Trial reportJournal of medical Internet research2026
Impact of GPT-4-Generated Discharge Letters on Patients' Medical Comprehension: Prospective Crossover Study.
Trial report in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Why We Need Patients and Community at the Center of AI Health Communication Research.Journal of medical Internet research · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPatients 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.
objectiveThis study aimed to evaluate whether GPT-4 (OpenAI)-generated patient-centered letters improve standardized patients' 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.
methodsIn 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-generated patient-centered letter. Participants read one version first, identified predefined safety-relevant "learning objectives," 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 ("Remember," "Understand").
resultsThe letter type significantly influenced comprehension (odds ratio [OR] 1.74, 95% CI 1.45-2.08; P<.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 "Remember" than on "Understand" learning objectives, regardless of letter type (OR 3.33, 95% CI 1.96-5.88; P<.001). Compared with standard hospital discharge letters, patient letters consistently improved results at both cognitive levels ("Remember": 278/545, 51% vs 242/545, 44.4%; "Understand": 212/528, 40.2% vs 171/528, 32.4% fully stated). The effect of patient letters varied by content field (P<.001). The greatest improvements were observed for "Medication" (170/254, 66.9% vs 129/254, 50.8% fully stated) and "Organization" (78/158, 49.4% vs 62/158, 39.2% fully stated). Improvements in the content field "Prevention of Complications" were modest, and those for "Lifestyle/Disease Management" were even smaller across all conditions. A total of 24.3% (261/1073) of key information remained unrecognized.
conclusionsIn this explanatory study, GPT-4-generated 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.
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