MENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance

Shahim Essaid; Jeff Andre; Ian M Brooks; Katherine H Hohman; Madelyne Hull; Sandra L Jackson; Michael G Kahn; Emily M Kraus; Neha Mandadi; Amanda K Martinez; Joyce Y Mui; Bob Zambarano; Andrey Soares

doi:10.1093/jamiaopen/ooae045

MENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance

JAMIA Open. 2024 May 29;7(2):ooae045. doi: 10.1093/jamiaopen/ooae045. eCollection 2024 Jul.

Authors

Shahim Essaid¹, Jeff Andre², Ian M Brooks^{1

3}, Katherine H Hohman⁴, Madelyne Hull³, Sandra L Jackson⁵, Michael G Kahn^{1

3}, Emily M Kraus^{6

7}, Neha Mandadi^{1

3}, Amanda K Martinez⁴, Joyce Y Mui^{1

3}, Bob Zambarano², Andrey Soares⁸

Affiliations

¹ Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States.
² Commonwealth Informatics Inc, Waltham, MA 02451, United States.
³ Health Data Compass, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States.
⁴ National Association of Chronic Disease Directors (NACDD), Decatur, GA 30030, United States.
⁵ National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention (CDC), Atlanta, GA 30333, United States.
⁶ Kraushold Consulting, Denver, CO 80120, United States.
⁷ Public Health Informatics Institute, Decatur, GA 30030, United States.
⁸ Department of Medicine, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States.

Abstract

Objectives: The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network that uses institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR examined using Health Language Seven's Fast Healthcare Interoperability Resources (HL7^® FHIR^®) and US Core Implementation Guide (US Core IG) compliant resources derived from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to create a standards-based ETL pipeline.

Materials and methods: The input data source was a research data warehouse containing clinical and administrative data in OMOP CDM Version 5.3 format. OMOP-to-FHIR transformations, using a unique JavaScript Object Notation (JSON)-to-JSON transformation language called Whistle, created FHIR R4 V4.0.1/US Core IG V4.0.0 conformant resources that were stored in a local FHIR server. A REST-based Bulk FHIR $export request extracted FHIR resources to populate a local MENDS database.

Results: Eleven OMOP tables were used to create 10 FHIR/US Core compliant resource types. A total of 1.13 trillion resources were extracted and inserted into the MENDS repository. A very low rate of non-compliant resources was observed.

Discussion: OMOP-to-FHIR transformation results passed validation with less than a 1% non-compliance rate. These standards-compliant FHIR resources provided standardized data elements required by the MENDS surveillance use case. The Bulk FHIR application programming interface (API) enabled population-level data exchange using interoperable FHIR resources. The OMOP-to-FHIR transformation pipeline creates a FHIR interface for accessing OMOP data.

Conclusion: MENDS-on-FHIR successfully replaced custom ETL with standards-based interoperable FHIR resources using Bulk FHIR. The OMOP-to-FHIR transformations provide an alternative mechanism for sharing OMOP data.

Keywords: HL7 Fast Healthcare Interoperability Resources (FHIR); Health Level Seven; electronic health records; health information interoperability; public health surveillance.