ObjectivesThis study presents the current status of nursing informatics education, the content covered in nursing informatics courses, the faculty efficacy, and the barriers to and additional supports for teaching nursing informatics in Korea.MethodsA set of questionnaires consisting of an 18-item questionnaire for nursing informatics education, a 6-item questionnaire for faculty efficacy, and 2 open-ended questions for barriers and additional supports were sent to 204 nursing schools via email and the postal service. Nursing schools offering nursing informatics were further asked to send their syllabuses. The subjects taught were analyzed using nursing informatics competency categories and other responses were tailed using descriptive statistics.ResultsA total of 72 schools (35.3%) responded to the survey, of which 38 reported that they offered nursing informatics courses in their undergraduate nursing programs. Nursing informatics courses at 11 schools were taught by a professor with a degree majoring in nursing informatics. Computer technology was the most frequently taught subject (27 schools), followed by information systems used for practice (25 schools). The faculty efficacy was 3.76 ± 0.86 (out of 5). The most frequently reported barrier to teaching nursing informatics (n = 9) was lack of awareness of the importance of nursing informatics. Training and educational opportunities was the most requested additional support.ConclusionsNursing informatics education has increased during the last decade in Korea. However, the proportions of faculty with degrees in nursing informatics and number of schools offering nursing informatics courses have not increased much. Thus, a greater focus is needed on training faculty and developing the courses.

Objectives This study analyzed the health technology trends and sentiments of users using Twitter data in an attempt to examine the public's opinions and identify their needs. Methods Twitter data related to health technology, from January 2010 to October 2016, were collected. An ontology related to health technology was developed. Frequently occurring keywords were analyzed and visualized with the word cloud technique. The keywords were then reclassified and analyzed using the developed ontology and sentiment dictionary. Python and the R program were used for crawling, natural language processing, and sentiment analysis. Results In the developed ontology, the keywords are divided into ‘health technology‘ and ‘health information‘. Under health technology, there are are six subcategories, namely, health technology, wearable technology, biotechnology, mobile health, medical technology, and telemedicine. Under health information, there are four subcategories, namely, health information, privacy, clinical informatics, and consumer health informatics. The number of tweets about health technology has consistently increased since 2010; the number of posts in 2014 was double that in 2010, which was about 150 thousand posts. Posts about mHealth accounted for the majority, and the dominant words were ‘care‘, ‘new‘, ‘mental‘, and ‘fitness‘. Sentiment analysis by subcategory showed that most of the posts in nearly all subcategories had a positive tone with a positive score. Conclusions Interests in mHealth have risen recently, and consequently, posts about mHealth were the most frequent. Examining social media users' responses to new health technology can be a useful method to understand the trends in rapidly evolving fields.

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BACKGROUND The decrease in children’s vaccination rates resulted in outbreaks of vaccine-preventable diseases. Child vaccine hesitancy can occur due to the ongoing COVID-19 pandemic as we are going through a time of uncertainty. OBJECTIVE This study aimed to assess the efficacy of the “Child Vaccination Friend Chatbot” based on changes in the measurements of vaccination information, motivation, self-efficacy, and vaccination behavioral intention variables. METHODS From 30th Jan. 2020 to 15th Feb. 2020, a total of 65 people participated in the trial through online recruitment. The participants of this study were parents who were raising children from 0 to 35 months old and those whose children expected to be vaccinated within three months. Participants were randomly assigned to the experimental group (N=34) and the control group (N=31). During the 12 weeks of the research, the participants were provided with vaccination schedule reminder alarms, a real-time consultation messenger service, information about vaccinations, and motivation boosters. Also, vaccination information, motivation, self-efficacy, and vaccination behavioral intention variables were measured every four weeks. RESULTS The experimental group that used the chatbot scored higher for vaccination information, motivation, self-efficacy, and vaccination behavioral intention than the control group. Based on the results, it is assumed that the child vaccination chatbot provided useful and prompt information to parents raising children who needed to be vaccinated. The child vaccination chatbot increased vaccination motivation, self-efficacy, and vaccination rates by providing necessary information to parents. CONCLUSIONS This study provides evidence that chatbot can be used as a tool to promote immunization through the provision of reminders and real-time consultation messenger service during the global crisis.