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 DQ4HEALTH 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.
Highlights
Healthcare data is evaluated as data with economic value; subsequently, it attracts global attention from observational studies and clinical studies alike [1,2,3]
The quality evaluation method refers to four evaluation criteria (NPR, weighted pass rate (WPR), net dimensional pass rate (NDPR), and weighted dimensional pass rate (WDPR)) for easy access to expert reviews in evaluating healthcare data
We developed a validation rule that can be applied to OMOP Common Data Model (CDM) by selecting frequent values through a review of previous studies on the existing information system quality and healthcare quality dimensions
Summary
Healthcare data is evaluated as data with economic value; subsequently, it attracts global attention from observational studies and clinical studies alike [1,2,3]. Healthcare data can be utilized remarkably rapidly, due to its large data set, continuity over time, and timely availability. Despite this potential, it remains difficult to analyze and integrate multicenter data due to skepticism among medical centers and different data structures of electronic health record (EHR) systems [4,5,6,7,8,9,10,11]. Data quality studies continue to use tools and assessment approaches to improve the quality of EHR data [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30].
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