SMART on FHIR Genomics: facilitating standardized clinico-genomic apps. (21st July 2015)

• Record Type: Journal Article
• Authors: Alterovitz, Gil; Warner, Jeremy; Zhang, Peijin; Chen, Yishen; Ullman-Cullere, Mollie; Kreda, David; Kohane, Isaac S.
• Is Part Of: Journal of the American Medical Informatics Association. Volume 22:Number 6(2015:Nov.)
• Page Start: 1173
• Page End: 1178
• Publication Date: 2015-07-21
• Subjects: genomics -- data sharing -- clinicogenomics -- data warehouse -- i2b2 -- SMART -- FHIR -- HL7 -- standards -- EMR -- EHR; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocv045
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Background Supporting clinical decision support for personalized medicine will require linking genome and phenome variants to a patient's electronic health record (EHR), at times on a vast scale. Clinico-genomic data standards will be needed to unify how genomic variant data are accessed from different sequencing systems. Methods A specification for the basis of a clinic-genomic standard, building upon the current Health Level Seven International Fast Healthcare Interoperability Resources (FHIR®) standard, was developed. An FHIR application protocol interface (API) layer was attached to proprietary sequencing platforms and EHRs in order to expose gene variant data for presentation to the end-user. Three representative apps based on the SMART platform were built to test end-to-end feasibility, including integration of genomic and clinical data. Results Successful design, deployment, and use of the API was demonstrated and adopted by HL7 Clinical Genomics Workgroup. Feasibility was shown through development of three apps by various types of users with background levels and locations. Conclusion This prototyping work suggests that an entirely data (and web) standards-based approach could prove both effective and efficient for advancing personalized medicine.