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Machine Learning-Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data

Jeremy A Balch1,2, Matthew M Ruppert2,3, Tyler J Loftus1,2

  • 1Department of Surgery, University of Florida Health, Gainesville, FL, United States.

JMIR Medical Informatics
|August 30, 2023
PubMed
Summary

Machine learning-enabled clinical information systems (ML-CISs) using Fast Healthcare Interoperability Resources (FHIR) show promise but vary in application. Guidelines are proposed to optimize future ML-CIS development for better healthcare delivery and research.

Keywords:
FHIRFast Healthcare Interoperability Resourcesclinical decision support systemclinical informaticsdecision supportinformation systemsinteroperabilityinteroperablemachine learningontologiesontologyreview methodologyreview methodsscoping review

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Area of Science:

  • Health Informatics
  • Machine Learning in Healthcare
  • Clinical Information Systems

Background:

  • Machine learning-enabled clinical information systems (ML-CISs) are crucial for advancing healthcare delivery and research.
  • The Fast Healthcare Interoperability Resources (FHIR) standard is increasingly adopted in ML-CIS development.
  • Current methods for applying FHIR to ML-CISs exhibit variability.

Purpose of the Study:

  • To evaluate and compare existing FHIR-based ML-CIS functionalities, strengths, and weaknesses.
  • To propose guidelines for optimizing the development of future ML-CISs.
  • To identify best practices for integrating machine learning with FHIR standards in clinical settings.

Main Methods:

  • A systematic literature search was conducted across Embase, PubMed, and Web of Science.
  • Articles describing FHIR-compliant machine learning systems for clinical data analytics or decision support were analyzed.
  • Systems were compared based on functionality, data sources, formats, security, performance, resource needs, and scalability.

Main Results:

  • 39 articles on FHIR-based ML-CISs were categorized into decision support (n=18), data management (n=10), and auxiliary modules (n=11).
  • Strengths included novel cloud system applications, Bayesian networks, visualization, and unstructured data translation to FHIR.
  • Identified weaknesses included lack of electronic health record interoperability and external validation of clinical efficacy.

Conclusions:

  • Current ML-CIS shortcomings can be mitigated through modular, interoperable data management and analytics platforms.
  • Secure interinstitutional data exchange and scalable APIs are essential for real-time and prospective clinical applications.
  • Optimizing ML-CISs requires addressing diverse electronic health record platform implementations and ensuring robust interoperability.