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Towards achieving semantic interoperability of clinical study data with FHIR

Overview of attention for article published in Journal of Biomedical Semantics, September 2017
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Title
Towards achieving semantic interoperability of clinical study data with FHIR
Published in
Journal of Biomedical Semantics, September 2017
DOI 10.1186/s13326-017-0148-7
Pubmed ID
Authors

Hugo Leroux, Alejandro Metke-Jimenez, Michael J. Lawley

Abstract

Observational clinical studies play a pivotal role in advancing medical knowledge and patient healthcare. To lessen the prohibitive costs of conducting these studies and support evidence-based medicine, results emanating from these studies need to be shared and compared to one another. Current approaches for clinical study management have limitations that prohibit the effective sharing of clinical research data. The objective of this paper is to present a proposal for a clinical study architecture to not only facilitate the communication of clinical study data but also its context so that the data that is being communicated can be unambiguously understood at the receiving end. Our approach is two-fold. First we outline our methodology to map clinical data from Clinical Data Interchange Standards Consortium Operational Data Model (ODM) to the Fast Healthcare Interoperable Resource (FHIR) and outline the strengths and weaknesses of this approach. Next, we propose two FHIR-based models, to capture the metadata and data from the clinical study, that not only facilitate the syntactic but also semantic interoperability of clinical study data. This work shows that our proposed FHIR resources provide a good fit to semantically enrich the ODM data. By exploiting the rich information model in FHIR, we can organise clinical data in a manner that preserves its organisation but captures its context. Our implementations demonstrate that FHIR can natively manage clinical data. Furthermore, by providing links at several levels, it improves the traversal and querying of the data. The intended benefits of this approach is more efficient and effective data exchange that ultimately will allow clinicians to switch their focus back to decision-making and evidence-based medicines.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 139 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
Unknown 138 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 29 21%
Student > Ph. D. Student 16 12%
Researcher 13 9%
Professor 10 7%
Student > Bachelor 10 7%
Other 25 18%
Unknown 36 26%
Readers by discipline Count As %
Computer Science 45 32%
Medicine and Dentistry 13 9%
Engineering 8 6%
Nursing and Health Professions 7 5%
Social Sciences 6 4%
Other 15 11%
Unknown 45 32%