ArticleJMIR human factors2025
Comparison of a Novel Machine Learning-Based Clinical Query Platform With Traditional Guideline Searches for Hospital Emergencies: Prospective Pilot Study of User Experience and Time Efficiency.
Article in JMIR human factors, 2025. 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.
- Understanding psychiatrist readiness for AI: a study of access, self-efficacy, trust, and design expectations.BMC health services research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unlabelled: Background: Emergency and acute medicine doctors require easily accessible evidence-based information to safely manage a wide range of clinical presentations. The inability to find evidence-based local guidelines on the trust's intranet leads to information retrieval from the World Wide Web. Artificial intelligence (AI) has the potential to make evidence-based information retrieval faster and easier. Objective: The aim of the study is to conduct a time-motion analysis, comparing cohorts of junior doctors using (1) an AI-supported search engine versus (2) the traditional hospital intranet. The study also aims to examine the impact of the AI-supported search engine on the duration of searches and workflow when seeking answers to clinical queries at the point of care. Methods: This pre- and postobservational study was conducted in 2 phases. In the first phase, clinical information searches by 10 doctors caring for acutely unwell patients in acute medicine were observed during 10 working days. Based on these findings and input from a focus group of 14 clinicians, an AI-supported, context-sensitive search engine was implemented. In the second phase, clinical practice was observed for 10 doctors for an additional 10 working days using the new search engine. Results: The hospital intranet group (n=10) had a median of 23 months of clinical experience, while the AI-supported search engine group (n=10) had a median of 54 months. Participants using the AI-supported engine conducted fewer searches. User satisfaction and query resolution rates were similar between the 2 phases. Searches with the AI-supported engine took 43 seconds longer on average. Clinicians rated the new app with a favorable Net Promoter Score of 20. Conclusions: We report a successful feasibility pilot of an AI-driven search engine for clinical guidelines. Further development of the engine including the incorporation of large language models might improve accuracy and speed. More research is required to establish clinical impact in different user groups. Focusing on new staff at beginning of their post might be the most suitable study design.
Indexed as
Identifiers
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.