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.

What is known and objectives In Korea, the side effects of sodium‐glucose cotransporter 2 inhibitors (SGLT2i) have not been clearly reported, aside from voluntary reporting. We aimed to develop detection algorithms for SGLT2i‐related genital tract infections (GTIs) and urinary tract infections (UTIs) via a common data model (CDM), an electronic medical record–based database for supporting multi‐hospital clinical research. We estimated the occurrence of GTIs and UTIs and—by assessing the status of each step of the algorithm—we also aimed to determine how clinicians responded to the SGLT2i‐related GTIs and UTIs. Methods We targeted all patients who were prescribed SGLT2i at Catholic University Seoul St. Mary's Hospital and Hallym University Dongtan Sacred Heart Hospital from January 2014 to August 2018. We developed algorithms for detection of SGLT2i‐related GTIs or UTIs that divided patients into "most likely," "possibly" or "less likely" categories of GTIs or UTIs. The numbers of patients at each step were extracted. Results and discussion A total of 4253 patients received their first prescription of SGLT2i. According to the algorithm used in this study, the proportions of "most likely GTI" and "possibly GTI" were 0.9% (37 out of 4253) and 19.4% (826 out of 4253 patients), respectively. Similarly, the proportions of "most likely UTI" and "possibly UTI" were 0.9% (38 out of 4253) and 20.2% (858 out of 4253 patients), respectively. Compared to the various existing prospective studies, both GTIs and UTIs showed lower occurrence among patients who met "most likely" criteria and higher occurrence among those who met "possibly" criteria. When a GTI or UTI occurred or was suspected, the overall rate of discontinuing SGLT2i was 51.8% (1721 out of 3323). Despite a confirmed or suspected GTI and an UTI, 62.8% (1460 out of 2323) and 14.2% (142 out of 1000) of patients continued to take SGLT2i, respectively. The discontinuation rate for suspected GTIs was significantly lower than that for suspected UTIs (37.2% vs. 85.8%, p < 0.001). What is new and conclusion In this study, although the GTIs appeared to have a similar occurrence as UTIs, however, the discontinuation rate of SGLT2i for suspected GTIs was relatively lower. Our study is novel in that we identified how the physicians approached SGLT2i‐related GTIs or UTIs at each step in a real‐world clinical practice setting. Although we could estimate SGLT2i‐related GTIs and UTIs via CDM, we were limited in our ability to accurately detect mild drug side effects via CDM, which lacked data for operational definition.

The importance of clinical information related to specimens is increasing due to the research on human biological specifications being conducted worldwide. In order to utilize data, it is necessary to define the range of data and develop a standardized system for collected resources. The purpose of this study is to establish clinical information standardization and to allow clinical information management systems to improve the utilization of biological specifications. The KBN CDM, consisting of 18 tables and 177 variables, was developed. The clinical information codes were mapped in standard terms. The 27 diseases in the group were collected from 17 biobanks, and all disorders not belonging to the group were standardized and loaded. We also developed a system that provides statistical visualization screens and data retrieval tools for data collection. This study developed a unified management system to model KBN CDM that collects standardized data, manages clinical information, and shares the information systematically. Through this system, all participating biobanks can be integrated into one system for integrated management and research.

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.

Kang DY, et al. J Korean Med Sci. 2022 Feb;37(7):e53. https://doi.org/10.3346/jkms.2022.37.e53