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Essaid S, Andre J, Brooks IM, Hohman KH, Hull M, Jackson SL, Kahn MG, Kraus EM, Mandadi N, Martinez AK, Mui JY, Zambarano B, Soares A. MENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance. JAMIA Open 2024; 7:ooae045. [PMID: 38818114 PMCID: PMC11137321 DOI: 10.1093/jamiaopen/ooae045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/20/2024] [Accepted: 05/10/2024] [Indexed: 06/01/2024] Open
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.
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Affiliation(s)
- Shahim Essaid
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
| | - Jeff Andre
- Commonwealth Informatics Inc, Waltham, MA 02451, United States
| | - Ian M Brooks
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
- Health Data Compass, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
| | - Katherine H Hohman
- National Association of Chronic Disease Directors (NACDD), Decatur, GA 30030, United States
| | - Madelyne Hull
- Health Data Compass, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
| | - Sandra L Jackson
- National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention (CDC), Atlanta, GA 30333, United States
| | - Michael G Kahn
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
- Health Data Compass, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
| | - Emily M Kraus
- Kraushold Consulting, Denver, CO 80120, United States
- Public Health Informatics Institute, Decatur, GA 30030, United States
| | - Neha Mandadi
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
- Health Data Compass, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
| | - Amanda K Martinez
- National Association of Chronic Disease Directors (NACDD), Decatur, GA 30030, United States
| | - Joyce Y Mui
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
- Health Data Compass, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
| | - Bob Zambarano
- Commonwealth Informatics Inc, Waltham, MA 02451, United States
| | - Andrey Soares
- Department of Medicine, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
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Lee JS, Tyler ARB, Veinot TC, Yakel E. Now Is the Time to Strengthen Government-Academic Data Infrastructures to Jump-Start Future Public Health Crisis Response. JMIR Public Health Surveill 2024; 10:e51880. [PMID: 38656780 PMCID: PMC11079773 DOI: 10.2196/51880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 02/24/2024] [Accepted: 03/05/2024] [Indexed: 04/26/2024] Open
Abstract
During public health crises, the significance of rapid data sharing cannot be overstated. In attempts to accelerate COVID-19 pandemic responses, discussions within society and scholarly research have focused on data sharing among health care providers, across government departments at different levels, and on an international scale. A lesser-addressed yet equally important approach to sharing data during the COVID-19 pandemic and other crises involves cross-sector collaboration between government entities and academic researchers. Specifically, this refers to dedicated projects in which a government entity shares public health data with an academic research team for data analysis to receive data insights to inform policy. In this viewpoint, we identify and outline documented data sharing challenges in the context of COVID-19 and other public health crises, as well as broader crisis scenarios encompassing natural disasters and humanitarian emergencies. We then argue that government-academic data collaborations have the potential to alleviate these challenges, which should place them at the forefront of future research attention. In particular, for researchers, data collaborations with government entities should be considered part of the social infrastructure that bolsters their research efforts toward public health crisis response. Looking ahead, we propose a shift from ad hoc, intermittent collaborations to cultivating robust and enduring partnerships. Thus, we need to move beyond viewing government-academic data interactions as 1-time sharing events. Additionally, given the scarcity of scholarly exploration in this domain, we advocate for further investigation into the real-world practices and experiences related to sharing data from government sources with researchers during public health crises.
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Affiliation(s)
- Jian-Sin Lee
- School of Information, University of Michigan, Ann Arbor, MI, United States
| | | | - Tiffany Christine Veinot
- School of Information, University of Michigan, Ann Arbor, MI, United States
- Department of Health Behavior and Health Education, School of Public Health, University of Michigan, Ann Arbor, MI, United States
- Department of Learning Health Sciences, School of Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Elizabeth Yakel
- School of Information, University of Michigan, Ann Arbor, MI, United States
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Huguet N, Chen J, Parikh RB, Marino M, Flocke SA, Likumahuwa-Ackman S, Bekelman J, DeVoe JE. Applying Machine Learning Techniques to Implementation Science. Online J Public Health Inform 2024; 16:e50201. [PMID: 38648094 DOI: 10.2196/50201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 11/15/2023] [Accepted: 03/14/2024] [Indexed: 04/25/2024] Open
Abstract
Machine learning (ML) approaches could expand the usefulness and application of implementation science methods in clinical medicine and public health settings. The aim of this viewpoint is to introduce a roadmap for applying ML techniques to address implementation science questions, such as predicting what will work best, for whom, under what circumstances, and with what predicted level of support, and what and when adaptation or deimplementation are needed. We describe how ML approaches could be used and discuss challenges that implementation scientists and methodologists will need to consider when using ML throughout the stages of implementation.
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Affiliation(s)
- Nathalie Huguet
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Jinying Chen
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Data Science Core, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- iDAPT Implementation Science Center for Cancer Control, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Ravi B Parikh
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Miguel Marino
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Susan A Flocke
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Sonja Likumahuwa-Ackman
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Justin Bekelman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, United States
| | - Jennifer E DeVoe
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
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4
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Essaid S, Andre J, Brooks IM, Hohman KH, Hull M, Jackson SL, Kahn MG, Kraus EM, Mandadi N, Martinez AK, Mui JY, Zambarano B, Soares A. MENDS-on-FHIR: Leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.09.23293900. [PMID: 38045364 PMCID: PMC10690355 DOI: 10.1101/2023.08.09.23293900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Objective 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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Affiliation(s)
- Shahim Essaid
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver CO
| | - Jeff Andre
- Commonwealth Informatics Inc, Waltham MA
| | - Ian M Brooks
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver CO
- Health Data Compass, University of Colorado Anschutz Medical Campus, Denver CO
| | | | - Madelyne Hull
- Health Data Compass, University of Colorado Anschutz Medical Campus, Denver CO
| | - Sandra L Jackson
- National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention (CDC), Atlanta GA
| | - Michael G Kahn
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver CO
- Health Data Compass, University of Colorado Anschutz Medical Campus, Denver CO
| | - Emily M Kraus
- Kraushold Consulting, Denver CO
- Public Health Informatics Institute, Decatur, GA
| | - Neha Mandadi
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver CO
- Health Data Compass, University of Colorado Anschutz Medical Campus, Denver CO
| | - Amanda K Martinez
- National Association of Chronic Disease Directors (NACDD), Decatur GA
| | - Joyce Y Mui
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver CO
- Health Data Compass, University of Colorado Anschutz Medical Campus, Denver CO
| | | | - Andrey Soares
- Department of Medicine, University of Colorado Anschutz Medical Campus, Denver CO
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Bakken S. Informatics and data science approaches address significant public health problems. J Am Med Inform Assoc 2023; 30:1009-1010. [PMID: 37205729 PMCID: PMC10198515 DOI: 10.1093/jamia/ocad076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 05/21/2023] Open
Affiliation(s)
- Suzanne Bakken
- School of Nursing, Department of Biomedical Informatics, Data Science Institute, Columbia University, New York, New York, USA
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