Articlenpj health systems2026
Uses of generative AI by non-clinician staff at an academic medical center.
Article in npj health systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Large language model (LLM) chat tools have the potential to transform healthcare workflows by improving efficiency and reducing administrative burdens. While prior research has predominantly focused on clinicians, non-clinician healthcare staff constitute the majority of the workforce, and their real-world chat tool use remains uncharacterized. This retrospective, cross-sectional study analyzed de-identified chat logs from a secure, HIPAA-compliant LLM chat tool deployed at an academic medical center over an 11-month period. Among 30,503 chat threads analyzed, 98% originated from non-clinician users across 239 roles. Usage was dominated by administrative tasks including email and document writing (53.9%), text manipulation (9.1%), and brainstorming (6.7%). A notable proportion of interactions included off-label queries unrelated to work or organizational goals, including 5.9% involving clinical decision-making. These findings highlight the need for targeted training, tailored governance policies, and refined evaluation frameworks to optimize appropriate LLM use while mitigating risks in healthcare settings.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.