Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Apr 19, 2023
Open Peer Review Period: Apr 19, 2023 - Jun 14, 2023
Date Accepted: Jun 17, 2023
(closed for review but you can still tweet)
Machine learning-enabled clinical information systems using fast healthcare interoperability data standards: a scoping review
ABSTRACT
Background:
Machine Learning-Enabled Clinical Information Systems (ML-CIS) have the potential to drive healthcare delivery and research. The Fast Healthcare Interoperability Resources (FHIR) data standard is increasingly applied in developing these systems. However, methods for applying FHIR to ML-CIS 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-CIS.
Methods:
Embase, PubMed, and Web of Science were searched for articles describing machine-learning systems 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 were compared across systems.
Results:
39 articles describing FHIR-based ML-CIS were divided into three categories according to their primary focus: Clinical Decision Support Systems (CDSSs) (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. Most intelligent systems lacked electronic health record interoperability and externally validated evidence of clinical efficacy.
Conclusions:
Shortcomings in current ML-CIS can be addressed by incorporating modular and interoperable data management, analytic platforms, secure inter-institutional data exchange, and application programming interfaces with adequate scalability to support both real-time and prospective clinical applications using electronic health record platforms with diverse implementations. Clinical Trial: n/a
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