ChatGPT-4 in Psychiatric Discharge Summaries: Toward a More Comprehensible and Emotionally Sensitive Clinical...

Psychiatric discharge summaries are vital for ensuring continuity of care, yet they are often written in technical language that can be difficult for patients to understand and may cause emotional distress or reinforce stigma. With increasing patient access to medical records, there is a pressing need to develop communication tools that are both comprehensible and emotionally safe.

This study aimed to evaluate the diagnostic fidelity, linguistic clarity, emotional sensitivity, treatment comprehension, and readability of psychiatric discharge summaries rewritten by ChatGPT-4 based on real clinical cases.

This was the first study in South America to examine the use of a generative language model for rewriting psychiatric discharge summaries. A mixed-methods, observational cross-sectional design was applied. Twenty-five anonymized clinical cases were rewritten using ChatGPT-4. Three psychiatrists independently assessed each AI-generated summary across four dimensions: diagnostic fidelity, clarity of language, perceived emotional risk, and understanding of treatment. Readability was evaluated using the Fernández-Huerta Index and the INFLESZ Scale. A thematic analysis of evaluators' written comments was also conducted.

Summaries generated by ChatGPT-4 were rated positively, particularly for clarity and treatment explanation. Significant improvements in readability were observed across all diagnostic groups (p < .001), with mean values surpassing recommended thresholds for general comprehension. However, five summaries remained below those thresholds, and some diagnostic inaccuracies were noted (e.g. omissions in bipolar disorder). Evaluators also highlighted emotionally charged or stigmatizing language in a few cases.

ChatGPT-4 can enhance the accessibility and emotional appropriateness of psychiatric discharge communication, supporting more patient-centered care. Nevertheless, professional oversight remains critical to ensure clinical accuracy and contextual sensitivity. Future research should include patient feedback, assess long-term outcomes, and explore hybrid human-AI collaboration models.