BACKGROUND
Data models play a crucial role in facilitating clinical research and taking full advantage of clinical data stored in medical systems; data, as well as the clear relationships between them, are expected to be in a standardized format to establish reproducible research. Using the Fast Healthcare Interoperability Resources (FHIR) standard for clinical data representation would be a practical methodology to enhance and accelerate interoperability and data availability for research.
OBJECTIVE
To investigate data models utilizing the FHIR standard, to offer a comprehensive overview of the best practices for developing and implementing these data models as well as presenting a summary of tools, mappings, limitations and other important details in the selected models.
METHODS
To ensure the extraction of reliable results, we followed the instructions of Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. We analyzed the indexed articles in PubMed, Scopus, Web of Science, IEEE Xplore, ACM digital library, and Google Scholar using Boolean operators to merge relevant keywords and their related terms.
RESULTS
Based on the reviewed articles, we categorized them into two main groups; pipeline-based data models and non-linear data models. We summarized each included article and extracted information about the FHIR resources, technologies and standards, and mappings. We additionally aimed to extract and summarize the limitations of each research to provide a comprehensive view of the potential challenges and limitations that future researchers may face.
CONCLUSIONS
Based on the results of our review, FHIR can be a very promising standard in developing interoperable data models and infrastructures, despite presenting some challenges in the development phase. Policymakers and healthcare specialists can utilize this standard in any field such as healthcare, research, administration, finance, and so on. Additionally, when developing data models, this standard can also be integrated with other health-related standards to propose more interoperable solutions.