Retrospective Comparative Analysis of Prostate Cancer In-Basket Messages: Responses from Closed-Domain LLM vs Clinical Teams
<|reference_start|>Retrospective Comparative Analysis of Prostate Cancer In-Basket Messages: Responses from Closed-Domain LLM vs Clinical Teams: In-basket message interactions play a crucial role in physician-patient communication, occurring during all phases (pre-, during, and post) of a patient's care journey. However, responding to these patients' inquiries has become a significant burden on healthcare workflows, consuming considerable time for clinical care teams. To address this, we introduce RadOnc-GPT, a specialized Large Language Model (LLM) powered by GPT-4 that has been designed with a focus on radiotherapeutic treatment of prostate cancer with advanced prompt engineering, and specifically designed to assist in generating responses. We integrated RadOnc-GPT with patient electronic health records (EHR) from both the hospital-wide EHR database and an internal, radiation-oncology-specific database. RadOnc-GPT was evaluated on 158 previously recorded in-basket message interactions. Quantitative natural language processing (NLP) analysis and two grading studies with clinicians and nurses were used to assess RadOnc-GPT's responses. Our findings indicate that RadOnc-GPT slightly outperformed the clinical care team in "Clarity" and "Empathy," while achieving comparable scores in "Completeness" and "Correctness." RadOnc-GPT is estimated to save 5.2 minutes per message for nurses and 2.4 minutes for clinicians, from reading the inquiry to sending the response. Employing RadOnc-GPT for in-basket message draft generation has the potential to alleviate the workload of clinical care teams and reduce healthcare costs by producing high-quality, timely responses.<|reference_end|>
@article{hao2024retrospective,
    title={Retrospective Comparative Analysis of Prostate Cancer In-Basket
  Messages: Responses from Closed-Domain LLM vs. Clinical Teams},
    author={Yuexing Hao, Jason M. Holmes, Jared Hobson, Alexandra Bennett, Daniel
  K. Ebner, David M. Routman, Satomi Shiraishi, Samir H. Patel, Nathan Y. Yu,
  Chris L. Hallemeier, Brooke E. Ball, Mark R. Waddle, and Wei Liu},
    journal={arXiv preprint arXiv:2409.18290},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18290},
    primaryClass={cs.AI cs.CY}
}