In Japan, the rapidly aging population has led to a demand for seamless medical and long-term care services to elderly. Given this situation, the Ministry of Health, Labor and Welfare aims to promote an Integrated Community Care System that can appropriately provide various services. This system requires the cooperation of all relevant organizations in the community, such as hospitals, nursing facilities, and pharmacies. Although each community is working toward strengthening cooperation, effective ways to promote concrete initiatives are still unclear. The increasing interest in the Integrated Community Care System has led to pioneering cases, but they are difficult to replicate in communities handicapped by insufficient resources. Therefore, each community need to promote initiatives based on regional characteristics.
The purpose of this study is to propose a method to promote such initiatives effectively. First, we list the activities required to establish the Integrated Community Care System and construct a model to put it in place. We then assess the present level of cooperation in a community (which we identify as Community A) and estimate its resources. We then develop a roadmap for the establishment of the System. Furthermore, we identify some effective initiatives and try to implement them in Community A.
A control chart is one of the representative methodologiesused in statistical process control. A multivariate control chart is used todetect changes in multiple variables. Ueno and Nagata (2018) proposed amultivariate exponentially weighted moving likelihood (MEWML) control chartaimed at detecting minor changes in the mean vector and variance-covariancematrix. Although the MEWML control chart can accurately detect the change whenthe variance increases, it is unable to detect the change when it decreases. Areduction in variance implies that the procedure is moving to a better state.Therefore, detecting the change can lead to an improvement in the procedure. Inthis paper, we detect reductions in variance by considering both the upper andlower control limits. Through a simulation, we demonstrate that reduction invariance can be detected by using the lower control limit.
An opportunistic maintenance policy based on a semi-Markov decisionprocess is presented for the critical component in a multicomponent system.Opportunistic maintenance is maintenance planning for the target (i.e. critical)component in which replacement is determined on the basis of maintenanceopportunities provided by other components. The transition of the deteriorationstate of the target component follows a semi-Markov process while the occurrenceof maintenance opportunities follows a Poisson process. Sensorsare used to continuously monitor the target component to enable immediatedetection of a state transition, but since the underlying state cannot be knownexactly, the decision maker obtains information on the deterioration stateprobabilistically from sensor results. The focus here is on one critical component in asystem, and the decision-making problem isformulated as a partially observable semi-Markov decision process. The cost structureis composed of operating cost and two types of replacement costs. Theproperties of an optimal policy minimizing the expected total discounted costover an infinite horizon are analytically investigated. Sufficient conditionsare derived under which the objective function for policy optimization isnondecreasing in each deterioration state and the optimal policy is determinedby a threshold. A property indicating the presence or absence of a maintenanceopportunity is presented that is based on the relationship between thresholdsfor optimal actions in a deterioration state.