ArticleJournal of the American Medical Informatics Association : JAMIA2023
DEPLOYR: a technical framework for deploying custom real-time machine learning models into the electronic medical record.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards Electronic Medical Record (EMR) utilization in developing countries: a conceptual model based on systematic literature review.BMC medical informatics and decision making · 2026Pooled it
- A five-phase evaluation framework for diagnostic and predictive medical artificial intelligence.NPJ digital medicine · 2026Article
- A systematic review on deep learning techniques for diabetic retinopathy classification in retinal fundus images.iScience · 2026Article
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- SmartAlert - Implementing Machine Learning-Driven Clinical Decision Support for Inpatient Laboratory Utilization Reduction.NEJM AI · 2026Article
- Digital Twins as the Implementation Layer of Precision Medicine in Pediatric Neurosurgery.Journal of Korean Neurosurgical Society · 2026Review
- Gaps in artificial intelligence research for rural health in the United States: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- A data-centric perspective on designing AI foundation models for healthcare.Frontiers in digital health · 2026Article
- Operationalizing clinical artificial intelligence through secure FHIR pipelines: a zero-trust cloud reference architecture for EHR interoperability.Frontiers in digital health · 2026Article
- In vitro to in vivo translation of artificial intelligence for clinical use: screening for acute coronary syndrome to identify ST-elevation myocardial infarction.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Development and validation of an explainable machine learning model for predicting postoperative venous thromboembolism in esophageal cancer.International journal of surgery (London, England) · 2026Article
- Anna: an open-source platform for real-time integration of machine learning classifiers with veterinary electronic health records.BMC veterinary research · 2025Article
- Real time machine learning prediction of next generation sequencing test results in live clinical settings.NPJ digital medicine · 2025Article
- Monitoring strategies for continuous evaluation of deployed clinical prediction models.Journal of biomedical informatics · 2025Article
- Performance Degradation between Development and Deployment of a Predictive Model for Central Line-Associated Bloodstream Infections in Hospitalized Children.Applied clinical informatics · 2025Article
- AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- The promises and limitations of artificial intelligence for quality improvement, patient safety, and research in hospital medicine.Journal of hospital medicine · 2025Article
- Systematic Exploration of Hospital Cost Variability: A Conformal Prediction-Based Outlier Detection Method for Electronic Health Records.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Predictive models for post-ERCP pancreatitis: a systematic review and meta-analysis.Frontiers in gastroenterology (Lausanne, Switzerland) · 2025Review
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Authors and funding
9 authors.
Funding
Abstract
objectiveHeatlhcare institutions are establishing frameworks to govern and promote the implementation of accurate, actionable, and reliable machine learning models that integrate with clinical workflow. Such governance frameworks require an accompanying technical framework to deploy models in a resource efficient, safe and high-quality manner. Here we present DEPLOYR, a technical framework for enabling real-time deployment and monitoring of researcher-created models into a widely used electronic medical record system. MATERIALS AND
methodsWe discuss core functionality and design decisions, including mechanisms to trigger inference based on actions within electronic medical record software, modules that collect real-time data to make inferences, mechanisms that close-the-loop by displaying inferences back to end-users within their workflow, monitoring modules that track performance of deployed models over time, silent deployment capabilities, and mechanisms to prospectively evaluate a deployed model's impact.
resultsWe demonstrate the use of DEPLOYR by silently deploying and prospectively evaluating 12 machine learning models trained using electronic medical record data that predict laboratory diagnostic results, triggered by clinician button-clicks in Stanford Health Care's electronic medical record. DISCUSSION: Our study highlights the need and feasibility for such silent deployment, because prospectively measured performance varies from retrospective estimates. When possible, we recommend using prospectively estimated performance measures during silent trials to make final go decisions for model deployment.
conclusionMachine learning applications in healthcare are extensively researched, but successful translations to the bedside are rare. By describing DEPLOYR, we aim to inform machine learning deployment best practices and help bridge the model implementation gap.
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