ArticleJournal of the American Medical Informatics Association : JAMIA2021
Clinician involvement in research on machine learning-based predictive clinical decision support for the hospital setting: A scoping review.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 47 papers, 3 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
47 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Understanding "Alert Fatigue" in Primary Care: Qualitative Systematic Review of General Practitioners Attitudes and Experiences of Clinical Alerts, Prompts, and Reminders.Journal of medical Internet research · 2025Pooled it
- Inclusion of Clinicians in the Development and Evaluation of Clinical Artificial Intelligence Tools: A Systematic Literature Review.Frontiers in psychology · 2022Pooled it
- Machine Learning in Medical Emergencies: a Systematic Review and Analysis.Journal of medical systems · 2021Pooled it
- Methodological innovations to advance substance use disorder research: Proceedings of a NIDA workshop on target trial emulation and translational testing of digital health tools.Journal of substance use and addiction treatment · 2026Article
- Participatory Design of AI-Based Clinical Decision Support Systems: Scoping Review.JMIR medical informatics · 2026Article
- Zero-Shot Classification of Postoperative Complications From Real-World Discharge Letters According to the Clavien-Dindo System Using Large Language Models in Liver Surgery: Comparative Study.Journal of medical Internet research · 2026Article
- Advancing Human-Centered AI in Clinical Decision Support: Sociocognitive Human-in-the-Loop Study in HIV Care.Journal of medical Internet research · 2026Article
- Bridging the governance gap in health artificial intelligence: integrating nursing perspectives into the National Academy of Medicine Artificial Intelligence Code of Conduct.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Decision Curve Analysis for Evaluating Machine Learning Models for Next-Day Transfer Out of ICU.medRxiv : the preprint server for health sciences · 2026Article
- Optimizing Hospital Discharge Planning: Empirical Insights and Requirements of AI-Based Technologies From an Explorative Mixed Methods Field Study.JMIR formative research · 2026Article
- Perceptions and knowledge of machine learning for paediatric related decision support in emergency care - A UK and Ireland network survey study of clinician leaders.PLOS digital health · 2026Article
- Fostering Multidisciplinary Collaboration in Artificial Intelligence and Machine Learning Education: Tutorial Based on the AI-READI Bootcamp.JMIR medical education · 2025Article
- A Responsible Framework for Assessing, Selecting, and Explaining Machine Learning Models in Cardiovascular Disease Outcomes Among People With Type 2 Diabetes: Methodology and Validation Study.JMIR medical informatics · 2025Article
- Anticipatory moral distress in machine learning-based clinical decision support tool development: A qualitative analysis.SSM. Qualitative research in health · 2025Article
- Machine Learning Clinical Decision Support for Interdisciplinary Multimodal Chronic Musculoskeletal Pain Treatment: Prospective Pilot Study of Patient Assessment and Prognostic Profile Validation.JMIR rehabilitation and assistive technologies · 2025Article
- Artificial intelligence in clinical medicine: a state-of-the-art overview of systematic reviews with methodological recommendations for improved reporting.Frontiers in digital health · 2025Review
- Optimizing Nursing Communication for Symptom Management in Hemodialysis: Development of an Artificial Intelligence-Based Web Predictive Model for Burden Classification and Evidence Navigation.Journal of nursing management · 2025Article
- Deployable machine learning-based decision support system for tracheostomy in acute burn patients.Burns & trauma · 2025Article
- Applying the Non-Adoption, Abandonment, Scale-up, Spread, and Sustainability Framework Across Implementation Stages to Identify Key Strategies to Facilitate Clinical Decision Support System Integration Within a Large Metropolitan Health Service: Interview and Focus Group Study.JMIR medical informatics · 2024Article
- Challenges and Facilitation Approaches for the Participatory Design of AI-Based Clinical Decision Support Systems: Protocol for a Scoping Review.JMIR research protocols · 2024Article
Corrections and comments
- Commented on by
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- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
objectiveThe study sought to describe the prevalence and nature of clinical expert involvement in the development, evaluation, and implementation of clinical decision support systems (CDSSs) that utilize machine learning to analyze electronic health record data to assist nurses and physicians in prognostic and treatment decision making (ie, predictive CDSSs) in the hospital. MATERIALS AND
methodsA systematic search of PubMed, CINAHL, and IEEE Xplore and hand-searching of relevant conference proceedings were conducted to identify eligible articles. Empirical studies of predictive CDSSs using electronic health record data for nurses or physicians in the hospital setting published in the last 5 years in peer-reviewed journals or conference proceedings were eligible for synthesis. Data from eligible studies regarding clinician involvement, stage in system design, predictive CDSS intention, and target clinician were charted and summarized.
resultsEighty studies met eligibility criteria. Clinical expert involvement was most prevalent at the beginning and late stages of system design. Most articles (95%) described developing and evaluating machine learning models, 28% of which described involving clinical experts, with nearly half functioning to verify the clinical correctness or relevance of the model (47%). DISCUSSION: Involvement of clinical experts in predictive CDSS design should be explicitly reported in publications and evaluated for the potential to overcome predictive CDSS adoption challenges.
conclusionsIf present, clinical expert involvement is most prevalent when predictive CDSS specifications are made or when system implementations are evaluated. However, clinical experts are less prevalent in developmental stages to verify clinical correctness, select model features, preprocess data, or serve as a gold standard.
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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.