ArticleCureus2026
Artificial Intelligence for Hospital Scheduling: A Simple, Reproducible, and Effective Model.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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
1 citing paper in PubMed.
- Automating Rule-Compliant and Equitable Call Schedules for Orthopedic Surgery Residents With Artificial Intelligence and Large Language Models: A Simulation-Based Validation Study.Journal of medical education and curricular developmentArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hospital scheduling, particularly for on-call shifts and daily assignments, is a complex task that must account for numerous factors, such as service requirements, staff preferences, and unplanned absences. Traditional methods often result in significant administrative burden and can lead to staff frustration, potentially affecting the quality of care. This study explores the use of artificial intelligence-based large language models (AI LLMs) to automate hospital scheduling through widely accessible tools, aiming to simplify the process, reduce manual effort, and enhance fairness. ChatGPT
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What OpenQuestion holds
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.