The Tokushukai Group is one of the largest hospital groups in Japan. By connecting all the hospitals via a network, the group has been able to achieve centralized management of clinical information. The Tokushukai Group has been utilizing the data in various ways, such as participation in clinical trials, development of anticancer drug protocol systems, participation in BioBank Japan, and provision of data to MID‒NET®. These days, the Tokushukai Group has started to provide the data, named as the “Tokushukai Medical Database (TMD)”, to academia and industries for research purposes. The TMD mainly consists of inpatient and outpatient administrative claims data, Diagnosis Procedure Combination (DPC) data, electronic health records including blood test results and vital signs, and hospital-based cancer registry information. The strengths of the TMD include the availability of blood test results and vital signs in an standardized format, and the possibility to go back to each patient's electronic medical record from their claims data and DPC data, with the permission of headquarters and the ethics committee in the Tokushukai group. By taking advantage of these strengths, there have been increasing number of studies in the TMD, such as research on acute kidney injury defined as blood test results, hyperkalemia in dialysis patients, and validation study on rheumatoid arthritis. The issues newly raised during the utilization of data (e.g., echocardiography data not standardized) are expected to be resolved with the update of the network system for a wider range of research topics in the future.

There is growing interest in generating evidence from routinely collected real-world data to support medical and regulatory decision-making. However, longitudinal study designs using real-world data are often complex, and text-only descriptions make it difficult for most readers to understand their designs. To address this issue, in 2019, experts from industry, government, and academia developed the “design diagram,” a framework for visualizing longitudinal study designs. The design diagram uses standardized terminology and a graphical structure to communicate study design details to readers, thereby improving reproducibility. Based on previous work by a joint task force between the International Society for Pharmacoepidemiology (ISPE) and the Professional Society for Health Economics and Outcomes Research (ISPOR), the diagram includes a comprehensive set of key study parameters related to reproducibility. It successfully presents study designs in an unambiguous and intuitive manner. Diagrams have been proposed for various study designs, including cohort, nested case-control, and self-controlled designs. Recently, a new diagram was developed that adds at-a-glance elements to show the observability of the source data used in the study. The use of design diagrams is recommended in both the ISPE/ISPOR-endorsed harmonized protocol template (HARPER) and in reporting guidelines for pharmacoepidemiological research, and its widespread use is expected. This paper describes the structure of the design diagram and provides examples of its use. Effective use of design diagrams is expected to improve the reproducibility and reliability of database studies.

Although the recent revision of the ministerial ordinance on Good Post-marketing Study Practice (GPSP) included the utilization of medical information databases for post-marketing surveillance, there has been limited research on the validity of diagnosis codes and other outcome definitions in Japanese databases such as administrative claims (“receipt”) database. This task force proposed how to conduct good validations studies, based on the narrative review on around 100 published papers around the world. The established check list consists of ： (ⅰ) understanding the type of the database (e.g. administrative claims data, electronic health records, disease registry) ； (ii) understanding the setting of the validation study (e.g. “population-based” or not) ； (iii) defining the study outcome ； (iv) determining the way of linkage between databases ； (v) defining the gold standard ； (vi) selecting the sampling method (e.g. using the information of all patients in the database or a hospital, random sampling from all patients, random sampling from patients satisfying the outcome definition, random sampling from patients satisfying and not satisfying the outcome definition, “all possible cases” method) and sample size ； (vii) calculating the measures of validity (e.g. sensitivity, specificity, positive predictive value, negative predictive value) ； and (viii) discussing how to use the result for future studies. In current Japan, where the linkage between databases is logistically and legally difficult, most validation studies would to be conducted on a hospital basis. In such a situation, detailed description of hospital and patient characteristics is important to discuss the generalizability of the validation study result to the entire database. This report is expected to encourage and help to conduct appropriate validation studies.