Use of and Satisfaction With Mobile Health Education During the COVID-19 Pandemic in Thailand: Cross-sectional Study

Abstract

Background RamaCovid is a mobile health (mHealth) education system that provides the Thai population with information about COVID-19 and self-risk assessment. RamaCovid has a chatbot system that provides automatic conversations (available 24 hours per day) and a live chat function that allows users to directly communicate with health professionals (available 4 hours per day in the evening). The system consists of (1) COVID-19 vaccine information, (2) self-care after vaccination, (3) frequently asked questions, (4) self-risk assessment, (5) hospital finding, (6) contact number finding, and (7) live chat with a health professional. Objective This study investigates the use of and satisfaction with the RamaCovid system. Methods Overall, 400 people were recruited via RamaCovid by broadcasting an infographic about the study. Questionnaires collected demographic data, users’ experiences of RamaCovid, and the use of and satisfaction with the system. The questions were answered using a 5-point Likert scale. Descriptive statistics were used to describe the participant characteristics and their use of and satisfaction with the RamaCovid system. The Mann-Whitney U test was performed to examine the difference in use and satisfaction between the adult and older adult groups. Results The participants showed high use of and satisfaction with the RamaCovid system. They used the information to take care of themselves and their family, and they gained information about their COVID-19 risk. The users were satisfied with the system because the information was easy to understand, trustworthy, and up to date. However, the older adult group had lower use of and satisfaction with the system compared to the adult group. Conclusions RamaCovid is an example of the successful implementation of mHealth education. It was an alternative way to work with the call center during the COVID-19 pandemic and increased access to health information and health care services. Providing ongoing updated information, improving the attractiveness of the media information, and the age group difference are important issues for further system development.

Performance of emergency triage prediction of an open access natural language processing based chatbot application (ChatGPT): A preliminary, scenario-based cross-sectional study

“…[ 1 ] Chatbots have been used in many fields including education, mental healthcare, maternity, chronic diseases by populations for screening in the COVID-19 pandemic. [ 6 23 24 25 ] The present study is the first use of ChatGPT for predicting triage categories in the literature, to the best of our knowledge. The overall performance of ChatGPT in five-level ESI triage categories was poor, while it showed better results in distinguishing high acuity cases.…”

“…[ 22 ] With this dynamic model trained from big data on the Internet, it has become an application that can perform actions such as providing information, answering questions, and creating content with high accuracy. [ 23 ] In this study, we aimed to measure the performance of ChatGPT on triage prediction in an emergency medicine (EM) scenario.…”

Mobile Tourism Recommender System for Users to Get a Better Choice of Tour

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