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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.19774 |
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| _version_ | 1866911613814046720 |
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| author | Nadalini, Nettuno Mehri, Tarannom Hoekman, Anne H Kagialari, Katerina Doornberg, Job N van der Laan, Tom P Oosterhoff, Jacobien H F Schoonbeek, Rosanne C Bootsma-Robroeks, Charlotte M H H T |
| author_facet | Nadalini, Nettuno Mehri, Tarannom Hoekman, Anne H Kagialari, Katerina Doornberg, Job N van der Laan, Tom P Oosterhoff, Jacobien H F Schoonbeek, Rosanne C Bootsma-Robroeks, Charlotte M H H T |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19774 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Phase 1 Implementation of LLM-generated Discharge Summaries showing high Adoption in a Dutch Academic Hospital Nadalini, Nettuno Mehri, Tarannom Hoekman, Anne H Kagialari, Katerina Doornberg, Job N van der Laan, Tom P Oosterhoff, Jacobien H F Schoonbeek, Rosanne C Bootsma-Robroeks, Charlotte M H H T Computation and Language Artificial Intelligence 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. |
| title | Phase 1 Implementation of LLM-generated Discharge Summaries showing high Adoption in a Dutch Academic Hospital |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2604.19774 |