2026 Volume 53 Issue 2 Pages 302-307
In Japan, health promotion programs have traditionally focused on "implementation," with key performance indicators (KPIs) based on participation rates. However, demographic aging and social structural changes have made uniform implementation insufficient to meet diverse health needs. In response, the third phase of the Data Health Plan shifted its objective from "implementation" to "problem-solving," emphasizing data-driven identification of health issues and evidence-based program design. This paper examines strategies to enhance the effectiveness of Specific Health Checkups and Specific Health Guidance through data utilization, organized into three perspectives: (1) program design, (2) program evaluation, and (3) knowledge extraction, followed by considerations for building a data infrastructure to advance implementation science.
For program design, analyzing medical expenditure and health checkup data enables targeted interventions. Our analysis of nine prefectures revealed that over 90% of identified health issues were linked to corresponding programs, indicating progress toward problem-solving approaches. At the individual level, higher understanding of checkup results was associated with improved adherence and subsequent health outcomes.
Program evaluation has evolved through the introduction of standardized indicators in the third phase, supported by systems such as the KDB and the Data Health Portal, enabling objective comparisons and reducing administrative burden. Knowledge extraction requires strengthening the "Check" and "Act" phases of PDCA by incorporating scientific evaluation and evidence-based policy making (EBPM). Tools such as the "Health Program Chart" have facilitated structured documentation and sharing of effective practices.
Finally, improving the effectiveness of Specific Health Checkups and Guidance requires data-driven strategies to clarify necessity and visualize benefits. Personalized risk projections, simulation of complication risk reduction, and indicators for productivity and resource optimization can drive behavioral change among individuals, employers, and municipalities. Data utilization thus serves as a foundation for enhancing health promotion program quality and promoting societal well-being.
