Enhancing Physician-Patient Communication in Oncology Using GPT-4 Through Simplified Radiology Reports: Multicenter Quantitative Study (Preprint)

Abstract

<sec> <title>BACKGROUND</title> Effective physician-patient communication is essential in clinical practice, especially in oncology, where radiology reports play a crucial role. These reports are often filled with technical jargon, making them challenging for patients to understand and affecting their engagement and decision-making. Large language models, such as GPT-4, offer a novel approach to simplifying these reports and potentially enhancing communication and patient outcomes. </sec> <sec> <title>OBJECTIVE</title> We aimed to assess the feasibility and effectiveness of using GPT-4 to simplify oncological radiology reports to improve physician-patient communication. </sec> <sec> <title>METHODS</title> In a retrospective study approved by the ethics review committees of multiple hospitals, 698 radiology reports for malignant tumors produced between October 2023 and December 2023 were analyzed. In total, 70 (10%) reports were selected to develop templates and scoring scales for GPT-4 to create simplified interpretative radiology reports (IRRs). Radiologists checked the consistency between the original radiology reports and the IRRs, while volunteer family members of patients, all of whom had at least a junior high school education and no medical background, assessed readability. Doctors evaluated communication efficiency through simulated consultations. </sec> <sec> <title>RESULTS</title> Transforming original radiology reports into IRRs resulted in clearer reports, with word count increasing from 818.74 to 1025.82 (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001), volunteers’ reading time decreasing from 674.86 seconds to 589.92 seconds (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001), and reading rate increasing from 72.15 words per minute to 104.70 words per minute (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001). Physician-patient communication time significantly decreased, from 1116.11 seconds to 745.30 seconds (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001), and patient comprehension scores improved from 5.51 to 7.83 (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001). </sec> <sec> <title>CONCLUSIONS</title> This study demonstrates the significant potential of large language models, specifically GPT-4, to facilitate medical communication by simplifying oncological radiology reports. Simplified reports enhance patient understanding and the efficiency of doctor-patient interactions, suggesting a valuable application of artificial intelligence in clinical practice to improve patient outcomes and health care communication. </sec>

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