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Tailoring Discharge Summaries to Health Care Providers' Needs (Part 1 of the Framework and Implementation of AI Tools
Mieke Deschepper1, Helga Rogge1, Mathias Syx1
1Data Science Institute, Ghent University Hospital, Corneel Heymanslaan 10, Ghent, 9000, Belgium, +32 93321814.
Background:
Medical discharge letters are critical for continuity of care but often lack clarity and personalization, making it difficult for health care providers to retrieve essential information. While large language models (LLMs) offer potential for automating summary generation, their effectiveness depends heavily on the quality and contextual relevance of the prompts used.
Objective:
The objective of this study was to develop and describe a human-centered, replicable framework for creating individualized prompts that guide LLMs in generating summaries tailored to the specific needs of health care providers.
Methods:
A multidisciplinary workshop was conducted at Ghent University Hospital with 26 health care providers from 5 institutions, including hospitals and general practitioner networks. Participants brainstormed ideal summary formats, generating 170 ideas categorized into themes such as "structure," "medical history," "medication," and "follow-up." These insights informed the development of a 110-item structured questionnaire distributed to 33 participants. Responses were used to generate personalized and generic prompts, refined using the context, objective, style, tone, audience, and response (CO-STAR) framework.
Results:
"Structure and layout" (40/170, 23.5%) and "follow-up" (27/170, 15.9%) were the most emphasized categories in the workshop. The questionnaire confirmed the importance of the "follow-up" and "medical history" sections. Prompts were generated per participant and by health care type, incorporating frequently selected responses. The CO-STAR framework was applied to improve prompt clarity and alignment with clinical expectations. Communication emerged as a new category during the workshop and was universally valued in the questionnaire.
Conclusions:
This study presents a novel, systematic approach to prompt engineering in clinical artificial intelligence applications. By translating qualitative input into structured, individualized prompts, the framework is designed to improve the usability and relevance of artificial intelligence-generated summaries. It proposes a scalable approach for integrating human-centered design into LLM deployment in health care, with the aim of supporting more accurate, context-aware clinical documentation. However, these outcomes remain to be empirically validated; this study is limited to the design and implementation of a human-centered prompt construction pipeline in a specific multicenter setting.
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