Transformation of Electronic Health Records and Questionnaire Data to OMOP CDM: A Feasibility Study Using SG_T2DM Dataset.

Autor:	Sathappan SMK; Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore., Jeon YS; Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore., Dang TK; Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore., Lim SC; Clinical Research Unit, Khoo Teck Puat Hospital, Singapore, Singapore., Shao YM; Clinical Research Unit, Khoo Teck Puat Hospital, Singapore, Singapore., Tai ES; Division of Endocrinology, National University Hospital, Singapore, Singapore., Feng M; Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore.; Institute of Data Science, National University of Singapore, Singapore, Singapore.
Jazyk:	angličtina
Zdroj:	Applied clinical informatics [Appl Clin Inform] 2021 Aug; Vol. 12 (4), pp. 757-767. Date of Electronic Publication: 2021 Aug 11.
DOI:	10.1055/s-0041-1732301
Abstrakt:	Background: Diabetes mellitus (DM) is an important public health concern in Singapore and places a massive burden on health care spending. Tackling chronic diseases such as DM requires innovative strategies to integrate patients' data from diverse sources and use scientific discovery to inform clinical practice that can help better manage the disease. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) was chosen as the framework for integrating data with disparate formats. Objective: The study aimed to evaluate the feasibility of converting Singapore based data source, comprising of electronic health records (EHR), cognitive and depression assessment questionnaire data to OMOP CDM standard. Additionally, we also validate whether our OMOP CDM instance is fit for the purpose of research by executing a simple treatment pathways study using Atlas, a graphical user interface tool to conduct analysis on OMOP CDM data as a proof of concept. Methods: We used de-identified EHR, cognitive, and depression assessment questionnaires data from a tertiary care hospital in Singapore to convert it to version 5.3.1 of OMOP CDM standard. We evaluate the OMOP CDM conversion by (1) assessing the mapping coverage (that is the percentage of source terms mapped to OMOP CDM standard); (2) local raw dataset versus CDM dataset analysis; and (3) Implementing Harmonized Intrinsic Data Quality Framework using an open-source R package called Data Quality Dashboard. Results: The content coverage of OMOP CDM vocabularies is more than 90% for clinical data, but only around 11% for questionnaire data. The comparison of characteristics between source and target data returned consistent results and our transformed data did not pass 38 (1.4%) out of 2,622 quality checks. Conclusion: Adoption of OMOP CDM at our site demonstrated that EHR data are feasible for standardization with minimal information loss, whereas challenges remain for standardizing cognitive and depression assessment questionnaire data that requires further work. Competing Interests: None declared. (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)
Databáze:	MEDLINE
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