Annotating Cohort Data Elements with OHDSI Common Data Model to Promote Research Reproducibility

Yiqing Zhao; Yanshan Wang; Henry Wang; Benjamin Yan; Feichen Shen; Kevin J. Peterson; Walter A. Rocca; Jennifer St Sauver; Hongfang Liu

doi:10.1109/BIBM.2018.8621269

Annotating Cohort Data Elements with OHDSI Common Data Model to Promote Research Reproducibility

Yiqing Zhao, Yanshan Wang, Henry Wang, Benjamin Yan, Feichen Shen, Kevin J. Peterson, Walter A. Rocca, Jennifer St Sauver, Hongfang Liu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

Rapid increase in the implementation of electronic health records (EHRs) has led to an unprecedented expansion in the availability of dense longitudinal cohort datasets for clinical studies. However, there is a growing need to ensure data traceability, validity, and reproducibility for EHR-based clinical research. Applying common data models that standardize EHR data elements could reduce research discrepancies and improve research reproducibility. As a pilot study, we utilized the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) developed by the Observational Health Data Sciences and Informatics (OHDSI) community to annotate cohort data elements from the local Rochester Epidemiology Project (REP). We evaluated the data coverage of the OMOP CDM by manually annotating the cohorts from 92 REP publications. Next, we examined cohort similarities between different publications using OMOP elements. Evaluation results showed that the OMOP CDM covers 99.8% of the content that is associated with cohort attributes. It demonstrated that the OMOP CDM can be used for data element standardization when extracting information from EHR and clinical registries. The OMOP CDM also shows its potential to be used as a tool for retrospective examination of cohort definition consistencies and epidemiology model similarities.

Original language	English (US)
Title of host publication	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Editors	Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1310-1317
Number of pages	8
ISBN (Electronic)	9781538654880
DOIs	https://doi.org/10.1109/BIBM.2018.8621269
State	Published - Jan 21 2019
Event	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain Duration: Dec 3 2018 → Dec 6 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/Territory	Spain
City	Madrid
Period	12/3/18 → 12/6/18

Keywords

Common Data Model
cohort study
data standardization
electronic health record

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics

Access to Document

10.1109/BIBM.2018.8621269

Cite this

Zhao, Y., Wang, Y., Wang, H., Yan, B., Shen, F., Peterson, K. J., Rocca, W. A., St Sauver, J., & Liu, H. (2019). Annotating Cohort Data Elements with OHDSI Common Data Model to Promote Research Reproducibility. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, & L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 1310-1317). Article 8621269 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621269

Annotating Cohort Data Elements with OHDSI Common Data Model to Promote Research Reproducibility. / Zhao, Yiqing; Wang, Yanshan; Wang, Henry et al.
Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1310-1317 8621269 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhao, Y, Wang, Y, Wang, H, Yan, B, Shen, F, Peterson, KJ, Rocca, WA , St Sauver, J & Liu, H 2019, Annotating Cohort Data Elements with OHDSI Common Data Model to Promote Research Reproducibility. in H Schmidt, D Griol, H Wang, J Baumbach, H Zheng, Z Callejas, X Hu, J Dickerson & L Zhang (eds), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018., 8621269, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1310-1317, 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, 12/3/18. https://doi.org/10.1109/BIBM.2018.8621269

Zhao Y, Wang Y, Wang H, Yan B, Shen F, Peterson KJ et al. Annotating Cohort Data Elements with OHDSI Common Data Model to Promote Research Reproducibility. In Schmidt H, Griol D, Wang H, Baumbach J, Zheng H, Callejas Z, Hu X, Dickerson J, Zhang L, editors, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1310-1317. 8621269. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). doi: 10.1109/BIBM.2018.8621269

Zhao, Yiqing ; Wang, Yanshan ; Wang, Henry et al. / Annotating Cohort Data Elements with OHDSI Common Data Model to Promote Research Reproducibility. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. editor / Harald Schmidt ; David Griol ; Haiying Wang ; Jan Baumbach ; Huiru Zheng ; Zoraida Callejas ; Xiaohua Hu ; Julie Dickerson ; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1310-1317 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

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title = "Annotating Cohort Data Elements with OHDSI Common Data Model to Promote Research Reproducibility",

abstract = "Rapid increase in the implementation of electronic health records (EHRs) has led to an unprecedented expansion in the availability of dense longitudinal cohort datasets for clinical studies. However, there is a growing need to ensure data traceability, validity, and reproducibility for EHR-based clinical research. Applying common data models that standardize EHR data elements could reduce research discrepancies and improve research reproducibility. As a pilot study, we utilized the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) developed by the Observational Health Data Sciences and Informatics (OHDSI) community to annotate cohort data elements from the local Rochester Epidemiology Project (REP). We evaluated the data coverage of the OMOP CDM by manually annotating the cohorts from 92 REP publications. Next, we examined cohort similarities between different publications using OMOP elements. Evaluation results showed that the OMOP CDM covers 99.8% of the content that is associated with cohort attributes. It demonstrated that the OMOP CDM can be used for data element standardization when extracting information from EHR and clinical registries. The OMOP CDM also shows its potential to be used as a tool for retrospective examination of cohort definition consistencies and epidemiology model similarities.",

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AB - Rapid increase in the implementation of electronic health records (EHRs) has led to an unprecedented expansion in the availability of dense longitudinal cohort datasets for clinical studies. However, there is a growing need to ensure data traceability, validity, and reproducibility for EHR-based clinical research. Applying common data models that standardize EHR data elements could reduce research discrepancies and improve research reproducibility. As a pilot study, we utilized the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) developed by the Observational Health Data Sciences and Informatics (OHDSI) community to annotate cohort data elements from the local Rochester Epidemiology Project (REP). We evaluated the data coverage of the OMOP CDM by manually annotating the cohorts from 92 REP publications. Next, we examined cohort similarities between different publications using OMOP elements. Evaluation results showed that the OMOP CDM covers 99.8% of the content that is associated with cohort attributes. It demonstrated that the OMOP CDM can be used for data element standardization when extracting information from EHR and clinical registries. The OMOP CDM also shows its potential to be used as a tool for retrospective examination of cohort definition consistencies and epidemiology model similarities.

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Annotating Cohort Data Elements with OHDSI Common Data Model to Promote Research Reproducibility

Abstract

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Keywords

ASJC Scopus subject areas

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