Medication non-compliance" is a failure to take medication properly. Therefore, medication is necessary for patients to be able to understand medication properly and to participate in treatment voluntarily with the right motivation. For this study, we design medication education based on the ARCS model(Attention, Relevance, Confidence, Satisfaction), which classifies concepts related to learning motivation (Keller, 1984), and which incorporates utilization of the communication robot "Pepper".

The purposes of this study were to obtain descriptive information about the job duties and tasks of Japanese occupational health nurses and to compare the roles and functions of occupational health nurses in the United States and Japan. A modified version of a job analysis survey developed by the American Board for Occupational Health Nurses was used to collect data. The findings indicated 62% of Japanese occupational health nurses perform direct care roles, approximately half perform educating or advising and consulting roles, and approximately 40% perform management roles. Details related to specific tasks and differences in nurses with varying preparation also are presented.

This paper considers and suggests efficient patrol of nurses from analysis result of nurse calls log. As the method of analysis, we consider applying Bayesian network. This is one of probability model that is available for prospects of a phenomenon, rational mind decision and so on. In conventional studies, correlation coefficient was used to examine relation between phenomena. However, we could analyze in detail by using Bayesian network because we didn't overlook the information that may overlook by using correlation coefficient. Applying the proposed method to total 5,478 nurse call data, we have obtained following knowledge: (a) some reasons of nurse call are inferred by situation of patient and demand level of nurse call, (b) an efficient patrol of nurse can be devised, and (c) Bayesian network didn't overlook the information compared with conventional method.We believe that nurse calls can play a role not merely as a means to convey patients' needs, but also as a medium to analyze and predict patients' needs using nurse calls log. In conventional study, the factor that influences the patient satisfaction often includes "Speed of correspondence to the nurse call" [1, 2]. In order to improve patient's satisfaction, the medic should adequately catch the desire of the patient. The real of reasons for calling was demonstrated.This paper considers and suggests efficient patrol of nurses from analysis result of nurse calls log. As the method of analysis, we consider applying Bayesian network (BN) [9,10]. This is one of probability model that is available for prospects of a phenomenon, rational mind decision and so on. In conventional studies, correlation coefficient was used to examine relation between phenomena. However, we could analyze in detail by using BN because we didn't overlook the information that may overlook by using correlation coefficient.In the following, overview of BN is introduced first. Then we will describe the feature of proposed BN modeling. Showing the numerical study, we will summarize what we did and potential future works. 978-1-4244-8314-3/10/$26.00 ©201O IEEE 272 II. BAYESIAN NETWORK A. Overview of Bayesian Network (BN)BN is a graphical modeling method where the quantitative dependence between nodes expressed as a random variable was shown at the conditional probability [4,5,7]. The conditional probability is calculated by the event (parameter) that happens at the node. Each node is connected with the direct link, and the link from node X to nodes Y indicates that Y has received the direct influence from X. In a word, it means there is a causal relation in the direction of the link. Figure I shows an example of BN which is consisted of three nodes "Gender", "Time Zone", and "Reason of Nurse Call". Each node has some parameters. Once a set of nodes have their own probability (it is called evidence), the conditional probabilities are calculated. It should be something like the posterior probability. To compare the prior probability with the posterior probability, called as "probabilistic ...

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