ArticleApplied clinical informatics2021
Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring.
Article in Applied clinical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Toward a Learning Health Care System: A Systematic Review and Evidence-Based Conceptual Framework for Implementation of Clinical Analytics in a Digital Hospital.Applied clinical informatics · 2022Pooled it
- A randomized controlled trial of artificial intelligence-based analytics for clinical deterioration.Scientific reports · 2026Trial
- Identifying Barriers to The Implementation of Communicating Narrative Concerns Entered by Registered Nurses, An Early Warning System SmartApp.Applied clinical informatics · 2024Trial
- Advances in Remote Monitoring Technology Applications in Anesthesia: A Narrative Review.Medical science monitor : international medical journal of experimental and clinical research · 2026Review
- Ghost in the Machine: Leveraging artificial intelligence in the trauma bay.Trauma surgery & acute care open · 2026Review
- FetalMLOps: operationalizing machine learning models for standard fetal ultrasound plane classification.Medical & biological engineering & computing · 2026Article
- "Be Really Careful about That": Clinicians' Perceptions of an Intelligence Augmentation Tool for In-Hospital Deterioration Detection.Applied clinical informatics · 2025Article
- Critical activities for successful implementation and adoption of AI in healthcare: towards a process framework for healthcare organizations.Frontiers in digital health · 2025Article
- Machine Learning Operations in Health Care: A Scoping Review.Mayo Clinic proceedings. Digital health · 2024Review
- Barriers to and Facilitators of Artificial Intelligence Adoption in Health Care: Scoping Review.JMIR human factors · 2024Article
- A comprehensive overview of barriers and strategies for AI implementation in healthcare: Mixed-method design.PloS one · 2024Review
- Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature.Applied clinical informatics · 2023Review
- Beyond prediction: Off-target uses of artificial intelligence-based predictive analytics in a learning health system.Learning health systems · 2023Article
- Novel approaches to capturing and using continuous cardiorespiratory physiological data in hospitalized children.Pediatric research · 2023Review
- Artificial Intelligence Applications in Health Care Practice: Scoping Review.Journal of medical Internet research · 2022Article
- Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden.BMC health services research · 2022Article
- The principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICU.NPJ digital medicine · 2022Review
- External validation of a novel signature of illness in continuous cardiorespiratory monitoring to detect early respiratory deterioration of ICU patients.Physiological measurement · 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A new development in the practice of medicine is Artificial Intelligence-based predictive analytics that forewarn clinicians of future deterioration of their patients. This proactive opportunity, though, is different from the reactive stance that clinicians traditionally take. Implementing these tools requires new ideas about how to educate clinician users to facilitate trust and adoption and to promote sustained use. Our real-world hospital experience implementing a predictive analytics monitoring system that uses electronic health record and continuous monitoring data has taught us principles that we believe to be applicable to the implementation of other such analytics systems within the health care environment. These principles are mentioned below:• To promote trust, the science must be understandable.• To enhance uptake, the workflow should not be impacted greatly.• To maximize buy-in, engagement at all levels is important.• To ensure relevance, the education must be tailored to the clinical role and hospital culture.• To lead to clinical action, the information must integrate into clinical care.• To promote sustainability, there should be periodic support interactions after formal implementation.
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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.