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Retrospective Comparative Analysis of Prostate Cancer In-Basket Messages: Responses From Closed-Domain Large Language

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  • 1Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ (Y.H., J.H., S.H.P., N.Y.Y., W.L.); Cornell University, Ithaca, NY (Y.H.); Department of Electric Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA (Y.H.); and Department of Radiation Oncology, Mayo Clinic, Rochester, MN (A.B., E.L.M., D.K.E., D.M.R., S.S., C.L.H., B.E.B., M.W.).

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This study shows that RadOnc-generative pretrained transformer (GPT) can assist in generating responses for prostate cancer patient messages, potentially reducing clinical team workload. While comparable in quality, limitations like context and domain knowledge require further AI development.

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Area of Science:

  • Artificial Intelligence in Oncology
  • Clinical Workflow Optimization
  • Natural Language Processing Applications

Background:

  • In-basket messages are a significant source of clinical workload for healthcare teams.
  • Prostate cancer treatment requires timely and accurate patient communication.
  • Large language models (LLMs) show promise in automating healthcare tasks.

Purpose of the Study:

  • To evaluate the effectiveness of RadOnc-GPT, a GPT-4 based LLM, in assisting with in-basket message response generation for prostate cancer treatment.
  • To assess the potential of AI in reducing clinical care team workload and time while maintaining response quality.

Main Methods:

  • RadOnc-GPT was integrated with electronic health records and evaluated on 158 in-basket message interactions from prostate cancer patients.
  • Quantitative natural language processing analysis and grading studies by 5 clinicians and 4 nurses assessed response completeness, correctness, clarity, empathy, and editing time.
  • The study period for grading was from July 20, 2024, to December 15, 2024.

Main Results:

  • RadOnc-GPT achieved comparable scores to clinicians in completeness, correctness, and clarity, and slightly outperformed them in empathy.
  • Identified limitations included lack of context, insufficient domain-specific knowledge, inability to perform meta-tasks, and hallucination.
  • Estimated time savings: 5.2 minutes per message for nurses and 2.4 minutes for clinicians.

Conclusions:

  • RadOnc-GPT demonstrates potential to significantly reduce clinical team workload in managing in-basket messages.
  • High-quality, timely AI-generated responses can improve healthcare efficiency and potentially reduce costs.
  • Further development is needed to address identified limitations for broader AI integration in clinical practice.