Factors and Challenges in Increasing the Utilization Rate of a New Long-term Care Service (Kantaki) in a Superaging Society: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Japan is a superaging society unparalleled in the world. Elderly people who need medical care do not receive adequate support in the community. As a new service to address this issue, a small-scale multifunctional in-home care nursing service called Kantaki was created in 2012. Kantaki, in collaboration with a primary physician, operates 24 hours a day and provides various nursing services (home visits, home care, day care, and overnight stays) to older people living in the community. The Japanese Nursing Association is working hard to promote this system; however, its low utilization rate is an issue. OBJECTIVE: This study aimed to determine factors influencing the utilization rate of Kantaki facilities. METHODS: This was a cross-sectional study. A questionnaire on the operation of Kantaki was sent to all administrators of Kantaki facilities operating in Japan from October 1 to December 31, 2020. A multiple regression analysis was used to determine factors associated with a high utilization rate. RESULTS: Responses from 154 of the 593 facilities were analyzed. The average utilization rate for all valid responding facilities was 79.4%. The average number of actual users and the break-even point were almost equal, resulting in little surplus profit from facility operations. A multiple regression analysis showed that factors that had a significant impact on the utilization rate included the break-even point, a surplus of users relative to the break-even point (ie, the margin of revenues), the number of months in office of the administrator, the type of corporation (ie, nonprofit), and Kantaki's profit from operating home-visit nursing offices. The break-even point, a surplus of users relative to the break-even point, and the number of months in office of the administrator were robust. In addition, support for reducing the burden on family helpers, a service sought by the system, significantly and negatively affected the utilization rate. In the analysis that removed the most influential factors, the cooperation of the home-visit nursing office, Kantaki's profit from operating the home-visit nursing office, and the number of full-time care workers were significantly related. CONCLUSIONS: To improve the utilization rate, managers need to stabilize their organization and increase profitability. However, a positive relationship was found between the break-even point and utilization rate, suggesting that simply increasing users did not contribute to cost reduction. Moreover, providing services that meet the needs of individual clients may result in lower utilization rates. These results, which are inconsistent with common sense, reflect the divergence between the assumptions underlying the system's design and actual conditions. To solve these issues, institutional reforms, such as an increase in nursing care fee points, may be necessary.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".