ArticleJMIR AI2026
Implementation and User Evaluation of an On-Premise Large Language Model in a German University Hospital Setting: Cross-Sectional Survey.
Article in JMIR AI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
6 authors.
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Abstract
Background: Large language models (LLMs) are increasingly used by employees at university hospitals for information retrieval or decision support. Self-hosted on-premise systems provide a secure environment and conform to data privacy and security regulations for handling sensitive personal data. Automation of standard procedures using an LLM application can substantially reduce time-consuming administrative tasks and facilitate the analysis of large datasets. Objective: The objective of our study was to gather feedback from registered artificial intelligence (AI) users on the usability and common use cases of the on-premise LLM infrastructure we established at the University Medicine Magdeburg to optimize the models to the needs of our facility. Methods: We developed an online questionnaire to which registered AI users were given access and were informed via email. Results: Of 322 registered AI users, 98 (30.4%) participated in the user survey. After filtering incomplete responses, results from 91 (28.3%) participants remained for further analysis. Speed and quality received overall high approval rates. Most of the users (n=57, 62.6%) used the platform at least once per week, and 44% (n=40) of the users reported saving at least 30 minutes of work per week by using our AI platform. A diverse set of use cases was observed, varying by profession; for example, health care and research professionals used the AI platform more frequently for creation and analysis tasks than administrative staff. Conclusions: Our data indicate that the implementation of a self-hosted on-premise LLM was associated with positive perceptions among a diverse group of professionals working at a university hospital, saving time and meeting their individual needs.
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