Related papers: The impact of responding to patient messages with large language model assistance

The impact of responding to patient messages with large language model assistance

URL: http://arxiv.org/abs/2310.17703v2
Date: Wed, 29 Nov 2023 19:10:58 GMT
Title: The impact of responding to patient messages with large language model assistance
Authors: Shan Chen, Marco Guevara, Shalini Moningi, Frank Hoebers, Hesham Elhalawani, Benjamin H. Kann, Fallon E. Chipidza, Jonathan Leeman, Hugo J.W.L. Aerts, Timothy Miller, Guergana K. Savova, Raymond H. Mak, Maryam Lustberg, Majid Afshar, Danielle S. Bitterman
Abstract summary: Documentation burden is a major contributor to clinician burnout. Many hospitals are actively integrating such systems into electronic medical record systems. We are the first to examine the utility of large language models in assisting clinicians draft responses to patient questions.
Score: 4.243020918808522
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: Documentation burden is a major contributor to clinician burnout, which is rising nationally and is an urgent threat to our ability to care for patients. Artificial intelligence (AI) chatbots, such as ChatGPT, could reduce clinician burden by assisting with documentation. Although many hospitals are actively integrating such systems into electronic medical record systems, AI chatbots utility and impact on clinical decision-making have not been studied for this intended use. We are the first to examine the utility of large language models in assisting clinicians draft responses to patient questions. In our two-stage cross-sectional study, 6 oncologists responded to 100 realistic synthetic cancer patient scenarios and portal messages developed to reflect common medical situations, first manually, then with AI assistance. We find AI-assisted responses were longer, less readable, but provided acceptable drafts without edits 58% of time. AI assistance improved efficiency 77% of time, with low harm risk (82% safe). However, 7.7% unedited AI responses could severely harm. In 31% cases, physicians thought AI drafts were human-written. AI assistance led to more patient education recommendations, fewer clinical actions than manual responses. Results show promise for AI to improve clinician efficiency and patient care through assisting documentation, if used judiciously. Monitoring model outputs and human-AI interaction remains crucial for safe implementation.

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