{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T09:23:10Z","timestamp":1787044990979,"version":"build-2736575974"},"reference-count":65,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Patients often struggle to understand standard hospital discharge letters, increasing the risk of medication errors and misunderstandings. According to cognitive load theory (CLT), complex, information-dense texts can overload working memory and impair comprehension. Artificial intelligence tools that generate patient-centered versions could help reduce extraneous cognitive load and bridge this gap. However, evidence for their effectiveness remains limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to evaluate whether GPT-4 (OpenAI)\u2013generated patient-centered letters improve standardized patients\u2019 retention and understanding of safety-relevant medical information compared with standard hospital discharge letters, and to explore potential effects on cognitive load as described by CLT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this prospective, randomized, crossover study, 48 trained standardized patients received a conventional discharge letter for an assigned disease (out of 3) and its matching GPT-4\u2013generated patient-centered letter. Participants read one version first, identified predefined safety-relevant \u201clearning objectives,\u201d and then repeated the task with the alternate version. The primary outcome was the proportion of learning objectives fully, partially, or not reported. In a secondary analysis, results were stratified by content field (Medication, Organization, Prevention of Complications, Lifestyle\/Disease Management) and Bloom taxonomy level (\u201cRemember,\u201d \u201cUnderstand\u201d).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The letter type significantly influenced comprehension (odds ratio [OR] 1.74, 95% CI 1.45-2.08; P&lt;.001). Patient letters, compared with discharge letters, led to higher rates of fully (490\/1073, 45.7% vs 413\/1073, 38.5%) or partially (322\/1073, 30% vs 287\/1073, 26.7%) stated learning objectives and fewer omissions (261\/1073, 24.3% vs 373\/1073, 34.8%). Participants performed better on \u201cRemember\u201d than on \u201cUnderstand\u201d learning objectives, regardless of letter type (OR 3.33, 95% CI 1.96-5.88; P&lt;.001). Compared with standard hospital discharge letters, patient letters consistently improved results at both cognitive levels (\u201cRemember\u201d: 278\/545, 51% vs 242\/545, 44.4%; \u201cUnderstand\u201d: 212\/528, 40.2% vs 171\/528, 32.4% fully stated). The effect of patient letters varied by content field (P&lt;.001). The greatest improvements were observed for \u201cMedication\u201d (170\/254, 66.9% vs 129\/254, 50.8% fully stated) and \u201cOrganization\u201d (78\/158, 49.4% vs 62\/158, 39.2% fully stated). Improvements in the content field \u201cPrevention of Complications\u201d were modest, and those for \u201cLifestyle\/Disease Management\u201d were even smaller across all conditions. A total of 24.3% (261\/1073) of key information remained unrecognized.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In this explanatory study, GPT-4\u2013generated patient letters improved comprehension of safety-relevant discharge information among standardized patients, particularly regarding medication and organizational aspects. However, they were less effective in supporting higher-order understanding, such as risk prevention or lifestyle management. These hypothesis-driven findings can be interpreted within a CLT framework and may motivate prospective evaluation of multimodal, iterative supports.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/81243","type":"journal-article","created":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T10:50:07Z","timestamp":1766573407000},"page":"e81243","source":"Crossref","is-referenced-by-count":4,"title":["Impact of GPT-4\u2013Generated Discharge Letters on Patients\u2019 Medical Comprehension: Prospective Crossover 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