ArticleBMJ open2023
Implemented machine learning tools to inform decision-making for patient care in hospital settings: a scoping review.
Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled 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.
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
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- Barriers and facilitators to the implementation and adoption of computerised clinical decision support systems: an overview of reviews.Systematic reviews · 2026Pooled it
- Implementation of Machine Learning Applications in Health Care Organizations: Systematic Review of Empirical Studies.Journal of medical Internet research · 2024Pooled it
- AI in Neurological Health Care: Qualitative Study of Patient and Public Perceptions.Journal of medical Internet research · 2026Article
- Comprehensive recommendations for the implementation of artificial intelligence in healthcare: a narrative review on facilitators and barriers.BMJ open quality · 2026Review
- Applications of artificial intelligence for real-world evidence generation: a protocol for a living scoping review.BMJ open · 2026Article
- Artificial intelligence should genuinely support clinical reasoning and decision making to bridge the translational gap.NPJ digital medicine · 2025Review
- Article
- Toward real-world deployment of machine learning for health care: External validation, continual monitoring, and randomized clinical trials.Health care science · 2024Article
- The Unseen Hand: AI-Based Prescribing Decision Support Tools and the Evaluation of Drug Safety and Effectiveness.Drug safety · 2024Article
- A comprehensive overview of barriers and strategies for AI implementation in healthcare: Mixed-method design.PloS one · 2024Review
- Predicting Successful Placements for Youth in Child Welfare with Machine Learning.Children and youth services review · 2023Article
- Intervention Activities and Implementation Strategies for School-Based Health Promotion: Identifying Core Functions and Forms to Facilitate Scale-up of an Effective Intervention.Implementation research and practiceArticle
- Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews.Health services research and managerial epidemiologyReview
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
Abstract
objectivesTo identify ML tools in hospital settings and how they were implemented to inform decision-making for patient care through a scoping review. We investigated the following research questions: What ML interventions have been used to inform decision-making for patient care in hospital settings? What strategies have been used to implement these ML interventions?
designA scoping review was undertaken. MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL) and the Cochrane Database of Systematic Reviews (CDSR) were searched from 2009 until June 2021. Two reviewers screened titles and abstracts, full-text articles, and charted data independently. Conflicts were resolved by another reviewer. Data were summarised descriptively using simple content analysis.
settingHospital setting. PARTICIPANT: Any type of clinician caring for any type of patient.
interventionMachine learning tools used by clinicians to inform decision-making for patient care, such as AI-based computerised decision support systems or "'model-based'" decision support systems. PRIMARY AND SECONDARY OUTCOME MEASURES: Patient and study characteristics, as well as intervention characteristics including the type of machine learning tool, implementation strategies, target population. Equity issues were examined with PROGRESS-PLUS criteria.
resultsAfter screening 17 386 citations and 3474 full-text articles, 20 unique studies and 1 companion report were included. The included articles totalled 82 656 patients and 915 clinicians. Seven studies reported gender and four studies reported PROGRESS-PLUS criteria (race, health insurance, rural/urban). Common implementation strategies for the tools were clinician reminders that integrated ML predictions (44.4%), facilitated relay of clinical information (17.8%) and staff education (15.6%). Common barriers to successful implementation of ML tools were time (11.1%) and reliability (11.1%), and common facilitators were time/efficiency (13.6%) and perceived usefulness (13.6%).
conclusionsWe found limited evidence related to the implementation of ML tools to assist clinicians with patient healthcare decisions in hospital settings. Future research should examine other approaches to integrating ML into hospital clinician decisions related to patient care, and report on PROGRESS-PLUS items.
fundingCanadian Institutes of Health Research (CIHR) Foundation grant awarded to SES and the CIHR Strategy for Patient Oriented-Research Initiative (GSR-154442). SCOPING REVIEW REGISTRATION: https://osf.io/e2mna.
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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.