# Multi-Center Healthcare Data Quality Measurement Model and Assessment Using OMOP CDM
###### Abstract  
Healthcare data has economic value and is evaluated as such. Therefore, it attracted global attention from observational and clinical studies alike. Recently, the importance of data quality research emerged in healthcare data research. Various studies are being conducted on this topic. In this study, we propose a DQ4 HEALTH model that can be applied to healthcare when reviewing existing data quality literature. The model includes 5 dimensions and 415 validation rules. The four evaluation indicators include the net pass rate (NPR), weighted pass rate (WPR), net dimensional pass rate (NDPR), and weighted dimensional pass rate (WDPR). They were used to evaluate the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) at three medical institutions. These indicators identify differences in data quality between the institutions. The NPRs of the three institutions (A, B, and C) were 96.58%, 90.08%, and 90.87%, respectively, and the WPR was 98.52%, 94.26%, and 94.81%, respectively. In the quality evaluation of the dimensions, the consistency was 70.06% of the total error data. The WDPRs were 98.22%, 94.74%, and 95.05% for institutions A, B, and C, respectively. This study presented indices for comparing quality evaluation models and quality in the healthcare field. Using these indices, medical institutions can evaluate the quality of their data and suggest practical directions for decreasing errors.  
healthcare data; OMOP CDM; multisite study; data quality assessment Article  
* 2021 2021 30 September 2021 2 2021 30 September 2021 2 2021 30 September 2021 2 2021 30 September 2021 2 2021 30 September 2021 2 2021 30 September 2021 2 2021 30 September 2021 2 2021 30 September 2021 2 2021 30 September 2021 30 September 2021 2 20  
the OMOP CDM V4.0 conversion process in the clinical data warehouse (CDW) of a hospital [14]. Additionally, the scalability of research on the detailed issues of the established common standard model is emphasized. [PERSON] proposed a rule-template schematic model. This study emphasizes that quality evaluation is carried out by constructing a rule-based model and deriving dimensions through an expert group composed of IT experts and clinicians [30]. When reviewing healthcare research, [PERSON] points out that different institutions have different methodologies for data quality [11]. Institutions can work closely with multiple stakeholders to address healthcare data quality issues. Some of these stakeholders may include clinicians, administrators, and IT professionals. Despite the importance of the quality of healthcare data, there were few studies on multicenter quality management indices involving research.  
Data quality assessment is vital in multicenter studies. The Observational Health Data Sciences and Informatics (OHDSI), a representative multicenter research network, is a network in which multiple stakeholders collaborate by analyzing large-scale medical data converted into the Common Data Model (CDM) format [12; 16]. The CDM enables the integration of heterogeneous data from healthcare institutions by presenting a standardized data model from other healthcare institutions. In addition, it is possible to simplify the research network between institutions by converting it into analytical data.  
This study presents a new conceptual model that can be applied to the quality of healthcare data through the review of existing data quality literature. In addition, this study selected medical institutions that established a large-scale cohort based on OHDSI's CDM. A data quality evaluation was then performed. This study contributes to the improvement of healthcare data quality by checking the difference in quality for each medical institution after a quality evaluation.