ArticleScientific reports2025
Shifts in emergency physicians' attitudes toward large language model-based documentation: a pre- and post-implementation study.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evidence on artificial intelligence-assisted clinical documentation and healthcare workers' emotional wellbeing at work: a scoping review.Frontiers in psychology · 2026Pooled it
- An evidence-informed framework for workforce well-being and burnout prevention in emergency medicine.Internal and emergency medicine · 2026Review
- Large language models in emergency medicine education: opportunities, challenges, and implementation pathways.Frontiers in public health · 2026Review
- A Performance-Based Rubric for Generative AI use in Medical Students' Research Tasks: Development and Initial Psychometric Evaluation.Journal of medical education and curricular developmentArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Large language models (LLMs) can assist physicians in writing medical notes more efficiently. This study evaluates whether using an LLM assistant for writing emergency department discharge notes can reduce doctors' workload and addresses concerns regarding the incorporation of AI in medical practice. Eight emergency doctors with an average experience of 12 years participated in our study. We surveyed them prior to, post 3 days, and post 5 weeks of their LLM usage. The results showed that doctors' concerns about using LLMs decreased significantly and remained low throughout the study period. Moreover, the LLM usage considerably reduced the perceived workload, with the time required to write each discharge note reduced by one-third of the original time. These findings demonstrate that doctors readily accept and benefit from LLM assistants in their daily practice. Our study provides the first real-world evidence of how doctors' attitudes toward AI assistants change over time in clinical settings, offering valuable insights into the future implementation of LLM-based documentation tools in healthcare.
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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.