Aim
　  To determine the impact of increased copayment on long-term care (LTC) and medical service utilization among older adults in Japan.
Methods
　  LTC and medical claims data were obtained for individuals aged ≥ 75 years as of August 1, 2014, and those utilizing LTC services in Fukuoka Prefecture, Japan, between August 2014 and March 2019. Participants were categorized into three groups: the 10% group (no copayment change), the 20% group (copayment increased from 10% to 20% in August 2015), and the 30% group (copayment increased from 20% to 30% in August 2018). Monthly panel data was constructed , and controlled interrupted time series analysis was used to estimate changes in LTC and medical expenditures before and after the copayment increases.
Results
　  Of 32,295 participants, 2991, 1459, and 925 were in the 10%, 20%, and 30% groups, respectively. LTC expenditure increased significantly by an average of 502.5 yen per month during the 12 months before the first intervention, with a further significant increase of 560.0 yen between the two intervention periods. The 30% group had a significant decrease (569.9 yen/month) in facility service expenditures and a significant increase (996.5 yen/month) in hospitalization expenditures after the second intervention. Conclusions
　  No clear reduction in LTC expenditure was observed, suggesting a possible shift from LTC to medical care. Further research is needed to examine measures, including copayments, for the appropriate medical care and LTC service use.

View full abstract

   This study assesses the effectiveness and variability of responses generated by various AI models in providing guidance on insulin injection guidance. By examining the capacity of AI in facilitating diabetes self-management, the research seeks to inform and advance the integration of AI technologies into healthcare practices, particularly from a health policy perspective.
   To evaluate the performance of AI systems in delivering insulin injection guidance, we compared four AI models: the Diabetes Self-Management GPTs Support System (DSM-GPTs), a customized AI developed with ChatGPT's GPTs, and three general-purpose AI models (GPT-4 Omni, Gemini 2.0 Flash, and Claude 3.7 Sonnet). Standardized prompts tailored for both normal and older diabetes patients were employed to assess the models. The outputs were analyzed using metrics such as word count, adherence to established injection protocols, and scores generated by a customized Scoring-GPTs system, rated on a 100-point scale.
   Eighty responses (10 per model per patient profile) were evaluated. All models achieved high median quality scores (range 90–96/100). Claude 3.7 Sonnet obtained the highest mean score (95.7 ± 3.4), followed by GPT-4 Omni (94.1 ± 3.2), Gemini 2.0 Flash (92.4 ± 6.7) and DSM-GPTs (90.6 ± 3.4) . GPT-4 Omni exhibited the lowest score variability, whereas Gemini 2.0 Flash showed the widest dispersion, reflecting less predictable performance. Response length differed markedly across models: Gemini produced the longest explanations (median ≈ 700 words) and Claude the briefest (median ≈ 310 words), while DSM-GPTs and GPT-4 Omni provided intermediate-length, reader-friendly answers. Procedural analysis of 20 key injection checkpoints revealed that GPT-4 Omni fully covered 66% of items, DSM-GPTs: 44%, Claude 3.7 Sonnet: 55%, and Gemini 2.0 Flash: 68%. GPT-4 Omni and DSM-GPTs were particularly consistent in hygiene and safety steps, whereas Gemini 2.0 Flash omitted basic preparation steps more frequently. DSM-GPTs uniquely incorporated geriatric-specific considerations (e.g., tremor, visual impairment) in 7/10 geriatric scenarios, exceeding the coverage of general-purpose models.
   Large-language-model (LLM) systems can generate high-quality insulin-injection guidance, but substantial differences exist in completeness, coherence and brevity. GPT-4 Omni balanced accuracy with concise delivery, whereas DSM-GPTs provided the most tailored geriatric advice. These findings highlight the need for benchmark frameworks and policy oversight to ensure safe, equitable deployment of AI-driven self-management tools in older adults in the future.

View full abstract

 In this study, we compared the pharmaceutical items, their numbers, and therapeutic categories listed in the essential medicines lists of various countries to examine policies for securing pharmaceuticals within the context of establishing pharmaceutical supply systems and national security policies. This study is a comparative survey based on publicly available information from the essential medicines lists of Japan, the United States (US), the European Union (EU), the World Health Organization (WHO), Thailand, and China, published between 2018 and 2023. Although the criteria for selecting essential medicines lists vary across the countries studied, 25 items were designated as essential medicines across Japan, the US, EU, WHO, Thailand, and China within the Japan List of Essential Medicines, accounting for 5.3% of Japan’s essential medicines list. The number of overlapping medicines between the lists of each country was determined, and the precision, recall, and F1 scores were calculated. The highest F1 score (consistency) was observed between China and WHO. Comparison of the US, EU, WHO, and CH against Japan and comparison of WHO, China and Thailand against the US as the baseline yielded F1 score below 0.2, indicating that the Japan and the US List of Essential Medicines have low level of consistency with the others. Regarding the characteristics and selection criteria of the medicines included in the essential medicines lists, Japan, the US, and the EU all focus on the continuity of pharmaceutical supply. However, Japan’s selections are based on requests from Japanese medical societies. In the US, medicines are designated as those used in acute care settings for conditions that immediately threaten life or as those required for patients to continue outpatient treatment. The EU evaluates medicines based on the diseases they target and the availability of alternatives. WHO focuses on medicines that are the most effective, safe, and cost-effectiveness for priority diseases in developing countries. Thailand emphasizes medicines available at reasonable prices for representative diseases, while China prioritizes medicines that meet healthcare needs, can be stably supplied, and are provided at appropriate prices. The Japan List of Essential Medicines and the US List of Essential Medicines exhibit high degree of heterogeneity compared with the NLEMs of the other countries, reflecting its unique selection process. This makes international coordination challenging from the perspective of securing pharmaceuticals. Moving forward, if Japan and the US aim to pursue international cooperation in pharmaceutical security, it will need to revise its list of essential medicines and actively work to ensure that the medicines Japan and the US deem necessary are recognized by other countries.

View full abstract