviewed by Emergency Medicine physicians for inaccuracies, hallucinations, and omissions.

Main Results:

  • GPT-4 and GPT-3.5-turbo generated summaries with varying error rates; 33% of GPT-4 and 10% of GPT-3.5-turbo summaries were error-free.
  • GPT-4 summaries had 10% inaccuracies, 42% hallucinations, and 47% omissions.
  • Inaccuracies and hallucinations were most common in the 'Plan' section, while omissions often involved 'Physical Examination' and 'History of Presenting Complaint'.

Conclusions:

  • LLMs can generate discharge summaries but are prone to factual hallucinations and omission of critical information.
  • Understanding the specific error patterns in LLM-generated clinical text is crucial for safe implementation.
  • Expert clinician review remains essential to mitigate risks associated with AI-generated medical documentation.