Evidence map›Paper›PMID 42564949›Full record

ArticleCureus2026

Clinician Use of a General-Purpose Large Language Model in Hospital Medicine: A Mixed-Methods Pilot Study.

Tarek Souaid, Alexander J Ryu, Donna K Lawson, Lindsay Norgaard, Jennifer M Hovell, M Caroline Burton, Shant Ayanian

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Tarek SouaidHospital Internal Medicine, Mayo Clinic, Rochester, USA.
Alexander J RyuHospital Internal Medicine, Mayo Clinic, Rochester, USA.
Donna K LawsonHospital Internal Medicine, Mayo Clinic, Rochester, USA.
Lindsay NorgaardHospital Internal Medicine, Mayo Clinic, Rochester, USA.
Jennifer M HovellHospital Internal Medicine, Mayo Clinic, Rochester, USA.
M Caroline BurtonHospital Internal Medicine, Mayo Clinic, Rochester, USA.
Shant AyanianHospital Internal Medicine, Mayo Clinic, Rochester, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligencechatgptclinical workflowenterprise aihospital medicinelarge language modelpilot study

Identifiers

PMID42564949
PMCPMC13446756

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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.