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. 2023 Aug 24:11:e48297.
doi: 10.2196/48297.

Machine Learning-Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review

Jeremy A Balch #  1   2 , Matthew M Ruppert #  2   3 , Tyler J Loftus  1   2 , Ziyuan Guan  2   3 , Yuanfang Ren  2   3 , Gilbert R Upchurch  1 , Tezcan Ozrazgat-Baslanti  2   3 , Parisa Rashidi  2   4 , Azra Bihorac  2   3
Affiliations

Machine Learning-Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review

Jeremy A Balch et al. JMIR Med Inform. .

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.

Keywords: FHIR; Fast Healthcare Interoperability Resources; clinical decision support system; clinical informatics; decision support; information systems; interoperability; interoperable; machine learning; ontologies; ontology; review methodology; review methods; scoping review.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1.
Figure 1.. Sample model of a proposed machine learning–enabled clinical information system using FHIR data standards. AI: artificial intelligence; API: application programming interface; FHIR: Fast Healthcare Interoperability Resources; HL7: Health Level 7; IoT: Internet of Things; OMOP: Observational Medical Outcomes Partnership.
See this image and copyright information in PMC

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