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Accepted for/Published in: JMIR Medical Informatics

Date Submitted: Feb 1, 2019
Date Accepted: Mar 24, 2019

The final, peer-reviewed published version of this preprint can be found here:

Fast Healthcare Interoperability Resources, Clinical Quality Language, and Systematized Nomenclature of Medicine—Clinical Terms in Representing Clinical Evidence Logic Statements for the Use of Imaging Procedures: Descriptive Study

Odigie E, Lacson R, Raja A, Osterbur D, Ip I, Schneider L, Khorasani R

Fast Healthcare Interoperability Resources, Clinical Quality Language, and Systematized Nomenclature of Medicine—Clinical Terms in Representing Clinical Evidence Logic Statements for the Use of Imaging Procedures: Descriptive Study

JMIR Med Inform 2019;7(2):e13590

DOI: 10.2196/13590

PMID: 31094359

PMCID: 6535979

Assessment of FHIR, Clinical Quality Language, and SNOMED CT in Representing Clinical Evidence Logic Statements for the Use of Imaging Procedures

  • Eseosa Odigie; 
  • Ronilda Lacson; 
  • Ali Raja; 
  • David Osterbur; 
  • Ivan Ip; 
  • Louise Schneider; 
  • Ramin Khorasani

ABSTRACT

Background:

Evidence-based guidelines and recommendations can be transformed into “If-Then” Clinical Evidence Logic Statements (CELS). Imaging-related CELS were represented in standardized formats in the Harvard Medical School Library of Evidence (HMS-LOE).

Objective:

We aimed to: 1) describe representation of CELS into established Systemized Nomenclature of Medicine-Clinical Terms (SNOMED CT), Clinical Quality Language (CQL) and Fast HealthCare Interoperability Resources (FHIR) standards; and 2) assess the limitations of using these standards to represent imaging-related CELS.

Methods:

This study was exempt from Institutional Review Board review as it involved no human subjects. Imaging-related clinical recommendations were extracted from evidence sources, and translated into CELS. The clinical terminology of CELS were represented using SNOMED CT, and the condition–action logic was represented in CQL and FHIR. Numbers of fully and partially represented CELS were tallied.

Results:

Of 765 CELS represented in the HMS-LOE as of December 2018, we were able to fully represent 137 of 765 (18%) of CELS using SNOMED CT, CQL and FHIR. We were able to represent terms using SNOMED CT in 137 of 765 (18%) of CELS and the temporal component for action (“Then”) statements in CQL and FHIR in 755 of 765 (99%) of CELS.

Conclusions:

CELS were represented as shareable CDS knowledge artifacts using existing standards, SNOMED CT, FHIR and CQL, to promote and accelerate adoption of evidence-based practice. Limitations to standardization persist, which could be minimized with an add-on set of standard terms and value sets and by adding timeframes to the CQL framework.


 Citation

Please cite as:

Odigie E, Lacson R, Raja A, Osterbur D, Ip I, Schneider L, Khorasani R

Fast Healthcare Interoperability Resources, Clinical Quality Language, and Systematized Nomenclature of Medicine—Clinical Terms in Representing Clinical Evidence Logic Statements for the Use of Imaging Procedures: Descriptive Study

JMIR Med Inform 2019;7(2):e13590

DOI: 10.2196/13590

PMID: 31094359

PMCID: 6535979

Per the author's request the PDF is not available.