TY - JOUR
T1 - Developing an ETL tool for converting the PCORnet CDM into the OMOP CDM to facilitate the COVID-19 data integration
AU - Yu, Yue
AU - Zong, Nansu
AU - Wen, Andrew
AU - Liu, Sijia
AU - Stone, Daniel J.
AU - Knaack, David
AU - Chamberlain, Alanna M.
AU - Pfaff, Emily
AU - Gabriel, Davera
AU - Chute, Christopher G.
AU - Shah, Nilay
AU - Jiang, Guoqian
N1 - Funding Information:
The OMOP CDM was developed by the Observational Medical Outcomes Partnership (OMOP), a project formerly chaired by the US Food and Drug Administration (FDA), administered by the Foundation for the National Institutes of Health (NIH), and funded by a consortium of pharmaceutical companies [2,29] . Currently, the CDM is maintained by an open-science community, Observational Health Data Sciences and Informatics (OHDSI). The OMOP CDM also aims to represent healthcare data from diverse sources in a consistent and standardized manner. As compared with the PCORnet CDM, the OMOP CDM is distinguished in that it possesses a comprehensive vocabulary component which contains hundreds of medical terminologies and maps them to a common coding system. Moreover, the encoding and relationships among distinct medical concepts are explicitly and formally specified [30] . In the current OMOP CDM version 6.0, there are 38 tables distributed across 6 domains ( Fig. 2 ). We use the OMOP CDM v6.0 as a destination data model for the ETL processing in this study.
Funding Information:
This study is supported in part by the NCATS CD2H Project (U24 TR002306) and the NIH FHIRCat Projects (R56 EB028101 and R01 EB030529).
Publisher Copyright:
© 2022 Elsevier Inc.
PY - 2022/3
Y1 - 2022/3
N2 - Objective: The large-scale collection of observational data and digital technologies could help curb the COVID-19 pandemic. However, the coexistence of multiple Common Data Models (CDMs) and the lack of data extract, transform, and load (ETL) tool between different CDMs causes potential interoperability issue between different data systems. The objective of this study is to design, develop, and evaluate an ETL tool that transforms the PCORnet CDM format data into the OMOP CDM. Methods: We developed an open-source ETL tool to facilitate the data conversion from the PCORnet CDM and the OMOP CDM. The ETL tool was evaluated using a dataset with 1000 patients randomly selected from the PCORnet CDM at Mayo Clinic. Information loss, data mapping accuracy, and gap analysis approaches were conducted to assess the performance of the ETL tool. We designed an experiment to conduct a real-world COVID-19 surveillance task to assess the feasibility of the ETL tool. We also assessed the capacity of the ETL tool for the COVID-19 data surveillance using data collection criteria of the MN EHR Consortium COVID-19 project. Results: After the ETL process, all the records of 1000 patients from 18 PCORnet CDM tables were successfully transformed into 12 OMOP CDM tables. The information loss for all the concept mapping was less than 0.61%. The string mapping process for the unit concepts lost 2.84% records. Almost all the fields in the manual mapping process achieved 0% information loss, except the specialty concept mapping. Moreover, the mapping accuracy for all the fields were 100%. The COVID-19 surveillance task collected almost the same set of cases (99.3% overlaps) from the original PCORnet CDM and target OMOP CDM separately. Finally, all the data elements for MN EHR Consortium COVID-19 project could be captured from both the PCORnet CDM and the OMOP CDM. Conclusion: We demonstrated that our ETL tool could satisfy the data conversion requirements between the PCORnet CDM and the OMOP CDM. The outcome of the work would facilitate the data retrieval, communication, sharing, and analysis between different institutions for not only COVID-19 related project, but also other real-world evidence-based observational studies.
AB - Objective: The large-scale collection of observational data and digital technologies could help curb the COVID-19 pandemic. However, the coexistence of multiple Common Data Models (CDMs) and the lack of data extract, transform, and load (ETL) tool between different CDMs causes potential interoperability issue between different data systems. The objective of this study is to design, develop, and evaluate an ETL tool that transforms the PCORnet CDM format data into the OMOP CDM. Methods: We developed an open-source ETL tool to facilitate the data conversion from the PCORnet CDM and the OMOP CDM. The ETL tool was evaluated using a dataset with 1000 patients randomly selected from the PCORnet CDM at Mayo Clinic. Information loss, data mapping accuracy, and gap analysis approaches were conducted to assess the performance of the ETL tool. We designed an experiment to conduct a real-world COVID-19 surveillance task to assess the feasibility of the ETL tool. We also assessed the capacity of the ETL tool for the COVID-19 data surveillance using data collection criteria of the MN EHR Consortium COVID-19 project. Results: After the ETL process, all the records of 1000 patients from 18 PCORnet CDM tables were successfully transformed into 12 OMOP CDM tables. The information loss for all the concept mapping was less than 0.61%. The string mapping process for the unit concepts lost 2.84% records. Almost all the fields in the manual mapping process achieved 0% information loss, except the specialty concept mapping. Moreover, the mapping accuracy for all the fields were 100%. The COVID-19 surveillance task collected almost the same set of cases (99.3% overlaps) from the original PCORnet CDM and target OMOP CDM separately. Finally, all the data elements for MN EHR Consortium COVID-19 project could be captured from both the PCORnet CDM and the OMOP CDM. Conclusion: We demonstrated that our ETL tool could satisfy the data conversion requirements between the PCORnet CDM and the OMOP CDM. The outcome of the work would facilitate the data retrieval, communication, sharing, and analysis between different institutions for not only COVID-19 related project, but also other real-world evidence-based observational studies.
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U2 - 10.1016/j.jbi.2022.104002
DO - 10.1016/j.jbi.2022.104002
M3 - Article
C2 - 35077901
AN - SCOPUS:85123846607
SN - 1532-0464
VL - 127
JO - Journal of Biomedical Informatics
JF - Journal of Biomedical Informatics
M1 - 104002
ER -