Phase 1 Implementation of LLM-generated Discharge Summaries showing high Adoption in a Dutch Academic Hospital

Nettuno Nadalini; Tarannom Mehri; Anne H Hoekman; Katerina Kagialari; Job N Doornberg; Tom P van der Laan; Jacobien H F Oosterhoff; Rosanne C Schoonbeek; Charlotte M H H T Bootsma-Robroeks

arXiv · 2604.19774

Phase 1 Implementation of LLM-generated Discharge Summaries showing high Adoption in a Dutch Academic Hospital

Nettuno Nadalini·Tarannom Mehri·Anne H Hoekman·Katerina Kagialari·Job N Doornberg·Tom P van der Laan·Jacobien H F Oosterhoff·Rosanne C Schoonbeek·Charlotte M H H T Bootsma-Robroeks

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Abstract

Writing discharge summaries to transfer medical information is an important but time-consuming process that can be assisted by Large Language Models (LLMs). This prospective mixed methods pilot study evaluated an Electronic Health Record (EHR)-integrated LLM to generate discharge summaries drafts. In total, 379 discharge summaries were generated in clinical practice by 21 residents and 4 physician assistants during 9 weeks in our academic hospital. LLM-generated text was copied in 58.5% of admissions, and identifiable LLM content could be traced to 29.1% of final discharge letters. Notably, 86.9% of users self-reported a reduction in documentation time, and 60.9% a reduction in administrative workload. Intent to use after the pilot phase was high (91.3%), supporting further implementation of this use-case. Accurately measuring the documentation time of users on discharge summaries remains challenging, but will be necessary for future extrinsic evaluation of LLM-assisted documentation.

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Nettuno Nadalini, Tarannom Mehri, Anne H Hoekman, Katerina Kagialari, Job N Doornberg, Tom P van der Laan, Jacobien H F Oosterhoff, Rosanne C Schoonbeek, Charlotte M H H T Bootsma-Robroeks. 2026-03-27. Phase 1 Implementation of LLM-generated Discharge Summaries showing high Adoption in a Dutch Academic Hospital. https://arxiv.org/abs/2604.19774

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