Comparative Evaluation of Resident-Written and GPT-5.2-Generated Ophthalmology Discharge Letters: A Retrospective Blinded Study

Background/Objectives: Discharge letters are essential for continuity of care but are often time-consuming to prepare and variable in quality. Large language models (LLMs) may help standardize and support this process, yet evidence in ophthalmology remains limited. This study compared the quality of resident-written and GPT-5.2-generated ophthalmology discharge letters derived from the same de-identified clinical data. Methods: This retrospective blinded study was conducted at a tertiary hospital in Croatia. For 146 consecutive inpatient discharges, original resident-written letters were paired with GPT-5.2-generated letters created using a standardized prompt; 142 complete pairs were available for the primary analysis. Three board-certified ophthalmologists evaluated anonymized letters using a structured assessment of accuracy, completeness, clarity/structure, tone/professional phrasing, conciseness, global quality, errors, omissions, and key content elements. Results: In the primary paired analysis, GPT-5.2-generated letters performed similarly to resident-written letters across accuracy, completeness, clarity/structure, errors, omissions, and overall quality. GPT-5.2-generated letters received higher ratings for tone/professional phrasing, whereas resident-written letters were rated as more concise, although inter-rater agreement was poor on these stylistic domains (at or below chance for conciseness) and these findings should therefore be interpreted as exploratory. Resident-written letters more often documented operations, while GPT-5.2-generated letters more consistently included findings. Reviewer-adjusted sensitivity analyses were less favorable to GPT-5.2 for several domains. Conclusions: GPT-5.2-generated ophthalmology discharge letters showed similar performance to resident-written letters in several evaluated domains in the primary paired analysis, but differences in specific content elements and less favorable sensitivity analyses indicate that clinician oversight remains necessary to ensure accuracy, procedural completeness, and clinical usability.

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Publication Details

Journal
Informatics
Published
2026-06-18
DOI
https://doi.org/10.3390/informatics13060093
Primary Topic
Healthcare Systems and Technology
Type
article
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article

Comparative Evaluation of Resident-Written and GPT-5.2-Generated Ophthalmology Discharge Letters: A Retrospective Blinded Study

Ljubo Znaor, Ante Krešo, Bruno Markioli, Josip Vrdoljak et al.
Informatics
Healthcare Systems and Technology
article

Comparative Evaluation of Resident-Written and GPT-5.2-Generated Ophthalmology Discharge Letters: A Retrospective Blinded Study

Ljubo Znaor, Ante Krešo, Bruno Markioli, Josip Vrdoljak, Ana Marušić, Jozefina Josipa Dukic, Darko Batistic, Filip Rada, Bosko Jaksic, Zrinka Aracic-Jaksic
article en

Abstract

Background/Objectives: Discharge letters are essential for continuity of care but are often time-consuming to prepare and variable in quality. Large language models (LLMs) may help standardize and support this process, yet evidence in ophthalmology remains limited. This study compared the quality of resident-written and GPT-5.2-generated ophthalmology discharge letters derived from the same de-identified clinical data. Methods: This retrospective blinded study was conducted at a tertiary hospital in Croatia. For 146 consecutive inpatient discharges, original resident-written letters were paired with GPT-5.2-generated letters created using a standardized prompt; 142 complete pairs were available for the primary analysis. Three board-certified ophthalmologists evaluated anonymized letters using a structured assessment of accuracy, completeness, clarity/structure, tone/professional phrasing, conciseness, global quality, errors, omissions, and key content elements. Results: In the primary paired analysis, GPT-5.2-generated letters performed similarly to resident-written letters across accuracy, completeness, clarity/structure, errors, omissions, and overall quality. GPT-5.2-generated letters received higher ratings for tone/professional phrasing, whereas resident-written letters were rated as more concise, although inter-rater agreement was poor on these stylistic domains (at or below chance for conciseness) and these findings should therefore be interpreted as exploratory. Resident-written letters more often documented operations, while GPT-5.2-generated letters more consistently included findings. Reviewer-adjusted sensitivity analyses were less favorable to GPT-5.2 for several domains. Conclusions: GPT-5.2-generated ophthalmology discharge letters showed similar performance to resident-written letters in several evaluated domains in the primary paired analysis, but differences in specific content elements and less favorable sensitivity analyses indicate that clinician oversight remains necessary to ensure accuracy, procedural completeness, and clinical usability.

InformaticsVol. 13(6)
Croatian Hydrographic Institute (HR), University of Split (HR)
Openalex Percentile: Top 4%
Healthcare Systems and Technology
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