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Leveraging a Large Language Model to Generate Quality Improvement Feedback for Clinical Notes.

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Large language models (LLMs) provide feedback on clinical notes that is as good as physician feedback. This AI-driven approach can improve documentation quality and streamline healthcare operations.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Documentation Improvement

Background:

  • Clinical documentation quality significantly impacts healthcare operations.
  • Current feedback mechanisms for improving clinical notes are often time-consuming and insufficient.
  • Large language models (LLMs) offer a potential solution for streamlining feedback processes.

Purpose of the Study:

  • To evaluate if LLM-generated feedback on medical contingency and discharge planning (MCDP) is non-inferior to physician feedback.
  • To assess the effectiveness of Generative Pre-trained Transformer 4 (GPT-4) in improving clinical documentation quality.

Main Methods:

  • A cross-sectional study compared GPT-4 feedback with physician feedback on 64 inpatient progress notes.
  • Notes were selected for low likelihood of containing MCDP using the AI Audit Tool.
  • A/B testing evaluated understandability, usefulness, acceptability, and impartiality using Likert scales.

Main Results:

  • GPT-4 feedback demonstrated non-inferiority to physician feedback across all evaluated measures.
  • Understandability (mean 1.27), usefulness (mean 2.09), and acceptability (mean 2.07) were significantly better with GPT-4.
  • Impartiality also showed non-inferior results (mean -0.20).

Conclusions:

  • LLM-generated feedback is a viable alternative to expert clinician feedback for improving clinical note quality.
  • This technology can enhance the efficiency and effectiveness of clinical documentation improvement processes.
  • AI tools like LLMs show promise in addressing documentation quality challenges in healthcare.