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Using Large Language Model Artificial Intelligence to Enhance Clinical Competency Committee Insight.

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Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise in evaluating resident feedback, generating assessments rated higher for quality and accuracy than human evaluations. While AI-generated feedback is comparable or superior, its usefulness requires further study.

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Area of Science:

  • Medical Education
  • Artificial Intelligence in Healthcare
  • Clinical Competency Assessment

Background:

  • Large language models (LLMs) offer potential for enhancing resident evaluation.
  • Limited research exists comparing AI to human analysis of clinical evaluations.
  • This study evaluates LLM AI for resident feedback analysis.

Purpose of the Study:

  • To assess the effectiveness of LLM AI in evaluating resident feedback.
  • To compare AI-generated resident assessments with human-generated ones.
  • To explore AI's role in streamlining educator review processes.

Main Methods:

  • Utilized LLM Llama-3.1 70B for analyzing emergency medicine (EM) educator feedback for 31 EM residents.
  • AI generated summaries of resident strengths, weaknesses, and milestone performance.
  • Clinical competency committee (CCC) members surveyed on AI-generated content quality, accuracy, specificity, and usefulness.

Main Results:

  • AI-generated assessments were significantly longer (391 words) than human assessments (79 words).
  • CCC members rated AI content more favorably for quality, accuracy, and specificity.
  • Usefulness ratings favored human-generated content, though AI was rated acceptable by 54.1%.

Conclusions:

  • AI can produce EM resident assessments with quality, accuracy, and specificity comparable or superior to human assessments.
  • AI-driven evaluations have the potential to streamline educator review and reduce workload.
  • Further investigation into the usefulness of AI-generated feedback is warranted.