【Objective】 We have some databases and sources about disease names as follows. There are ICD-10, the national registry of designated intractable diseases, and other names associated with them (non-designated intractable diseases). In addition, there are “disease name masters”, and “MEDIS standardized disease masters (MEDIS masters)”. Noda et al. (2019) reported that some disease names may differ depending on the databases. Therefore, we investigated them focusing on the intractable diseases. 【Methods】 We obtained the latest (as of January 2021) list of the intractable disease names, and non-designated intractable disease names. The MEDIS masters were obtained from the website of the “Standard Disease-Code Master working group”, then we made a table which describes the intractable disease name and disease name masters and analyzed the differences between the sources and databases. 【Results】 In January 2021, there were 333 designated intractable disease names, and 1,259 non-designated intractable diseases. After our previous study, 3 non-designated intractable diseases names were added. Whereas, some designated intractable diseases and non-designated intractable diseases names were not registered in either master. Moreover, some designated intractable diseases and non-designated intractable diseases were either totally different or at least one character different in each master. 【Conclusion】 This study revealed that the change of the disease names and masters due to be added or deleted them. Also, we found that they continue to maintenance masters. On the other hand, some disease names were still not organized, therefore these should be standardized for the accurate insured medical treatment.

　The purpose of this study is to clarify how social media opinion on fertility treatment has changed with the change of the subsidy system. We analyzed using tweets containing the words “infertility” “artificial insemination” and “in vitro fertilization”. We analyzed tweets posted on Twitter from 2010 to 2012 before the grant system change and from 2016 to 2018 after the change. We performed morphological analysis using the target tweet, calculated the number of appearances for each word, created a co-occurrence network, and calculated the emotion polarity value. A tweet analysis that included “infertility” or “in vitro fertilization” revealed words about cost in the co-occurrence network from 2016 to 2018 after the change in the subsidy system, and positive changes in emotional polarity values were seen. It was suggested that public opinion on social media due to the change in the subsidy system may have changed more positively than before the change.

　Informatics is becoming increasingly important for introducing ICT into the medical and welfare fields. Therefore, the purpose of this study was to clarify the status of informatics education at nursing educational institutions. Three hundred and nine nursing educational institutions were assessed, and those with the largest number of participants were nursing colleges and higher professional nursing schools (241, 78.0％), with informatics lectures provided at 297(96.1％). Basic knowledge of personal computers (272, 91.6％) was the most highly covered topic related to informatics. Objectives in medical/nursing informatics were divided into three categories: the content of lectures, the medical and/or nursing informatics curriculum, and the challenges of medical/nursing informatics education. Most nursing educational institutions provided lectures on informatics, but the importance of medical/nursing informatics specific to nursing is considered to increase in the future.

　The aim of the study was to determine the explanatory rate of the variance in “patient-nurse contact time in the ward” by the quantified nursing index “nursing need”. The subjects were 40 patients in hospital. Contact time was measured over six days using ICT equipment, and the correlation between level of nursing needs and contact time on each of the days analyzed. Analyses were conducted by testing for differences in contact time for each nursing need item and by regression analysis with contact time as the objective variable and the nursing need item as the explanatory variable. As a result, significant differences in contact time were found in eight items on the level of nursing needs list. Ultimately, the multiple regression analysis yielded a regression model that comprised five of these items and a coefficient of determination of 0.207. The results confirmed that the level of nursing need was a measure significantly related to contact time. However, the regression model created from these items explained only 20％ of the variance suggesting that it would be difficult to predict contact time based on the level of nursing needs alone.