SUMMARYSimilar to the healthcare systems in other industrialized countries, the Japanese healthcare system is facing the problem of increasing medical expenditure. In Japan, this situation may be primarily attributed to advanced technological developments, an aging population, and increasing patient demand. Japan also faces the problem of a declining youth population due to a low birth rate. Taken together, these problems present the healthcare system with a very difficult financial situation. Several reforms have been undertaken to contain medical expenditure, such as increasing employee copayment for health insurance from 10% to 20% in 1997 and from 20% to 30% in 2003 in order to curb unnecessary visits to medical institutions. Since the aging of the Japanese population is inevitable, a suitable method to contain medical expenditure may be to screen individuals who are likely to develop lifestyle-related diseases and conduct early intervention programs for them to prevent the development of diseases such as myocardial infarction or stroke that are costly to treat. If this goal is attained, it may contribute to the containment of medical expenditure as well as to improving the quality of life of the elderly. Therefore, the Japanese Ministry of Health, Labor and Welfare has decided to introduce a nationwide health screening and intervention program specifically targeting the metabolic syndrome commencing April 2008. Here, we discuss (1) the background of the Japanese healthcare system and the problems facing it, (2) the underlying objective and details of the new screening program, and (3) the expected impact of the program. (Int Heart J 2008; 49: 193-203)

To evaluate condition-specific antibiotic prescription rates and the appropriateness of antibiotic use in outpatient settings in Japan. Methods: Using Japan's national administrative claims database, all outpatient visits with infectious disease diagnoses were linked to reimbursed oral antibiotic prescriptions. Prescription rates stratified by age, sex, prefecture, and antibiotic category were determined for each infectious disease diagnosis. The proportions of any antibiotic prescription to all infectious disease visits and the proportions of first-line antibiotic prescriptions to all antibiotic prescriptions were calculated for each infectious disease diagnosis. Results: Of the 659 million infectious disease visits between April 2012 and March 2015, antibiotics were prescribed in 266 million visits (704 prescriptions per 1000 population per year). Third-generation cephalosporins, macrolides, and quinolones accounted for 85.9% of all antibiotic prescriptions. Fifty-six percent of antibiotic prescriptions were directed toward infections for which antibiotics are generally not indicated. The diagnoses with frequent antibiotic prescription were bronchitis (184 prescriptions per 1000 population per year), viral upper respiratory infections (166), pharyngitis (104), sinusitis (52), and gastrointestinal infection (41), for which 58.3%, 40.6%, 58.9%, 53.9%, and 26.1% of visits antibiotics were prescribed, respectively. First-line antibiotics were rarely prescribed for pharyngitis (8.8%) and sinusitis (9.8%). More antibiotics were prescribed for children aged 0-9 years, adult women, and patients living in western Japan. Conclusions: Antibiotic prescription rates are high in Japan. Acute respiratory or gastrointestinal infections, which received the majority of the antibiotics generally not indicated, should be the main targets of antimicrobial stewardship intervention.

No abstract

No abstract

Background Telehealth using telephones or online communication is being promoted as a policy initiative in several countries. However, there is a lack of research on telehealth utilization in a country such as Japan that offers free access to medical care and regulates telehealth provision—particularly with respect to COVID-19. Objective The present study aimed to clarify telehealth utilization, the characteristics of patients and medical institutions using telehealth, and the changes to telehealth in Japan in order to support the formulation of policy strategies for telehealth provision. Methods Using a medical administrative claim database of the National Health Insurance and Advanced Elderly Medical Service System in Mie Prefecture, we investigated patients who used telehealth from January 2017 to September 2021. We examined telehealth utilization with respect to both patients and medical institutions, and we determined their characteristics. Using April 2020 as the reference time point for COVID-19, we conducted an interrupted time-series analysis (ITSA) to assess changes in the monthly proportion of telehealth users to beneficiaries. Results The number of telehealth users before the reference time point was 13,618, and after the reference time point, it was 28,853. Several diseases and conditions were associated with an increase in telehealth utilization. Telehealth consultations were mostly conducted by telephone and for prescriptions. The ITSA results showed a sharp increase in the proportion of telehealth use to beneficiaries after the reference time point (rate ratio 2.97; 95% CI 2.14-2.31). However, no apparent change in the trend of increasing or decreasing telehealth use was evident after the reference time point (rate ratio 1.00; 95% CI 1.00-1.01). Conclusions We observed a sharp increase in telehealth utilization after April 2020, but no change in the trend of telehealth use was evident. We identified changes in the characteristics of patients and providers using telehealth.

No abstract

No abstract

Background: Driven by the rapid aging of the population, Japan introduced public long-term care insurance to reinforce healthcare services for the elderly in 2000. Precisely predicting future demand for long-term care services helps authorities to plan and manage their healthcare resources and citizens to prevent their health status deterioration. Methods: This paper presents our novel study for developing an effective model to predict individual-level future long-term care demand using previous healthcare insurance claims data. We designed two discriminative models and subsequently trained and validated the models using three learning algorithms with medical and long-term care insurance claims and enrollment records, which were provided by 170 regional public insurers in Gifu, Japan.Results: The prediction model based on multiclass classification and gradient-boosting decision tree achieved practically high accuracy (weighted average of Precision, 0.872; Recall, 0.878; and F-measure, 0.873) for up to 12 months after the previous claims. The top important feature variables were indicators of current health status (e.g., current eligibility levels and age), risk factors to worsen future healthcare status (e.g., dementia), and preventive care services for improving future healthcare status (e.g., training and rehabilitation). Conclusions: The intensive validation tests have indicated that the developed prediction method holds high robustness, even though it yields relatively lower accuracy for specific patient groups with health conditions that are hard to distinguish.