ArticleCureus2026
Clinician Use of a General-Purpose Large Language Model in Hospital Medicine: A Mixed-Methods Pilot Study.
Article in Cureus, 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.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Large language models (LLMs) are entering clinical practice, but real-world use in hospital medicine is not well described. Methods We conducted a two-month pilot of a general-purpose LLM application in a hospital medicine division. Two cross-sectional anonymous surveys were administered - one at pilot initiation and one at pilot conclusion. Results Response rates were 71.4% (initial) and 48.6% (end of pilot). Reported use was high at both timepoints (18/20 [90.0%] vs 15/17 [88.2%]); median prompts per workday were 2.0 (IQR, 1.0-6.3) and 1.5 (IQR, 1.0-2.0), respectively. Net Promoter Score was +20.0 at initiation and +11.8 at pilot conclusion. Early use emphasized clinical reasoning and administrative tasks; later use shifted toward documentation, summarization, and research work. Efficiency and time savings were the dominant perceived benefit, whereas hallucinations, source transparency, and privacy concerns were the main risks. Conclusions General-purpose LLM applications may offer near-term value as supervised tools for lower-risk workflows, but clinical implementation should include structured governance, training, and ongoing evaluation.
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