Objective：Compared to research conducted under a controlled system such as a clinical trial, differences in characteristics among observers (medical care providers) can be an issue when conducting observational research in general or research using RWD in particular. In particular, systematic differences in observational behaviors (attitudes) by department may cause confusion in interpreting study results, so we aimed to quantify the differences in behaviors toward patient observation by department.
Design：Descriptive aggregation using “Millennium medical record” Database. Basic statistics such as median values for the number of characters in the clinical summary, which is a free entry column, are obtained for each department. Based on the purpose of this study, it was judged that it was appropriate to omit the description after hospitalization because the number of characters described in the part of the clinical course up to hospitalization in the clinical summary was included in the character count.
Results：The median number of letters in internal medicine was 503. The ratios of the median number of letters to the median number of letters in various departments were surgery (0.55), ophthalmology (0.57), psychiatry/psychosomatic medicine (2.85), pediatrics (1.19), obstetrics/gynecology (1.04), and dermatology, orthopedic surgery/plastic surgery (0.41), respectively.
Conclusion：There are characteristic differences in the number of letters in the free entry items depending on the medical department.

Background：Research utilizing secondary data such as electronic medical information databases, raises concerns about the adequacy of outcome identification. Before initiating a study, researchers develop phenotyping algorithms to identify outcomes. However, if required, validation studies of these algorithms are rarely conducted leading to inefficiencies and a lack of transparency in the research process.
Methodology：Outcome Definition Repository (ODR) is an online code repository database and knowledge platform, that compiles various phenotyping algorithms to identify research outcomes from electronic medical information databases. It was collaboratively developed by three related academic societies: the Japanese Society for Epidemiology, the Japanese Society for Pharmacoepidemiology, and the Japanese Society for Clinical Epidemiology. The first step was to establish specific criteria for the functioning of the ODR, including defining the registration targets and items, followed by the construction of a test database to conduct trials. A User Acceptance Testing (UAT) was performed, by entering data from previously published articles. Building upon the pilot ODR, an operational version of the system was developed and tested. In May 2023, the database was made accessible to the limited members of the three societies, to gather feedback for improvement. Based on their input, the system was updated, and by 2024, it was introduced to a wider group of target members from each society, in accordance with their internal policies. The primary functions of the ODR are registering and retrieving outcome definitions. As of March 2025, 412 publications and 3,838 outcome definitions had been registered. The platform is operated through financial contributions from three societies.
Strategies for Dissemination and Facilitation：Developing strategies to encourage code registration is essential, such as implementing incentive programs. Establishing a dedicated organization specializing in ODR will ensure its sustainable and progressive operation. Therefore, additional support from academia, industry, and government is crucial.
Conclusion：The ODR has been accessible to certain members of the three governing societies, since 2024. Moving forward, additional efforts are needed to promote ODR usage, encourage code registration, and ensure its sustainable operation.

When a randomized controlled trial (RCT) is infeasible, unethical, or untimely, causal inference from observational data can serve as an effective alternative for scientific and clinical decision-making. However, observational studies harbor methodological vulnerabilities―not only confounding due to lack of randomization, but also selection bias or immortal time, arising from flawed study designs―that can fundamentally distort effect estimates. Target trial emulation has gained prominence as a framework for addressing these challenges. This approach has two steps：(1) specifying the protocol of a hypothetical pragmatic RCT (the target trial) that would answer the causal question of interest, and (2) explicitly emulating that trial with existing observational data. Its greatest contribution is the elimination of ambiguous causal questions in observational studies, transforming them into well-defined causal estimands. In this article, we synthesize the conceptual foundations of target trial emulation and detail methodological considerations for its implementation. As an illustrative example, we describe an observational study that compared denosumab with oral bisphosphonates for cardiovascular safety and fracture-prevention effectiveness in maintenance dialysis patients with osteoporosis. The target trial framework offers a structured approach that prevents design-induced biases and clarifies the limitations inherent in observational data, thereby enabling epidemiologists who grapple with causal questions to draw more valid inferences.