Global Health Informatics: the state of research and lessons learned

Quintana, Yuri; Cullen, Theresa; Holmes, John H.; Joshi, Ashish; Novillo-Ortiz, David; Liaw, Siaw‐Teng

doi:10.1093/jamia/ocad027

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…The COVID-19 pandemic highlighted the urgent need for rapid access to clinical data from diverse settings to assess risk factors and treatment outcomes [24][25][26][27][28][29]. Chronic disease surveillance registries also need access to linked clinical, administrative, and social determinants of health data across diverse healthcare settings [30].…”

Section: Discussionmentioning

confidence: 99%

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

Essaid,

Andre,

Brooks

et al. 2023

Preprint

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Objective The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network. Current data partners create institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR provides a standards-based ETL approach 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). Materials and Methods The input data source was a research data warehouse (RDW) 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 language called Whistle, created FHIR R4.0.1/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 transformations passed validation with only minimal non-compliance issues. These resources provided the clinical and administrative 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 "facade" for accessing OMOP data. Conclusion MENDS-on-FHIR successfully replaced custom ETL with standards-based interoperable FHIR resources using Bulk FHIR. The OMOP-on-FHIR transformations provide an alternative mechanism for sharing OMOP data.

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Section: Discussionmentioning

confidence: 99%

“…The COVID-19 pandemic highlighted the urgent need for rapid access to clinical data from diverse settings to assess emerging risk factors and treatment outcomes [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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

Essaid,

Andre,

Brooks

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

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

Essaid,

Andre,

Brooks

et al. 2024

JAMIA Open

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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.

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Global Health Informatics: the state of research and lessons learned

Cited by 2 publications

References 37 publications

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

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

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

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