Evidence map›Paper›PMID 38818114›Full record

ArticleJAMIA open2024

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 and 3 more

Abstract read
In one paragraph

Article in JAMIA open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Shahim EssaidDepartment of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States.ORCID https://orcid.org/0000-0003-2338-2550
Jeff AndreCommonwealth Informatics Inc, Waltham, MA 02451, United States.ORCID https://orcid.org/0009-0007-2654-6764
Ian M BrooksDepartment of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States.ORCID https://orcid.org/0000-0003-2724-936X
Katherine H HohmanNational Association of Chronic Disease Directors (NACDD), Decatur, GA 30030, United States.ORCID https://orcid.org/0000-0003-1552-4010
Madelyne HullHealth Data Compass, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States.ORCID https://orcid.org/0000-0002-1387-5004
Sandra L JacksonNational Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention (CDC), Atlanta, GA 30333, United States.ORCID https://orcid.org/0000-0003-4810-0572
Michael G KahnDepartment of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States.ORCID https://orcid.org/0000-0003-4786-6875
Emily M KrausKraushold Consulting, Denver, CO 80120, United States.ORCID https://orcid.org/0000-0002-2966-6936
Neha MandadiDepartment of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States.ORCID https://orcid.org/0009-0008-8317-4327
Amanda K MartinezNational Association of Chronic Disease Directors (NACDD), Decatur, GA 30030, United States.ORCID https://orcid.org/0000-0001-5549-7788
Joyce Y MuiDepartment of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States.ORCID https://orcid.org/0000-0002-4952-5735
Bob ZambaranoCommonwealth Informatics Inc, Waltham, MA 02451, United States.ORCID https://orcid.org/0009-0003-5723-748X
Andrey SoaresDepartment of Medicine, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States.ORCID https://orcid.org/0000-0003-4319-9411

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 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.

Indexed as

electronic health recordshealth information interoperabilityHealth Level SevenHL7 Fast Healthcare Interoperability Resources (FHIR)public health surveillance

Identifiers

PMID38818114
PMCPMC11137321

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.