What’s Going On With Me and How Can I Better Manage My Health? The Potential of GPT-4 to Transform Discharge Letters Into Patient-Centered Letters to Enhance Patient Safety: Prospective, Exploratory Study

• DOI: 10.2196/67143
• Publication Date: 2024-11-29T00:05:55Z
• Authors: Felix Eisinger; Friederike Holder...; Moritz Mahling; Christian Stegema...; Anne Herrmann–Werner; Eric Nazarenus; Alessandra Sonanini; Martina Guthoff; Carsten Eickhoff; Martin Holderried
• Keywords: Original Paper; Computer applications to medicine. Medical informatics; R858-859.7; Public aspects of medicine; RA1-1270

Background For hospitalized patients, the discharge letter serves as a crucial source of medical information, outlining important discharge instructions and health management tasks. However, these letters are often written in professional jargon, making them difficult for patients with limited medical knowledge to understand. Large language models, such as GPT, have the potential to transform these discharge summaries into patient-friendly letters, improving accessibility and understanding.

Objective This study aims to use GPT-4 to convert discharge letters into more readable patient-centered letters. We evaluated how effectively and comprehensively GPT-4 identified and transferred patient safety–relevant information from the discharge letters to the transformed patient letters.

Methods Three discharge letters were created based on common medical conditions, containing 72 patient safety–relevant pieces of information, referred to as “learning objectives.” GPT-4 was prompted to transform these discharge letters into patient-centered letters. The resulting patient letters were analyzed for medical accuracy, patient centricity, and the ability to identify and translate the learning objectives. Bloom’s taxonomy was applied to analyze and categorize the learning objectives.

Results GPT-4 addressed the majority (56/72, 78%) of the learning objectives from the discharge letters. However, 11 of the 72 (15%) learning objectives were not included in the majority of the patient-centered letters. A qualitative analysis based on Bloom’s taxonomy revealed that learning objectives in the “Understand” category (9/11) were more frequently omitted than those in the “Remember” category (2/11). Most of the missing learning objectives were related to the content field of “prevention of complications.” By contrast, learning objectives regarding “lifestyle” and “organizational” aspects were addressed more frequently. Medical errors were found in a small proportion of sentences (31/787, 3.9%). In terms of patient centricity, the patient-centered letters demonstrated better readability than the discharge letters. Compared with discharge letters, they included fewer medical terms (132/860, 15.3%, vs 165/273, 60/4%), fewer abbreviations (43/860, 5%, vs 49/273, 17.9%), and more explanations of medical terms (121/131, 92.4%, vs 0/165, 0%).

Conclusions Our study demonstrates that GPT-4 has the potential to transform discharge letters into more patient-centered communication. While the readability and patient centricity of the transformed letters are well-established, they do not fully address all patient safety–relevant information, resulting in the omission of key aspects. Further optimization of prompt engineering may help address this issue and improve the completeness of the transformation.