ArticleJMIR medical informatics2023
Machine Learning-Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review.
Article in JMIR medical informatics, 2023. 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.
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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Electronic health records: managerial insights from an umbrella review.BMC health services research · 2026Pooled it
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- Recommendations for HLA Genotyping Data Standards and Clinical Laboratory Staffing Considerations.HLA · 2026Review
- Artificial Intelligence in Rhinoplasty Recovery: Linguistic Intelligence and Machine Learning-Driven Insights.Journal of clinical medicine · 2026Article
- Physiologic phenotypes in blunt thoracic aortic injury: implications for risk stratification and surgical decision-making using machine learning.Frontiers in digital health · 2026Article
- Article
- SQL on FHIR - Tabular views of FHIR data using FHIRPath.NPJ digital medicine · 2025Article
- Data Interoperability in Context: The Importance of Open-Source Implementations When Choosing Open Standards.Journal of medical Internet research · 2025Article
- Era of Generalist Conversational Artificial Intelligence to Support Public Health Communications.Journal of medical Internet research · 2025Article
- A Review on Integrating Breast Cancer Clinical Data: A Unified Platform Perspective.Current treatment options in oncology · 2025Review
- Artificial intelligence in heart failure - a comprehensive literature review.Cardiology journal · 2025Review
- An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation.Proceedings of machine learning research · 2024Article
- Quantitative texture analysis using machine learning for predicting interpretable pulmonary perfusion from non-contrast computed tomography in pulmonary embolism patients.Respiratory research · 2024Article
- State-of-the-Art Fast Healthcare Interoperability Resources (FHIR)-Based Data Model and Structure Implementations: Systematic Scoping Review.JMIR medical informatics · 2024Article
- Implementation of the World Health Organization Minimum Dataset for Emergency Medical Teams to Create Disaster Profiles for the Indonesian SATUSEHAT Platform Using Fast Healthcare Interoperability Resources: Development and Validation Study.JMIR medical informatics · 2024Article
- INSPIRE datahub: a pan-African integrated suite of services for harmonising longitudinal population health data using OHDSI tools.Frontiers in digital health · 2024Review
- Building digital patient pathways for the management and treatment of multiple sclerosis.Frontiers in immunology · 2024Review
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
Background: Machine learning-enabled clinical information systems (ML-CISs) have the potential to drive health care delivery and research. The Fast Healthcare Interoperability Resources (FHIR) data standard has been increasingly applied in developing these systems. However, methods for applying FHIR to ML-CISs are variable. Objective: This study evaluates and compares the functionalities, strengths, and weaknesses of existing systems and proposes guidelines for optimizing future work with ML-CISs. Methods: Embase, PubMed, and Web of Science were searched for articles describing machine learning systems that were used for clinical data analytics or decision support in compliance with FHIR standards. Information regarding each system's functionality, data sources, formats, security, performance, resource requirements, scalability, strengths, and limitations was compared across systems. Results: A total of 39 articles describing FHIR-based ML-CISs were divided into the following three categories according to their primary focus: clinical decision support systems (n=18), data management and analytic platforms (n=10), or auxiliary modules and application programming interfaces (n=11). Model strengths included novel use of cloud systems, Bayesian networks, visualization strategies, and techniques for translating unstructured or free-text data to FHIR frameworks. Many intelligent systems lacked electronic health record interoperability and externally validated evidence of clinical efficacy. Conclusions: Shortcomings in current ML-CISs can be addressed by incorporating modular and interoperable data management, analytic platforms, secure interinstitutional data exchange, and application programming interfaces with adequate scalability to support both real-time and prospective clinical applications that use electronic health record platforms with diverse implementations.
Indexed as
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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.