Tripathi Satvik, Mutter Liam, Muppuri Meghana, Dheer Suhani, Garza-Frias Emiliano, Awan Komal, Jha Aakash, Dezube Michael, Tabari Azadeh, Bizzo Bernardo C, Dreyer Keith J, Bridge Christopher P, Daye Dania
Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States.
Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States.
Eur J Radiol. 2025 Jun;187:112124. doi: 10.1016/j.ejrad.2025.112124. Epub 2025 Apr 17.
The PRECISE framework, defined as Patient-Focused Radiology Reports with Enhanced Clarity and Informative Summaries for Effective Communication, leverages GPT-4 to create patient-friendly summaries of radiology reports at a sixth-grade reading level.
The purpose of the study was to evaluate the effectiveness of the PRECISE framework in improving the readability, reliability, and understandability of radiology reports. We hypothesized that the PRECISE framework improves the readability and patient understanding of radiology reports compared to the original versions.
The PRECISE framework was assessed using 500 chest X-ray reports. Readability was evaluated using the Flesch Reading Ease, Gunning Fog Index, and Automated Readability Index. Reliability was gauged by clinical volunteers, while understandability was assessed by non-medical volunteers. Statistical analyses including t-tests, regression analyses, and Mann-Whitney U tests were conducted to determine the significance of the differences in readability scores between the original and PRECISE-generated reports.
Readability scores significantly improved, with the mean Flesch Reading Ease score increasing from 38.28 to 80.82 (p-value < 0.001), the Gunning Fog Index decreasing from 13.04 to 6.99 (p-value < 0.001), and the ARI score improving from 13.33 to 5.86 (p-value < 0.001). Clinical volunteer assessments found 95 % of the summaries reliable, and non-medical volunteers rated 97 % of the PRECISE-generated summaries as fully understandable.
The application of the PRECISE approach demonstrates promise in enhancing patient understanding and communication without adding significant burden to radiologists. With improved reliability and patient-friendly summaries, this approach holds promise for fostering patient engagement and understanding in healthcare decision-making. The PRECISE framework represents a pivotal step towards more inclusive and patient-centric care delivery.
PRECISE框架被定义为“具有增强清晰度和信息丰富摘要以实现有效沟通的以患者为中心的放射学报告”，它利用GPT-4创建六年级阅读水平的患者友好型放射学报告摘要。
本研究的目的是评估PRECISE框架在提高放射学报告的可读性、可靠性和可理解性方面的有效性。我们假设，与原始版本相比，PRECISE框架可提高放射学报告的可读性和患者理解度。
使用500份胸部X光报告对PRECISE框架进行评估。使用弗莱什易读性指数、冈宁雾度指数和自动可读性指数评估可读性。由临床志愿者评估可靠性，由非医学志愿者评估可理解性。进行了包括t检验、回归分析和曼-惠特尼U检验在内的统计分析，以确定原始报告与PRECISE生成的报告在可读性分数上差异的显著性。
可读性分数显著提高，平均弗莱什易读性指数分数从38.28提高到80.82（p值<0.001），冈宁雾度指数从13.04降低到6.99（p值<0.001），自动可读性指数分数从13.33提高到5.86（p值<0.001）。临床志愿者评估发现95%的摘要可靠，非医学志愿者将97%的PRECISE生成的摘要评为完全可理解。
PRECISE方法的应用在增强患者理解和沟通方面显示出前景，而不会给放射科医生增加显著负担。随着可靠性的提高和患者友好型摘要的出现，这种方法有望促进患者在医疗决策中的参与和理解。PRECISE框架是朝着更具包容性和以患者为中心的护理提供迈出的关键一步。
