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    • Resolution: Unresolved
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      Observational clinical research is emerging as a major source of clinical evidence and discovery; we cannot afford to learn all society needs to know from discrete, randomized clinical trials (though that remains the gold standard of evidence). Generating research data warehouses as a basis for analysis remains an expensive and laborious process; many academic medical centers make such investments, virtually no other healthcare providers do. The ubiquitous emergence of reliable FHIR APIs in EHR systems (increasingly required in the US by HHS/ONC regulation) supports a scalable export of EHR content to research data warehouses.

      OMOP (Observational Medical Outcomes Partnership) is the most common data model for EHR data warehouses and a standard, reusable FHIR to OMOP mapping would dramatically reduce the cost and effort to generate and maintain research data warehouses for all care delivery settings, including rural health settings. This in turn would substantially increase the scale and scope of observational analytics for clinical outcomes, best evidence discovery, and disease monitoring in pandemic circumstances, among other uses.

      As a coordinating body for the effort proposed, Vulcan would function as a strategic consolidator of effort and use-case mediator, maximizing value to the broadest possible research constituency. Furthermore, cataloging and analysis of prior work in and of itself will provide valuable contributions to communities in addition to research.

      Identify and catalog preliminary work
      Prior OMOP + FHIR transformation projects have aimed to address the needs of one or two types of organizations, typically also focused on a single clinical domain. This substantial previous work was done by many groups; thus, much tedious semantic mapping has been established, reducing the overall time-to deliver a viable work product.
      To-date, 22 distinct projects that have mapped FHIR to OMOP have been identified, in-scope for this project. The preliminary list of these projects can be found in the worksheet. The project as proposed will include a report of results from the environmental scan identifying in-scope, FHIR to OMOP transformation projects, and an evaluation of as many FHIR-to-OMOP map / transformation artifacts as can be obtained from prior work.

      Show
      Observational clinical research is emerging as a major source of clinical evidence and discovery; we cannot afford to learn all society needs to know from discrete, randomized clinical trials (though that remains the gold standard of evidence). Generating research data warehouses as a basis for analysis remains an expensive and laborious process; many academic medical centers make such investments, virtually no other healthcare providers do. The ubiquitous emergence of reliable FHIR APIs in EHR systems (increasingly required in the US by HHS/ONC regulation) supports a scalable export of EHR content to research data warehouses. OMOP (Observational Medical Outcomes Partnership) is the most common data model for EHR data warehouses and a standard, reusable FHIR to OMOP mapping would dramatically reduce the cost and effort to generate and maintain research data warehouses for all care delivery settings, including rural health settings. This in turn would substantially increase the scale and scope of observational analytics for clinical outcomes, best evidence discovery, and disease monitoring in pandemic circumstances, among other uses. As a coordinating body for the effort proposed, Vulcan would function as a strategic consolidator of effort and use-case mediator, maximizing value to the broadest possible research constituency. Furthermore, cataloging and analysis of prior work in and of itself will provide valuable contributions to communities in addition to research. Identify and catalog preliminary work Prior OMOP + FHIR transformation projects have aimed to address the needs of one or two types of organizations, typically also focused on a single clinical domain. This substantial previous work was done by many groups; thus, much tedious semantic mapping has been established, reducing the overall time-to deliver a viable work product. To-date, 22 distinct projects that have mapped FHIR to OMOP have been identified, in-scope for this project. The preliminary list of these projects can be found in the worksheet. The project as proposed will include a report of results from the environmental scan identifying in-scope, FHIR to OMOP transformation projects, and an evaluation of as many FHIR-to-OMOP map / transformation artifacts as can be obtained from prior work.
    • Biomedical Research & Regulation
    • Vulcan
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      The OHDSI CDM Vocabulary and CDM Working Groups
      Grahame Grieve
      Ed Hammond
      Christian Reich
      Christopher Chute
      Melissa Haendel
      Catherine Diederich
      Ben Smith
      Davera Gabriel
      Ann Phillips
      Harsh Sharma
      Myung Choi
      Bryan Laraway
      Karthik Natarajan
      Aniket Sao
      Jason Patterson
      Attiya Waqqas
      Adam Lee
      Vickie Reyes
      Sergey Krikov
      Debbie Bucci
      Daijiro Wachi
      Stephanie Hong
      Chris Roeder
      Guy Tsafnat
      Mike Hamidi
      May Terry
      J. Marc Overhage
      Patrick McLaughlin
      Qi Yang
      Sean Finan
      Somesh Patel
      Show
      The OHDSI CDM Vocabulary and CDM Working Groups Grahame Grieve Ed Hammond Christian Reich Christopher Chute Melissa Haendel Catherine Diederich Ben Smith Davera Gabriel Ann Phillips Harsh Sharma Myung Choi Bryan Laraway Karthik Natarajan Aniket Sao Jason Patterson Attiya Waqqas Adam Lee Vickie Reyes Sergey Krikov Debbie Bucci Daijiro Wachi Stephanie Hong Chris Roeder Guy Tsafnat Mike Hamidi May Terry J. Marc Overhage Patrick McLaughlin Qi Yang Sean Finan Somesh Patel
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      Myung Choi - IQVIA
      Chris Roeder - CCU Anschutz
      Aniket Sao - Innovaccer
      Jason Patterson, Karthik Natarajan- Columbia Univ
      Attiya Waqqas - RedCap Cloud
      Vickie Reyes - Guideline Central
      Sergey Krikov - Parexel
      Joe Flack, Stephanie Hong - Johns Hopkins Univ
      Adam Lee - Univ North Carolina
      Show
      Myung Choi - IQVIA Chris Roeder - CCU Anschutz Aniket Sao - Innovaccer Jason Patterson, Karthik Natarajan- Columbia Univ Attiya Waqqas - RedCap Cloud Vickie Reyes - Guideline Central Sergey Krikov - Parexel Joe Flack, Stephanie Hong - Johns Hopkins Univ Adam Lee - Univ North Carolina
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      OMOP + FHIR Collaboration. This project will be the primary work product for the Terminologies subgroup under the collaboration. This project team will work cooperatively with the Data Model Harmonization subgroup, and on a sect basis with the Oncology use case and digital quality measures subgroups.
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      OMOP + FHIR Collaboration. This project will be the primary work product for the Terminologies subgroup under the collaboration. This project team will work cooperatively with the Data Model Harmonization subgroup, and on a sect basis with the Oncology use case and digital quality measures subgroups.
    • Product Family Product Project Intent Lineage Ballot Type Target Cycle Actions
      1
      FHIR
      Other
      Other
       
      Joint Ballot with other SDO
      January 2024
    • Vulcan FHIR to OMOP
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      Project relies on cooperation from and availability of work products from prior FHIR-to-OMOP mapping implementation. To date, 21 projects have been identified as list in this worksheet (https://docs.google.com/spreadsheets/d/1ZJZVJEvOiZKi4O8ofpI1zVrc2QbcsFDes2ieC3NBQx8/edit#gid=0), and of these 3 projects have representation in project calls and have committed to contribute mapping content. The project team may determine that static maps must be supplemented with scripts, executable code or some additional technical support to meet the needs of the research community. Developing this supplementary material will be published via open-source distribution channels and will only be balloted if that is a requirement of the TSC / HL7.
      Show
      Project relies on cooperation from and availability of work products from prior FHIR-to-OMOP mapping implementation. To date, 21 projects have been identified as list in this worksheet ( https://docs.google.com/spreadsheets/d/1ZJZVJEvOiZKi4O8ofpI1zVrc2QbcsFDes2ieC3NBQx8/edit#gid=0), and of these 3 projects have representation in project calls and have committed to contribute mapping content. The project team may determine that static maps must be supplemented with scripts, executable code or some additional technical support to meet the needs of the research community. Developing this supplementary material will be published via open-source distribution channels and will only be balloted if that is a requirement of the TSC / HL7.
    • Yes
    • Yes
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      OHDSI
      All of Us
      National COVID Cohort Collaborative (N3C)
      CampFHIR
      OMOPonFHIR (Georgia Tech)
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      OHDSI All of Us National COVID Cohort Collaborative (N3C) CampFHIR OMOPonFHIR (Georgia Tech)
    • Yes
    • To be determined by prior work participants, at present we have project participation and content commitments from Georgia Tech (OMOPonFHIR), and the University of North Carolina (CampFHIR).
    • Academic/Research, Association/Goverment Agency, Healthcare IT Vendors, Pharmaceutical/Biotech, Standards Development Organizations (SDOs)
    • Universal
    • US

    Description

      Project Scope and deliverables
      The project aims to support selection of a canonical gold standard for only the US Core Data for Interoperability and the SNOMED CT International Patient Summary sub-ontology. Analysis and selection of the exact version of both FHIR IGs representing the core, in-scope data elements and OMOP target CDM will ensue. The question of which version(s) of the available FHIR implementation guides (IGs) would be followed, and in what sequence additionally will be determined. An overlap / gap analysis of the US Core and IPS elements will be conducted, which will simplify map evaluation and curation work in the iterative cycles planned. The content focus for each iterative work cycle will be based upon dividing up the core data classes, or domains into groups. Each content group will be assembled, examined and any gap mapping curated in a serial fashion. In this way the core content processing is broken-up into repeating cycles. An evaluation of the options for publication of the project work products will be conducted, and one or more publishing mechanisms will be selected that maximize both available production resources and reach to the broader OMOP & HL7 communities.

      To support analysis of prior work, a standard format will be developed to which prior map artifacts will be transformed. By utilizing this standard format, many map artifacts for a single common data element in scope can be compared across source projects. In addition to mapping content alignment, available tools which perform data comparisons (or “diffs”) will be leveraged to support our analyses. Once analysis of available prior work for a class of data elements is complete, the best approach for mapping each data element will be selected from prior work, or curated based on consensus or criteria developed by the project team. These examples of “best practice” maps will be collated for testing / review and publication.

      This proposal will be more sophisticated terminologically than prior IG activities, because the context / intellectual effort required to develop maps used in IGs will be preserved with the maps themselves. Thus, we hope to raise and establish a new “gold standard” for transformation artifacts and maps. Aligned with the HL7 publication process, cycles will include Connectathon validation activities for the “gold standard” content identified in each cycle by the project team. Testing and evaluation configuration scenarios and the supporting Connectathon infrastructure will be developed as a component of the initial project cycle. Issues surfaced as a result of Connectathon activities will be addressed in the subsequent development cycle.

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            daverag Davera Gabriel
            daverag Davera Gabriel
            Davera Gabriel Davera Gabriel
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