Background: Robot-assisted radical cystectomy is becoming a common treatment for bladder carcinoma. However, in comparison with open radical cystectomy, its cost-effectiveness has not been confirmed. Although few published reviews have compared total costs between the two surgical procedures, no study has compared segmental costs and explained their impact on total costs. Methods: A systematic review was conducted based on studies on the segmental costs of open, laparoscopic, and robot-assisted radical cystectomy using PubMed, Web of Science, and Cochrane Library databases to provide insight into cost-effective management methods for radical cystectomy. The segmental costs included operating, robot-related, complication, and length of stay costs. A sensitivity analysis was conducted to determine the impact of the annual number of cases on the per-case robot-related costs. Results: We identified two studies that compared open and laparoscopic surgeries and nine that compared open and robotic surgeries. Open radical cystectomy costs were higher than those of robotic surgeries in two retrospective single-institution studies, while robot-assisted radical cystectomy costs were higher in 1 retrospective single-institution study, 1 randomized controlled trial, and 4 large database studies. Operating costs were higher for robotic surgery, and accounted for 63.1–70.5% of the total robotic surgery cost. Sensitivity analysis revealed that robot-related costs were not a large proportion of total surgery costs in institutions with a large number of cases but accounted for a large proportion of total costs in centers with a small number of cases. Conclusions: The results show that robot-assisted radical cystectomy is more expensive than open radical cystectomy. The most effective methods to decrease costs associated with robotic surgery include a decrease in operating time and an increase in the number of cases. Further research is required on the cost-effectiveness of surgeries, including quality measures such as quality of life and quality-adjusted life years.

Background: Apps for real-time continuous glucose monitoring (CGM) on smartphones and other devices linked to CGM systems have recently been developed, and such CGM apps are also coming into use in Japan. In comparison with conventional retrospective CGM, the use of CGM apps improves patients’ own blood glucose control, which is expected to help slow the progression of type 2 diabetes mellitus (DM) and prevent complications, but the effect of their introduction on medical costs remains unknown. Objective: Our objective in this study was to perform an economic appraisal of CGM apps from the viewpoint of assessing public medical costs associated with type 2 DM, using the probability of developing type 2 DM–associated complications, and data on medical costs and utility value to carry out a medical cost simulation using a Markov model in order to ascertain the cost-effectiveness of the apps. Methods: We developed a Markov model with the transition states of insulin therapy, nephrosis, dialysis, and cardiovascular disease, all of which have a major effect on medical costs, to identify changes in medical costs and utility values resulting from the introduction of a CGM app and calculated the incremental cost-effectiveness ratio (ICER). Results: The ICER for CGM app use was US $33,039/quality-adjusted life year (QALY). Conclusions: Sensitivity analyses showed that, with the exception of conditions where the transition probability of insulin therapy, utility value, or increased medical costs increases, the ICER for the introduction of CGM apps was below the threshold of US $43,478/QALY used by the Central Social Insurance Medical Council. Our results provide basic data on the cost-effectiveness of introducing CGM apps, which are currently starting to come into use.

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.

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.

Trend of Gini coefficient of healthcare resources in China from 1998 to 2016 Yuchen Yang, Yasuhiro Morii, Kensuke Fujiwara, Tomoki Ishikawa, Hiroko Yamashina, Teppei Suzuki, Jun Nakaya, Katsuhiko Ogasawara