Road Traffic Crashes and Fatalities in Japan 2000-2010 With Special Reference to the Elderly Road User

Objective: To investigate comparative road user crash and fatality rates in Japan between 2000 and 2010 in the elderly and young. Methods: Data from the Japan Ministry of Health, Labor and Welfare Vital Statistics Database and the Institute for Traffic Accident Research and Data Analysis were used to calculate crash rates by age group, vehicle, and license category. Results: Fatal crash rates per 100,000 licensed drivers for 4-wheeled motor vehicle drivers decreased by 53, 56, and 42 percent among the 65-69, 70-74, and >= 75 age groups between 2000 and 2010, respectively, compared to 66 and 60 percent among the 16-19 and 20-24 age groups, respectively. Fatal crash rates per 100,000 licensed riders for 2-wheeled motor vehicles decreased by 64, 23, and 33 percent in the 65-69, 70-74, and >= 75 age groups, respectively. Similarly, fatal crash rates per million population among bicyclists and pedestrians decreased in all age groups but were highest in the elderly age group in all years; the annual fatal crash rate for elderly pedestrians was 3 to 10 times higher than that for younger pedestrians. Conclusions: Despite the overall decrease in the elderly crash and fatal crash rates in all road use categories, elderly pedestrians are more susceptible to road traffic crashes and are more likely to be killed than younger persons. Further research may reduce this risk

Projecting future supply and demand for physical therapists in Japan using system dynamics

Objectives: Japan is the oldest country in the world, and its demand for medical care is expected to increase. Although a clear vision regarding the supply and demand for physical therapy services is necessary, there has been no research that forecasts the supply and demand for physical therapists in Japan. Consensus has not been reached on whether the supply of physical therapists is sufficient. This study projects this supply and demand to provide medical policymakers with basic data. Methods: A system dynamics model was created to predict the number of physical therapists working in hospitals and clinics in Japan from 2014 to 2040. The future demand for physical therapy was estimated using the rehabilitation service utilization data from Open National Database, a publicly available nationwide health claims database. Sufficiency rates (supply/demand) were calculated, and sensitivity analysis was conducted on supply-related parameters. Results: The number of physical therapists was projected to be 1.74 and 2.54 times greater in 2025 and 2040, respectively, than in 2014. The sufficiency rates were 1.72, 2.39, and 3.30 in 2015, 2025, and 2040, respectively. The sensitivity analysis revealed that attrition rates had the greatest effects on sufficiency. Conclusions: Although the current supply appears to be needed, considering the expected increase and uncertainty in medical needs. However, there is a possibility of a future oversupply, especially after 2025, when the rate of increase in demand will lessen. Further studies are required to evaluate the distribution of physical therapists among regions and specialties. (C) 2019 Fellowship of Postgraduate Medicine. Published by Elsevier Ltd. All rights reserved

Acceptance of the Use of Artificial Intelligence in Medicine Among Japan's Doctors and the Public : A Questionnaire Survey

Background: The use of artificial intelligence (AI) in the medical industry promises many benefits, so AI has been introduced to medical practice primarily in developed countries. In Japan, the government is preparing for the rollout of AI in the medical industry. This rollout depends on doctors and the public accepting the technology. Therefore it is necessary to consider acceptance among doctors and among the public. However, little is known about the acceptance of AI in medicine in Japan. Objective: This study aimed to obtain detailed data on the acceptance of AI in medicine by comparing the acceptance among Japanese doctors with that among the Japanese public. Methods: We conducted an online survey, and the responses of doctors and members of the public were compared. AI in medicine was defined as the use of AI to determine diagnosis and treatment without requiring a doctor. A questionnaire was prepared referred to as the unified theory of acceptance and use of technology, a model of behavior toward new technologies. It comprises 20 items, and each item was rated on a five-point scale. Using this questionnaire, we conducted an online survey in 2018 among 399 doctors and 600 members of the public. The sample-wide responses were analyzed, and then the responses of the doctors were compared with those of the public using t tests. Results: Regarding the sample-wide responses (N=999), 653 (65.4%) of the respondents believed, in the future, AI in medicine would be necessary, whereas only 447 (44.7%) expressed an intention to use AI-driven medicine. Additionally, 730 (73.1%) believed that regulatory legislation was necessary, and 734 (73.5%) were concerned about where accountability lies. Regarding the comparison between doctors and the public, doctors (mean 3.43, SD 1.00) were more likely than members of the public (mean 3.23, SD 0.92) to express intention to use AI-driven medicine (P<.001), suggesting that optimism about AI in medicine is greater among doctors compared to the public. Conclusions: Many of the respondents were optimistic about the role of AI in medicine. However, when asked whether they would like to use AI-driven medicine, they tended to give a negative response. This trend suggests that concerns about the lack of regulation and about accountability hindered acceptance. Additionally, the results revealed that doctors were more enthusiastic than members of the public regarding AI-driven medicine. For the successful implementation of AI in medicine, it would be necessary to inform the public and doctors about the relevant laws and to take measures to remove their concerns about them

Projecting supply and demand for pharmacists in pharmacies based on the number of prescriptions and system dynamics modeling

Background Pharmacists play an important role in promoting people's health in Japan, which has an aging population. Hence, it is necessary that the distribution of pharmacists meets the population's needs in each region. This study projects the future supply and demand for pharmacists in pharmacies to consider an optimal distribution of pharmacists. Methods The future supply of pharmacists working in pharmacies in Hokkaido is projected using system dynamics modeling, according to their career path. The demand is projected based on the number of prescriptions, sourced from publicly available sources. The analysis period is 2015-2040. The estimated demand is converted into the number of pharmacists and the sufficiency is evaluated using sufficiency ratio (supply/demand ratio). Sensitivity analyses of the sufficiency ratio were conducted to estimate the effects of changes in parameters such as national exam pass rate, enrollments, attrition rates, the number of prescriptions per pharmacist, and diffusion of newly licensed pharmacists. Results The projected supply, in 2025 and 2040, is 1.24 and 1.56 times, respectively, as that in 2015 and the demand is 1.11 and 0.98 times, respectively. In 2015, although the sufficiency ratio in Hokkaido overall is 1.19, the ratios are higher in urban medical areas and lower than 1 in rural medical areas, such as Minamihiyama, Emmon, and Nemuro. By 2040, the sufficiency ratios are greater than 1 for all areas except for Emmon and higher than 2 in some areas. The sensitivity analyses found that the sufficiency ratio was most sensitive to diffusion of newly licensed pharmacists and the number of prescriptions per pharmacist. Conclusion Optimal distribution should be considered, as the results reveal a possible shortage in the number of pharmacists in rural medical areas in 2015-2025. Conversely, as the demand is projected to decrease after 2025 with a population decrease, future supply should be determined in order not to cause an oversupply after 2025. Refinements of the projection model should be conducted since the related factors such as the roles of pharmacists will change over time

Projecting supply and demand for pharmacists in pharmacies based on the number of prescriptions and system dynamics modeling

Background Pharmacists play an important role in promoting people's health in Japan, which has an aging population. Hence, it is necessary that the distribution of pharmacists meets the population's needs in each region. This study projects the future supply and demand for pharmacists in pharmacies to consider an optimal distribution of pharmacists. Methods The future supply of pharmacists working in pharmacies in Hokkaido is projected using system dynamics modeling, according to their career path. The demand is projected based on the number of prescriptions, sourced from publicly available sources. The analysis period is 2015-2040. The estimated demand is converted into the number of pharmacists and the sufficiency is evaluated using sufficiency ratio (supply/demand ratio). Sensitivity analyses of the sufficiency ratio were conducted to estimate the effects of changes in parameters such as national exam pass rate, enrollments, attrition rates, the number of prescriptions per pharmacist, and diffusion of newly licensed pharmacists. Results The projected supply, in 2025 and 2040, is 1.24 and 1.56 times, respectively, as that in 2015 and the demand is 1.11 and 0.98 times, respectively. In 2015, although the sufficiency ratio in Hokkaido overall is 1.19, the ratios are higher in urban medical areas and lower than 1 in rural medical areas, such as Minamihiyama, Emmon, and Nemuro. By 2040, the sufficiency ratios are greater than 1 for all areas except for Emmon and higher than 2 in some areas. The sensitivity analyses found that the sufficiency ratio was most sensitive to diffusion of newly licensed pharmacists and the number of prescriptions per pharmacist. Conclusion Optimal distribution should be considered, as the results reveal a possible shortage in the number of pharmacists in rural medical areas in 2015-2025. Conversely, as the demand is projected to decrease after 2025 with a population decrease, future supply should be determined in order not to cause an oversupply after 2025. Refinements of the projection model should be conducted since the related factors such as the roles of pharmacists will change over time

A Bayesian Network-Based Browsing Model for Patients Seeking Radiology-Related Information on Hospital Websites : Development and Usability Study

Background: An increasing number of people are visiting hospital websites to seek better services and treatments compared to the past. It is therefore important for hospitals to develop websites to meet the needs of their patients. However, few studies have investigated whether and how the current hospital websites meet the patient's needs. Above all, in radiation departments, it may be difficult for patients to obtain the desired information regarding modality and diagnosis because such information is subdivided when described on a website. Objective: The purpose of this study is to suggest a hospital website search behavior model by analyzing the browsing behavior model using a Bayesian network from the perspective of one-to-one marketing. Methods: First, we followed the website access log of Hokkaido University Hospital, which was collected from September 1, 2016, to August 31, 2017, and analyzed the access log using Google Analytics. Second, we specified the access records related to radiology from visitor browsing pages and keywords. Third, using these resources, we structured 3 Bayesian network models based on specific patient needs: radiotherapy, nuclear medicine examination, and radiological diagnosis. Analyzing each model, this study considered why some visitors could not reach their desired page and improvements to meet the needs of visitors seeking radiology-related information. Results: The radiotherapy model showed that 74% (67/90) of the target visitors could reach their requested page, but only 2% (2/90) could reach the Center page where inspection information, one of their requested pages, is posted. By analyzing the behavior of the visitors, we clarified that connecting with the radiotherapy and radiological diagnosis pages is useful for increasing the proportion of patients reaching their requested page. Conclusions: We proposed solutions for patient web-browsing accessibility based on a Bayesian network. Further analysis is necessary to verify the accuracy of the proposed model in comparison to other models. It is expected that information provided on hospital websites will be improved using this method