We investigated factors related to the overall assessment of inpatient satisfaction based on the US Patient Assessment Index (HCAHPS). Respondents were the 4,787 patients discharged from 2014 to 2016, who answered all the questions and whose comprehensive evaluation of satisfaction was high (9, 10). As a result, (1) From the factor analysis based on the 22 questions, communication with doctors and nurses, examination, explanation on treatment, pain control, explanation on medicine, and six potentials of understanding of care after discharge the factor was extracted. These were similar to the integrated index of HCAHPS. (2) Non-hierarchical cluster analysis showed that the group with high overall score and high group with low average score exist in the group with the same overall evaluation. (3) By the binary logistic regression analysis, it is revealed that the high evaluation group factors that divide the two groups are related to the day of discharge, room movement, number of participating nurses, MRI examination, and medical department at discharge.

The use of cloud computing system, which is the basic technology supporting ICT, is expanding. However, when each sever is connected with much terminals, the server shows low capability compared to being connected with small number of terminals. The limit of capacity of servers for cloud system leads to significant processing time delay. To avoid it, edge computing system for IoT has been proposed. In the edge system, multiple servers called edges are connected directly or close distance between the server and the terminal (or thing). In the previous paper, we proposed the effective BP learning method for the edge system. In this paper, we will propose clustering methods for the edge system and show the effectiveness of them in some simulations.

A study involving 394 nursing students was conducted, with the aim of assessing “pictures taken during the same disaster,” looking at such pictures at “the same time” and at the “same location,” and if the students were to communicate, clarifying “differences between the sender and receiver in the transmission of information and the reason why information transmission is difficult” and “the kind of information a person wants to convey.” A self-administered questionnaire was used to obtain responses using a 0%–100% scale. We found that the students often gathered information in terms of what they knew and conveyed this information to others on the basis of their imaginations and assumptions. In addition, the amount of information that was assumed by the sender (mean ± SE: 75.4% ± 1.32%) was greater than that transmitted to the receiver by the sender (p < 0.0001). Furthermore, information was collected using various terms to indicate the content of one piece of information that was then expressed to the other party. The terms used by the recipient were “unfamiliar with” or “I do not know.” The transmission of information may possibly lead to the receiver's own interpretation and assumption. The degree of information transmission is influenced by an individual’s vocabulary. With regard to information regarding life and health, proper organization and transmission of information using communication devices are important..

Research reports have indicated that information transmission in large-scale disasters is counterproductive because of the following reasons: people are mostly out of sight; “information is often transmitted using communication devices;” “higher the magnitude of the disaster, more confusing the information becomes;” and “the assumption of people.” In this study, we compared two groups of nursing students (n = 394) by showing them photographs of dogs and cats to determine the type of information that should be collected and how this information should be conveyed to others in a nonverbal format when communication devices are unavailable.As a result of the free description by the sender, the following results were obtained: “collecting information and conveying it in detail are better;” “common image recognition requires knowledge and mutual verification;” and “even detailed information is difficult to convey unless the terms are mutually recognized.” No significant difference was noted between the two groups regarding whether the information provided by the sender was what the receiver had assumed. The sender who saw the picture of a cat was able to more efficiently convey the picture than the sender who saw the picture of a dog (p = 0.005).The information collected by the sender may be difficult to convey the recipient, unless the sender and receiver have common knowledge, images, and expressions.

Nursing is said to be an information-dependent profession. In order to provide good quality nursing, it is necessary to acquire the ability to learn to operate ICT equipment, identify appropriate information, and use that information. However, nurses’ relative lack of information education and their fear, anxiety, and other negative emotional reactions to computers arising from their lack of computer literacy are likely to lead to a lack of interest in computers.

The purpose of this study was to investigate the relationship between ICT skills, computer anxiety, and clinical nursing competency among first-year nursing staff at a university hospital. It was found that the higher the ICT skills, the lower the computer anxiety and the higher the clinical nursing competency (p <.001). It was suggested that information education including ICT skills is effective in reducing computer anxiety and may lead to improvement in clinical nursing competency.

Recently, AI technology called deep learning has been attracting attention in the field of image recognition. Image recognition is a process of estimating a corresponding image pattern from image information. Deep learning is a kind of neural network. It is a method of machine learning using a hierarchical neural network with a multi-layer structure. Deep learning has shown great results in pattern recognition. In this study, we consider the classification of unearthed coins using deep learning. There are many types of unearthed coins to be excavated from a ruin. However, the classification of unearthed coins is even difficult task for archaeological experts. The objective of this research is to conduct studies into the classification of unearthed coins by deep learning. If excavated coins can be classified by computer image recognition, it will be very useful in numismatics.