Version 1
: Received: 1 November 2023 / Approved: 1 November 2023 / Online: 2 November 2023 (07:45:02 CET)
How to cite:
Henke, E.; Zoch, M.; Peng, Y.; Reinecke, I.; Sedlmayr, M.; Bathelt, F. Conceptual Design of a Generic Data Harmonization Process for OMOP CDM. Preprints2023, 2023110104. https://doi.org/10.20944/preprints202311.0104.v1
Henke, E.; Zoch, M.; Peng, Y.; Reinecke, I.; Sedlmayr, M.; Bathelt, F. Conceptual Design of a Generic Data Harmonization Process for OMOP CDM. Preprints 2023, 2023110104. https://doi.org/10.20944/preprints202311.0104.v1
Henke, E.; Zoch, M.; Peng, Y.; Reinecke, I.; Sedlmayr, M.; Bathelt, F. Conceptual Design of a Generic Data Harmonization Process for OMOP CDM. Preprints2023, 2023110104. https://doi.org/10.20944/preprints202311.0104.v1
APA Style
Henke, E., Zoch, M., Peng, Y., Reinecke, I., Sedlmayr, M., & Bathelt, F. (2023). Conceptual Design of a Generic Data Harmonization Process for OMOP CDM. Preprints. https://doi.org/10.20944/preprints202311.0104.v1
Chicago/Turabian Style
Henke, E., Martin Sedlmayr and Franziska Bathelt. 2023 "Conceptual Design of a Generic Data Harmonization Process for OMOP CDM" Preprints. https://doi.org/10.20944/preprints202311.0104.v1
Abstract
To gain insight into the real-life care of patients in the healthcare system, data from hospital information systems and insurance systems are required. Consequently, linking clinical data with claims data is necessary. To ensure their syntactic and semantic interoperability, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) was chosen. However, there is no detailed guide that would allow researchers to follow a consistent process for data harmonization. Thus, the aim of this paper is to conceptualize a generic data harmonization process for OMOP CDM. For this purpose, we conducted a literature review focusing on publications that address the harmonization of clinical or claims data in OMOP CDM. Subsequently, the process steps used and their chronological order were extracted for each included publication. The results were then compared to derive a generic sequence of the process steps. From 23 publications included, a generic data harmonization process for OMOP CDM was conceptualized, consisting of nine process steps: dataset specification, data profiling, vocabulary identification, coverage analysis of vocabularies, semantic mapping, structural mapping, extract-transform-load-process, qualitative and quantitative data quality analysis. This process can be used as a step-by-step guide to assist other researchers in harmonizing source data in OMOP CDM.
Keywords
OMOP; OHDSI; interoperability; data harmonization; clinical data; claims data
Subject
Public Health and Healthcare, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
