Data model harmonization for the All Of Us Research Program: Transforming i2b2 data into the OMOP common data model
The All Of Us Research Program (AOU) is building a nationwide cohort of one million patients' EHR and genomic data. Data interoperability is paramount to the program's success. AOU is standardizing its EHR data around the Observational Medical Outcomes Partnership (OMOP) data model. OMOP i...
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| creator | Klann, Jeffrey G Joss, Matthew A H Embree, Kevin Murphy, Shawn N |
| description | The All Of Us Research Program (AOU) is building a nationwide cohort of one million patients' EHR and genomic data. Data interoperability is paramount to the program's success. AOU is standardizing its EHR data around the Observational Medical Outcomes Partnership (OMOP) data model. OMOP is one of several standard data models presently used in national-scale initiatives. Each model is unique enough to make interoperability difficult. The i2b2 data warehousing and analytics platform is used at over 200 sites worldwide, which uses a flexible ontology-driven approach for data storage. We previously demonstrated this ontology system can drive data reconfiguration, to transform data into new formats without site-specific programming. We previously implemented this on our 12-site Accessible Research Commons for Health (ARCH) network to transform i2b2 into the Patient Centered Outcomes Research Network model.
Here, we leverage our investment in i2b2 high-performance transformations to support the AOU OMOP data pipeline. Because the ARCH ontology has gained widespread national interest (through the Accrual to Clinical Trials network, other PCORnet networks, and the Nebraska Lexicon), we leveraged sites' existing investments into this standard ontology. We developed an i2b2-to-OMOP transformation, driven by the ARCH-OMOP ontology and the OMOP concept mapping dictionary. We demonstrated and validated our approach in the AOU New England HPO (NEHPO). First, we transformed into OMOP a fake patient dataset in i2b2 and verified through AOU tools that the data was structurally compliant with OMOP. We then transformed a subset of data in the Partners Healthcare data warehouse into OMOP. We developed a checklist of assessments to ensure the transformed data had self-integrity (e.g., the distributions have an expected shape and required fields are populated), using OMOP's visual Achilles data quality tool. This i2b2-to-OMOP transformation is being used to send NEHPO production data to AOU. It is open-source and ready for use by other research projects. |
| doi_str_mv | 10.1371/journal.pone.0212463 |
| format | Article |
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Here, we leverage our investment in i2b2 high-performance transformations to support the AOU OMOP data pipeline. Because the ARCH ontology has gained widespread national interest (through the Accrual to Clinical Trials network, other PCORnet networks, and the Nebraska Lexicon), we leveraged sites' existing investments into this standard ontology. We developed an i2b2-to-OMOP transformation, driven by the ARCH-OMOP ontology and the OMOP concept mapping dictionary. We demonstrated and validated our approach in the AOU New England HPO (NEHPO). First, we transformed into OMOP a fake patient dataset in i2b2 and verified through AOU tools that the data was structurally compliant with OMOP. We then transformed a subset of data in the Partners Healthcare data warehouse into OMOP. We developed a checklist of assessments to ensure the transformed data had self-integrity (e.g., the distributions have an expected shape and required fields are populated), using OMOP's visual Achilles data quality tool. This i2b2-to-OMOP transformation is being used to send NEHPO production data to AOU. 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This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2019 Klann et al 2019 Klann et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-68afb30eb45db88acb22adaa403e31869f9583b4efd33482a55810c6ff540b5a3</citedby><cites>FETCH-LOGICAL-c692t-68afb30eb45db88acb22adaa403e31869f9583b4efd33482a55810c6ff540b5a3</cites><orcidid>0000-0003-2043-1601</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6380544/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6380544/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,729,782,786,866,887,2106,2932,23875,27933,27934,53800,53802</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30779778$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Lovis, Christian</contributor><creatorcontrib>Klann, Jeffrey G</creatorcontrib><creatorcontrib>Joss, Matthew A H</creatorcontrib><creatorcontrib>Embree, Kevin</creatorcontrib><creatorcontrib>Murphy, Shawn N</creatorcontrib><title>Data model harmonization for the All Of Us Research Program: Transforming i2b2 data into the OMOP common data model</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>The All Of Us Research Program (AOU) is building a nationwide cohort of one million patients' EHR and genomic data. Data interoperability is paramount to the program's success. AOU is standardizing its EHR data around the Observational Medical Outcomes Partnership (OMOP) data model. OMOP is one of several standard data models presently used in national-scale initiatives. Each model is unique enough to make interoperability difficult. The i2b2 data warehousing and analytics platform is used at over 200 sites worldwide, which uses a flexible ontology-driven approach for data storage. We previously demonstrated this ontology system can drive data reconfiguration, to transform data into new formats without site-specific programming. We previously implemented this on our 12-site Accessible Research Commons for Health (ARCH) network to transform i2b2 into the Patient Centered Outcomes Research Network model.
Here, we leverage our investment in i2b2 high-performance transformations to support the AOU OMOP data pipeline. Because the ARCH ontology has gained widespread national interest (through the Accrual to Clinical Trials network, other PCORnet networks, and the Nebraska Lexicon), we leveraged sites' existing investments into this standard ontology. We developed an i2b2-to-OMOP transformation, driven by the ARCH-OMOP ontology and the OMOP concept mapping dictionary. We demonstrated and validated our approach in the AOU New England HPO (NEHPO). First, we transformed into OMOP a fake patient dataset in i2b2 and verified through AOU tools that the data was structurally compliant with OMOP. We then transformed a subset of data in the Partners Healthcare data warehouse into OMOP. We developed a checklist of assessments to ensure the transformed data had self-integrity (e.g., the distributions have an expected shape and required fields are populated), using OMOP's visual Achilles data quality tool. This i2b2-to-OMOP transformation is being used to send NEHPO production data to AOU. 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Data interoperability is paramount to the program's success. AOU is standardizing its EHR data around the Observational Medical Outcomes Partnership (OMOP) data model. OMOP is one of several standard data models presently used in national-scale initiatives. Each model is unique enough to make interoperability difficult. The i2b2 data warehousing and analytics platform is used at over 200 sites worldwide, which uses a flexible ontology-driven approach for data storage. We previously demonstrated this ontology system can drive data reconfiguration, to transform data into new formats without site-specific programming. We previously implemented this on our 12-site Accessible Research Commons for Health (ARCH) network to transform i2b2 into the Patient Centered Outcomes Research Network model.
Here, we leverage our investment in i2b2 high-performance transformations to support the AOU OMOP data pipeline. Because the ARCH ontology has gained widespread national interest (through the Accrual to Clinical Trials network, other PCORnet networks, and the Nebraska Lexicon), we leveraged sites' existing investments into this standard ontology. We developed an i2b2-to-OMOP transformation, driven by the ARCH-OMOP ontology and the OMOP concept mapping dictionary. We demonstrated and validated our approach in the AOU New England HPO (NEHPO). First, we transformed into OMOP a fake patient dataset in i2b2 and verified through AOU tools that the data was structurally compliant with OMOP. We then transformed a subset of data in the Partners Healthcare data warehouse into OMOP. We developed a checklist of assessments to ensure the transformed data had self-integrity (e.g., the distributions have an expected shape and required fields are populated), using OMOP's visual Achilles data quality tool. This i2b2-to-OMOP transformation is being used to send NEHPO production data to AOU. It is open-source and ready for use by other research projects.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>30779778</pmid><doi>10.1371/journal.pone.0212463</doi><tpages>e0212463</tpages><orcidid>https://orcid.org/0000-0003-2043-1601</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS) Journals Open Access; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Analysis Analytics Arches Biology and Life Sciences Biomedical Research Clinical trials Cohort Studies Community Computer and Information Sciences Computer science Concept mapping Consortia Data collection Data modeling Data models Data processing Data storage Data warehouses Data warehousing Databases, Factual Delivery of Health Care Electronic health records Electronic Health Records - trends Gene mapping Health care Hospitals Humans Informatics Information management Information science Information Storage and Retrieval - methods Initiatives Internet Interoperability Investments Jargon Laboratories Medical records Medical research Medicine Medicine and Health Sciences Metadata Ontology Pathology Patients Precision medicine Reconfiguration Research and Analysis Methods Research projects Researchers Social Sciences Standard data Transformation United States |
| title | Data model harmonization for the All Of Us Research Program: Transforming i2b2 data into the OMOP common data model |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-02T21%3A12%3A59IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data%20model%20harmonization%20for%20the%20All%20Of%20Us%20Research%20Program:%20Transforming%20i2b2%20data%20into%20the%20OMOP%20common%20data%20model&rft.jtitle=PloS%20one&rft.au=Klann,%20Jeffrey%20G&rft.date=2019-02-19&rft.volume=14&rft.issue=2&rft.spage=e0212463&rft.epage=e0212463&rft.pages=e0212463-e0212463&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0212463&rft_dat=%3Cgale_plos_%3EA574784070%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2184387064&rft_id=info:pmid/30779778&rft_galeid=A574784070&rft_doaj_id=oai_doaj_org_article_c36a27b4d90741278008b3fa9156ee39&rfr_iscdi=true |