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Challenges of health data standard adoption and usage: a systematic review
Alberto Marfoglia1, Valerio Antonio Arcobelli2, Serena Moscato2
1Department of Computer Science and Engineering - DISI, University of Bologna, Via dell'Università 50, 47521 Cesena, Italy.
Objective:
To explore the adoption and practical implementation of the three major health data standards (i.e., FHIR, OMOP-CDM, and openEHR), to evaluate their maturity level in terms of how extensively they have been applied and integrated into everyday clinical and research practice.
Methods:
We conducted a systematic review registered in PROSPERO (CRD42024623398) following PRISMA guidelines. Literature searches were performed through PubMed, Cochrane, Scopus, Web of Science, and IEEE Xplore from 2021 to 2024. After de-duplication and screening, 99 studies were included. Data was extracted and classified according to five health application domains and five use cases based on the intended purpose of the standard in the work. Studies were assessed for implementation scale, ETL tools, coverage of the standard (i.e., the number of mapped source variables), and whether standards were adapted or used as-is.
Results:
Of the 99 included studies, 57% used OMOP-CDM, 39% FHIR, and 8% openEHR. Most applications occurred in research settings (87%) and focused on data reuse (47%) or clinical decision support (23%). OMOP-CDM was preferred for large-scale, longitudinal research, while FHIR was dominant in the public health domain and for real-time data exchange. Only 27% of studies reported the coverage of the standard. FHIR implementations often require customization, complicating interoperability. OMOP-CDM offered strong analytical tooling but posed challenges for mapping and data loss. Few studies using openEHR reported limitations, with its uptake remaining limited.
Conclusion:
Although FHIR, OMOP-CDM, and openEHR hold significant potential to enhance interoperability, their adoption remains fragmented. Each standard shows specific strengths: FHIR for exchange, OMOP-CDM for analytics, and openEHR for data persistence. A hybrid approach and clearer implementation practices are essential to support scalable, interoperable health data ecosystems.
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