Comprehension of an AI-generated discharge letter versus a traditional discharge letter: two controlled quasi-experimental parallel-group studies

Clicks: 20
ID: 328440
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #101 of 164 articles by views in International journal for quality in health care : journal of the International Society for Quality in Health Care

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 164 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
BACKGROUND: Discharge letters are essential for continuity of care, yet patients often misunderstand their content. Poor discharge communication is associated with medication errors, inappropriate healthcare use, and hospital readmissions. Artificial intelligence (AI) language models may improve discharge documentation clarity and accessibility. This study aimed to compare a GPT-4-generated, accessibility-optimised discharge letter with a conventional discharge letter regarding comprehension of key discharge information, and to evaluate satisfaction and identify predictors of comprehension. METHODS: Two controlled quasi-experimental parallel-group studies were conducted. Study 1 included adults recruited online (n = 341 analysed), while Study 2 involved medical and nursing students at the University of Turin (n = 791 analysed). Allocation was based on age parity. The primary outcome was a structured comprehension score assessing diagnosis, treatment, investigations, and follow-up instructions. Secondary outcomes included readability, clarity, perceived comprehension, organisation, and satisfaction. Health literacy (HL) was assessed using the HLS-EU-Q6. Multiple linear regression analyses identified predictors of comprehension. RESULTS: Participants receiving the AI-generated discharge letter achieved significantly higher comprehension scores in both studies (Study 1: median 4 vs. 2; Study 2: median 4 vs. 3; both P < 0.001). Differences remained significant after adjustment for sociodemographic variables, HL, and healthcare background. AI-generated letters also improved all secondary outcomes (all P < 0.001). No significant improvement was observed for identification of follow-up appointment dates. Higher comprehension was associated with healthcare background, higher education, sufficient HL, and better perceived health status. CONCLUSION: AI-generated discharge letters significantly improved measured comprehension and satisfaction in both general and higher-literacy populations; validation using real clinical letters and diverse patients is required.
Reference Key
openalex_W7212369728 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Fabrizio Bert, Lorenzo Rosset, Francesco Conrado, Daniele Consoli, Giacomo Scaioli, Giuseppina Lo Moro, Roberta Siliquini
Are you one of these authors? Claim your profile
Journal International journal for quality in health care : journal of the International Society for Quality in Health Care
Year 2026
DOI
10.1093/intqhc/mzag133
URL
https://academic.oup.com/intqhc/advance-article-pdf/doi/10.1093/intqhc/mzag133/71046462/mzag133.pdf
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.