Development of Integrated Data Quality Management System for Observational Medical Outcomes Partnership Common Data Model

Seol Whan Oh; Soo Jeong Ko; Yun Seon Im; Surin Jung; Bo Yeon Choi; Jae Yoon Kim; Sunghyeon Park; Wona Choi; In Young Choi

doi:10.3233/SHTI230985

Development of Integrated Data Quality Management System for Observational Medical Outcomes Partnership Common Data Model

Seol Whan Oh
, Soo Jeong Ko
, Yun Seon Im
, Surin Jung
, Bo Yeon Choi
, Jae Yoon Kim
, Sunghyeon Park
, Wona Choi
, In Young Choi

Department of Medical Informatics

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

1 Scopus citations

Abstract

The amount of research on the gathering and handling of healthcare data keeps growing. To support multi-center research, numerous institutions have sought to create a common data model (CDM). However, data quality issues continue to be a major obstacle in the development of CDM. To address these limitations, a data quality assessment system was created based on the representative data model OMOP CDM v5.3.1. Additionally, 2,433 advanced evaluation rules were created and incorporated into the system by mapping the rules of existing OMOP CDM quality assessment systems. The data quality of six hospitals was verified using the developed system and an overall error rate of 0.197% was confirmed. Finally, we proposed a plan for high-quality data generation and the evaluation of multi-center CDM quality.

Original language	English
Title of host publication	MEDINFO 2023 - The Future is Accessible
Subtitle of host publication	Proceedings of the 19th World Congress on Medical and Health Informatics
Editors	Jen Bichel-Findlay, Paula Otero, Philip Scott, Elaine Huesing
Publisher	IOS Press BV
Pages	349-353
Number of pages	5
ISBN (Electronic)	9781643684567
DOIs	https://doi.org/10.3233/SHTI230985
State	Published - 25 Jan 2024
Event	19th World Congress on Medical and Health Informatics, MedInfo 2023 - Sydney, Australia Duration: 8 Jul 2023 → 12 Jul 2023

Publication series

Name	Studies in Health Technology and Informatics
Volume	310
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	19th World Congress on Medical and Health Informatics, MedInfo 2023
Country/Territory	Australia
City	Sydney
Period	8/07/23 → 12/07/23

Bibliographical note

Publisher Copyright:
© 2024 International Medical Informatics Association (IMIA) and IOS Press.

Keywords

common data model
Data quality
data quality management system

Access to Document

10.3233/SHTI230985

Cite this

Oh, S. W., Ko, S. J., Im, Y. S., Jung, S., Choi, B. Y., Kim, J. Y., Park, S., Choi, W., & Choi, I. Y. (2024). Development of Integrated Data Quality Management System for Observational Medical Outcomes Partnership Common Data Model. In J. Bichel-Findlay, P. Otero, P. Scott, & E. Huesing (Eds.), MEDINFO 2023 - The Future is Accessible: Proceedings of the 19th World Congress on Medical and Health Informatics (pp. 349-353). (Studies in Health Technology and Informatics; Vol. 310). IOS Press BV. https://doi.org/10.3233/SHTI230985

Oh, Seol Whan ; Ko, Soo Jeong ; Im, Yun Seon et al. / Development of Integrated Data Quality Management System for Observational Medical Outcomes Partnership Common Data Model. MEDINFO 2023 - The Future is Accessible: Proceedings of the 19th World Congress on Medical and Health Informatics. editor / Jen Bichel-Findlay ; Paula Otero ; Philip Scott ; Elaine Huesing. IOS Press BV, 2024. pp. 349-353 (Studies in Health Technology and Informatics).

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abstract = "The amount of research on the gathering and handling of healthcare data keeps growing. To support multi-center research, numerous institutions have sought to create a common data model (CDM). However, data quality issues continue to be a major obstacle in the development of CDM. To address these limitations, a data quality assessment system was created based on the representative data model OMOP CDM v5.3.1. Additionally, 2,433 advanced evaluation rules were created and incorporated into the system by mapping the rules of existing OMOP CDM quality assessment systems. The data quality of six hospitals was verified using the developed system and an overall error rate of 0.197\% was confirmed. Finally, we proposed a plan for high-quality data generation and the evaluation of multi-center CDM quality.",

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Oh, SW, Ko, SJ, Im, YS, Jung, S, Choi, BY, Kim, JY, Park, S, Choi, W & Choi, IY 2024, Development of Integrated Data Quality Management System for Observational Medical Outcomes Partnership Common Data Model. in J Bichel-Findlay, P Otero, P Scott & E Huesing (eds), MEDINFO 2023 - The Future is Accessible: Proceedings of the 19th World Congress on Medical and Health Informatics. Studies in Health Technology and Informatics, vol. 310, IOS Press BV, pp. 349-353, 19th World Congress on Medical and Health Informatics, MedInfo 2023, Sydney, Australia, 8/07/23. https://doi.org/10.3233/SHTI230985

Development of Integrated Data Quality Management System for Observational Medical Outcomes Partnership Common Data Model. / Oh, Seol Whan; Ko, Soo Jeong; Im, Yun Seon et al.
MEDINFO 2023 - The Future is Accessible: Proceedings of the 19th World Congress on Medical and Health Informatics. ed. / Jen Bichel-Findlay; Paula Otero; Philip Scott; Elaine Huesing. IOS Press BV, 2024. p. 349-353 (Studies in Health Technology and Informatics; Vol. 310).

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

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Oh SW, Ko SJ, Im YS, Jung S, Choi BY, Kim JY et al. Development of Integrated Data Quality Management System for Observational Medical Outcomes Partnership Common Data Model. In Bichel-Findlay J, Otero P, Scott P, Huesing E, editors, MEDINFO 2023 - The Future is Accessible: Proceedings of the 19th World Congress on Medical and Health Informatics. IOS Press BV. 2024. p. 349-353. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI230985

Development of Integrated Data Quality Management System for Observational Medical Outcomes Partnership Common Data Model

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Bibliographical note

Keywords

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