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Abstract Allopurinol is the first-line agent for patients with gout, including those with moderate‐to‐severe chronic kidney disease. However, increased thyroid-stimulating hormone (TSH) levels are observed in patients with long-term allopurinol treatment. This large-scale, nested case–control, retrospective observational study analysed the association between allopurinol use and increased TSH levels. A common data model based on an electronic medical record database of 19,200,973 patients from seven hospitals between January 1997 and September 2020 was used. Individuals aged > 19 years in South Korea with at least one record of a blood TSH test were included. Data of 59,307 cases with TSH levels > 4.5 mIU/L and 236,508 controls matched for sex, age (± 5), and cohort registration date (± 30 days) were analysed. An association between the risk of increased TSH and allopurinol use in participants from five hospitals was observed. A meta-analysis (I 2 = 0) showed that the OR was 1.51 (95% confidence interval: 1.32–1.72) in both the fixed and random effects models. The allopurinol intake group demonstrated that increased TSH did not significantly affect free thyroxine and thyroxine levels. After the index date, some diseases were likely to occur in patients with subclinical hypothyroidism and hypothyroidism. Allopurinol administration may induce subclinical hypothyroidism.

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.

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.