European Health Data & Evidence Network-learnings from building out a standardized international health data network

. 2023 Dec 22 ; 31 (1) : 209-219.

Jazyk angličtina Země Velká Británie, Anglie Médium print

Typ dokumentu pozorovací studie, časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid37952118

OBJECTIVE: Health data standardized to a common data model (CDM) simplifies and facilitates research. This study examines the factors that make standardizing observational health data to the Observational Medical Outcomes Partnership (OMOP) CDM successful. MATERIALS AND METHODS: Twenty-five data partners (DPs) from 11 countries received funding from the European Health Data Evidence Network (EHDEN) to standardize their data. Three surveys, DataQualityDashboard results, and statistics from the conversion process were analyzed qualitatively and quantitatively. Our measures of success were the total number of days to transform source data into the OMOP CDM and participation in network research. RESULTS: The health data converted to CDM represented more than 133 million patients. 100%, 88%, and 84% of DPs took Surveys 1, 2, and 3. The median duration of the 6 key extract, transform, and load (ETL) processes ranged from 4 to 115 days. Of the 25 DPs, 21 DPs were considered applicable for analysis of which 52% standardized their data on time, and 48% participated in an international collaborative study. DISCUSSION: This study shows that the consistent workflow used by EHDEN proves appropriate to support the successful standardization of observational data across Europe. Over the 25 successful transformations, we confirmed that getting the right people for the ETL is critical and vocabulary mapping requires specific expertise and support of tools. Additionally, we learned that teams that proactively prepared for data governance issues were able to avoid considerable delays improving their ability to finish on time. CONCLUSION: This study provides guidance for future DPs to standardize to the OMOP CDM and participate in distributed networks. We demonstrate that the Observational Health Data Sciences and Informatics community must continue to evaluate and provide guidance and support for what ultimately develops the backbone of how community members generate evidence.

Zobrazit více v PubMed

Reisinger SJ, Ryan PB, O’Hara DJ, et al.Development and evaluation of a common data model enabling active drug safety surveillance using disparate healthcare databases. J Am Med Inform Assoc. 2010;17(6):652-662. 10.1136/jamia.2009.002477 PubMed DOI PMC

Ong T, Pradhananga R, Holve E, Kahn MG.. A framework for classification of electronic health data extraction-transformation-loading challenges in data network participation. EGEMS (Washington, DC). 2017;5(1):10. 10.5334/egems.222 PubMed DOI PMC

Common Data Model (CDM). OMOP Common Data Model (CDM). Accessed May 14, 2023. https://github.com/OHDSI/CommonDataModel

Observational Health Data Sciences and Informatics. The Book of OHDSI. The Book of OHDSI. Accessed August 29, 2019. https://ohdsi.github.io/TheBookOfOhdsi/

OHDSI/WhiteRabbit [program]. Version V0.10.1. GitHub. Accessed May 14, 2023. https://github.com/OHDSI/WhiteRabbit

OHDSI/Usagi [program]. Version V1.3.0. GitHub. Accessed May 14, 2023. https://github.com/OHDSI/Usagi

OHDSI/Automated Characterization of Health Information at Large-scale Longitudinal Evidence Systems (ACHILLES) [program]. Version V1.6.3. GitHub. Accessed May 14, 2023. https://github.com/OHDSI/Achilles

OHDSI/DataQualityDashboard (DQD) [program]. GitHub. Accessed May 14, 2023. https://github.com/OHDSI/DataQualityDashboard

OHDSI Forums. OHDSI Forums [Web Forum]. Accessed May 14, 2023. https://forums.ohdsi.org/

European Health Data & Evidence Network (EHDEN). European Health Data & Evidence Network (EHDEN) [Web Page]. Accessed May 14, 2023. https://www.ehden.eu/

European Health Data & Evidence Network (EHDEN). 04/2020—COVID19 Rapid Collaboration Call. 04/2020—COVID19 Rapid Collaboration Call. Accessed November 6, 2022. https://www.ehden.eu/open-calls/04-2020-covid19-data-partner-call/

OHDSI White Rabbit. OHDSI White Rabbit. Accessed November 6, 2022. http://ohdsi.github.io/WhiteRabbit/index.html

OHDSI Usagi. OHDSI Usagi. Accessed August 27, 2023. https://www.ohdsi.org/web/wiki/doku.php?id=documentation:software:usagi

Adverse Events of Special Interest within COVID-19 Subjects Code Repository. Adverse Events of Special Interest within COVID-19 Subjects Code Repository [GitHub Repository] 2021. Accessed November 6, 2022. https://github.com/ohdsi-studies/Covid19SubjectsAesiIncidenceRate

Voss EA, Sa Ostropolets A, Nyberg F, et al. Adverse Events of Special Interest within COVID-19 Subjects—Research Protocol. Adverse Events of Special Interest within COVID-19 Subjects—Research Protocol. Accessed November 6, 2022. https://ohdsi-studies.github.io/Covid19SubjectsAesiIncidenceRate/Protocol.html

Voss EA, Shoaibi A, Yin Hui Lai L,. et al. Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study. EClinicalMedicine. 2023;58:101932. 10.1016/j.eclinm.2023.101932 PubMed DOI PMC

Blacketer C, Voss EA, DeFalco F, et al.Using the data quality dashboard to improve the EHDEN network. Appl Sci. 2021;11(24):11920.

Oja M, Tamm S, Mooses K, et al. Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned, medRxiv 2023:2023.02.16.23285697. 10.1101/2023.02.16.23285697, preprint: not peer reviewed. PubMed DOI PMC

Yu Y, Zong N, Wen A, et al.Developing an ETL tool for converting the PCORnet CDM into the OMOP CDM to facilitate the COVID-19 data integration. J Biomed Inform. 2022;127:104002. 10.1016/j.jbi.2022.104002 PubMed DOI PMC

Puttmann D, De Keizer N, Cornet R, Van Der Zwan E, Bakhshi-Raiez F.. FAIRifying a quality registry using OMOP CDM: challenges and solutions. Stud Health Technol Inform. 2022;294:367-371. 10.3233/shti220476 PubMed DOI

Carus J, Nürnberg S, Ückert F, Schlüter C, Bartels S.. Mapping cancer registry data to the episode domain of the Observational Medical Outcomes Partnership Model (OMOP). Appl Sci. 2022;12(8):4010.

Papez V, Moinat M, Voss EA, et al.Transforming and evaluating the UK Biobank to the OMOP Common Data Model for COVID-19 research and beyond. J Am Med Inform Assoc. 2022;30(1):103-111. 10.1093/jamia/ocac203 PubMed DOI PMC

Biedermann P, Ong R, Davydov A, et al.Standardizing registry data to the OMOP Common Data Model: experience from three pulmonary hypertension databases. BMC Med Res Methodol. 2021;21(1):238. 10.1186/s12874-021-01434-3 PubMed DOI PMC

Lamer A, Depas N, Doutreligne M, et al.Transforming French electronic health records into the observational medical outcome partnership’s common data model: a feasibility study. Appl Clin Inform. 2020;11(1):13-22. 10.1055/s-0039-3402754 PubMed DOI PMC

Klann JG, Joss MAH, Embree K, Murphy SN.. Data model harmonization for the All Of Us Research Program: transforming i2b2 data into the OMOP Common Data Model. PLoS One. 2019;14(2):e0212463. 10.1371/journal.pone.0212463 PubMed DOI PMC

You SC, Lee S, Cho SY, et al.Conversion of National Health Insurance Service-National Sample Cohort (NHIS-NSC) Database into Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM). Stud Health Technol Inform. 2017;245:467-470. PubMed

Yoon D, Ahn EK, Park MY, et al.Conversion and data quality assessment of electronic health record data at a Korean Tertiary Teaching Hospital to a common data model for distributed network research. Healthc Inform Res. 2016;22(1):54-58. 10.4258/hir.2016.22.1.54 PubMed DOI PMC

FitzHenry F, Resnic FS, Robbins SL, et al.Creating a common data model for comparative effectiveness with the observational medical outcomes partnership. Appl Clin Inform. 2015;6(3):536-547. 10.4338/aci-2014-12-cr-0121 PubMed DOI PMC

Matcho A, Ryan P, Fife D, Reich C.. Fidelity assessment of a clinical practice research datalink conversion to the OMOP Common Data Model. Drug Saf. 2014;37(11):945-959. 10.1007/s40264-014-0214-3 PubMed DOI PMC

Makadia R, Ryan PB.. Transforming the Premier Perspective Hospital Database into the Observational Medical Outcomes Partnership (OMOP) common data model. EGEMS (Washington, DC). 2014;2(1):1110. 10.13063/2327-9214.1110 PubMed DOI PMC

Zhou X, Murugesan S, Bhullar H, et al.An evaluation of the THIN database in the OMOP Common Data Model for active drug safety surveillance. Drug Saf. 2013;36(2):119-134. 10.1007/s40264-012-0009-3 PubMed DOI

Overhage JM, Ryan PB, Reich CG, Hartzema AG, Stang PE.. Validation of a common data model for active safety surveillance research. J Am Med Inform Assoc. 2012;19(1):54-60. 10.1136/amiajnl-2011-000376 PubMed DOI PMC

Perseus. Perseus [Git Repository]. Accessed January 28, 2023. https://github.com/OHDSI/Perseus

Susana [Web Page]. Accessed May 14, 2022. https://interhop.frama.io/omop/susana/

de Ridder MAJ, de Wilde M, de Ben C, et al.Data resource profile: the Integrated Primary Care Information (IPCI) database, The Netherlands. Int J Epidemiol. 2022;51(6):e314-e323. 10.1093/ije/dyac026 PubMed DOI PMC

Najít záznam

Citační ukazatele

Nahrávání dat ...

Možnosti archivace

Nahrávání dat ...