ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
Physician Usage and Acceptance of a Machine Learning Recommender System for Simulated Clinical Order Entry.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed.
- OrderRex clinical user testing: a randomized trial of recommender system decision support on simulated cases.Journal of the American Medical Informatics Association : JAMIA · 2020Trial
- Barriers to and facilitators of clinician acceptance and use of artificial intelligence in healthcare settings: a scoping review.BMJ open · 2025Article
- Limited echocardiogram acquisition by novice clinicians aided with deep learning: A randomized controlled trial.Biology methods & protocols · 2025Article
- Doctors' perception on the ethical use of AI-enabled clinical decision support systems for antibiotic prescribing recommendations in Singapore.Frontiers in public health · 2024Article
- Acquisition of Cardiac Point-of-Care Ultrasound Images With Deep Learning: A Randomized Trial for Educational Outcomes With Novices.CHEST pulmonary · 2023Article
- Graph-based clinical recommender: Predicting specialists procedure orders using graph representation learning.Journal of biomedical informatics · 2023Article
- Use, Impact, Weaknesses, and Advanced Features of Search Functions for Clinical Use in Electronic Health Records: A Scoping Review.Applied clinical informatics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Clinical decision support tools that automatically disseminate patterns of clinical orders have the potential to improve patient care by reducing errors of omission and streamlining physician workflows. However, it is unknown if physicians will accept such tools or how their behavior will be affected. In this randomized controlled study, we exposed 34 licensed physicians to a clinical order entry interface and five simulated emergency cases, with randomized availability of a previously developed clinical order recommender system. With the recommender available, physicians spent similar time per case (6.7 minutes), but placed more total orders (17.1 vs. 15.8). The recommender demonstrated superior recall (59% vs 41%) and precision (25% vs 17%) compared to manual search results, and was positively received by physicians recognizing workflow benefits. Further studies must assess the potential clinical impact towards a future where electronic health records automatically anticipate clinical needs.
Identifiers
32477627PMC7233080What 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.