Background
EBPM, an extension of EBM, entails the clarification of policy objectives and the utilization of research-derived evidence as the foundation for policy development, implementation, and assessment (practice). This concept stems from the recognition that preceding policies lack substantiation through empirical evidence. Consequently, endeavors have been undertaken to produce empirical evidence. Nevertheless, the challenge emerges when, despite the accumulation of evidence, its execution in realworld scenarios deviates from expectations, thereby prompting a focus on implementation science.
Our research concentrates on elucidating the practical implementation of preventive medical interventions, the efficacy of which is epidemiologically evident. In clinical medicine, evidence implementation is partially achieved through regulation via expert consultation of guidelines and systems. Conversely, the preventive medical interventions we focus on, particularly in the community and occupational domains, present greater challenges in implementation due to a higher number of stakeholders and less regulated actors. Additionally, challenges arise in generating evidence as predominant research often explores contributing factors (risks) rather than strategies for modifying these factors. For instance, while it is established that excessive salt intake contributes to hypertension and stroke, limited research addresses methods to reduce salt intake in daily life. Moreover, in Japan, policy formulation is infrequently grounded in evidence. Given this context, an approach that assesses policy effectiveness postimplementation and subsequently considers de-implementation is imperative. This entails adopting an action-research methodology that closely engages with the target, verifies effects through a PDCA (Plan-Do-Check-Act) framework, and iteratively improves interventions.
Verification of implementation of preventive medical interventions
Against the backdrop outlined above, the primary focus of the Data Health Research Unit centers on preventive medical intervention measures utilizing data, particularly data health plans implemented by medical insurers. Our research emphasizes the prerequisites for effective policy implementation. Notably, workplace-related studies have demonstrated a correlation between changes in individual health (metabolic syndrome) and the specific workplace environment (Kakinuma et al., 2019).
Additionally, previous studies have illustrated a positive association between sustained use of electronic tools and improved health outcomes (Nakao et al., 2020). Identifying these aspects, scholars have extracted factors conducive to a higher preventive intervention measure implementation rate, traditionally perceived as challenges related to the awareness of the target audience (Hamamatsu et al., 2021). However, advancing such research necessitates the systematic acquisition of comprehensive data, including organizational (insurersʼ) attributes, the insurer’s implemented measures, and their corresponding outcomes. Analyzing data solely at the individual level may overlook the collective influence exerted by the group or organization to which an individual belongs.
Therefore, it is crucial to conduct empirical research that integrates characteristics and environments at both the group and individual levels. To facilitate this, establishing a framework for continuous data acquisition from medical insurers is imperative. For occupational insurance (health insurance unions), we successfully developed the “Data Health Portal Site,” a tool standardizing data health plans, using subsidies from the Ministry of Health, Labor and Welfare. Presently, nearly all health insurance associations utilize data health portal sites for data collection. However, in the case of regional insurance (National Health Insurance), a platform akin to the Data Health Portal Site is yet to be established (Furui et al., 2019). To address this, a uniform framework for structuring and acquiring data has been developed and implemented across ten prefectures as of September 2022. Our commitment to intervention-type research involves leveraging insights from cross-sectional studies and accumulating scientific evidence at an elevated level. Notably, considerable efforts have been directed toward establishing the infrastructure for data acquisition. The transfer of the Data Health Portal Site to the Social Insurance Medical Fee Payment Fund in July 2022 has created an environment conducive to focused analysis.
Establishment of infrastructure for measurement and evaluation
Moreover, we are engaged in research that establishes the groundwork for validating the efficacy of preventive interventions through data analysis. In light of societal challenges, including a shortage of human resources and declining labor productivity in an aging society, companies have embraced the promotion of employee health as a national policy. However, the absence of easily measurable indicators assessing the impact and effectiveness of health investments prompted the Health Management Research Unit (2012‒2017), an extension of the Data Health Research Unit, to propose an index.
Specifically, the Single-Item Presenteeism Question, a one-item version developed by the University of Tokyo, measures the loss of labor productivity attributed to employee health/physical condition. The index’s validity has been substantiated (Muramatsu et al., 2021) and is presently incorporated into the government’s Health and Productivity Management Organization Certification System. While medical cost data are occasionally employed to assess the effectiveness of preventive interventions, historical analyses have been confined to mechanical aggregations based on injury and illness names in claims. To gain insights into the allocation of medical resources across diseases, we devised a method for disease-specific allocation of medical resources (Hiramatsu et al., 2022). This methodology has practical applications, including evaluating the cost-effectiveness of therapeutic and preventive interventions and visualizing the allocation status of medical resources, such as in end-of-life care. The development of these indices and methodologies stands as a foundational accomplishment in validating the effectiveness of preventive interventions and will underpin forthcoming research endeavors.
Educational Initiatives
The Data Health Research Unit has systematically advanced educational initiatives by seamlessly integrating them with academic research outcomes. Since 2018, we have conducted training for medical insurer health insurance associations through the Data Health Portal Site. For National Health Insurance (prefectures), the Prefectural Leadership Program has been offered since 2020. Furthermore, we have authored guidebooks for crafting data health plans, textbooks promoting health management, and manuals for preventing lifestyle-related diseases targeted at elementary school students.
The “Data Health Electronic Lecture” (comprising eight chapters in total) is accessible online. Recognizing the significance of practitioner education as a cornerstone for empirical research progress, we have extended our educational efforts. In response to the prevailing medical insurance framework, which primarily focuses on health education and guidance for adults, the Data Health Research Unit has initiated educational activities for children. We are now in the process of developing a program that integrates data health planning as a supplementary teaching material for elementary school health classes with a particular emphasis on lifestyle-related disease prevention education. Additionally, a “Children’s Health Insurance Newsletter” is being established for dependents (elementary school students) through the health insurance association. Our commitment to knowledge dissemination extends to undergraduate and graduate students at the University of Tokyo’s School of Medicine and Graduate School, specifically within the Department of Public Health Medicine, where academic findings are shared during classes.