PRECISE framework: Enhanced radiology reporting with GPT for improved readability, reliability, and patient-centered care.
Authors
Affiliations (7)
Affiliations (7)
- 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.
- Department of Psychiatry, Brigham and Women's 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; Harvard Medical School, Boston, MA, United States.
- School of Arts & Sciences, University of Pennsylvania, Philadelphia PA, United States.
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Mass General Brigham Data Science Office (DSO), 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; Harvard Medical School, Boston, MA, United States. Electronic address: [email protected].
Abstract
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.