Barriers to Telemedicine Adoption during the COVID-19 Pandemic in Taiwan: Comparison of Perceived Risks by Socioeconomic Status Correlates.

Telemedicine is the use of technology to deliver healthcare services from a distance. In some countries, telemedicine became popular during the COVID-19 pandemic. Its increasing popularity provides new research opportunities to unveil users' perceptions toward its adoption and continued use. Existing studies have provided limited information and understanding of Taiwanese users and the various sociodemographic factors that influence their intention to use telemedicine services. Thus, the goals of this study were twofold: identifying the dimensions of perceived risks of telemedicine services in Taiwan and providing specific responses to those perceptions as well as determining strategies to promote telemedicine to local policymakers and influencers by providing a better understanding of the perceived risks in relation to socioeconomic status. We collected 1000 valid responses using an online survey and found performance risk to be the main barrier, which was followed by psychological, physical, and technology risks. Older adults with lower levels of education are less likely to use telemedicine services compared to other categories because of multiple perceived risks, including social and psychological concerns. Understanding the differences in perceived risks of telemedicine services by socioeconomic status may aid in identifying the actions required to overcome barriers and may consequently improve adoption of the technology and user satisfaction.

A Narrative Review of Innovative Responses During the COVID-19 Pandemic in 2020.

Objectives: The coronavirus disease 2019 (COVID-19) pandemic presented unprecedented challenges to healthcare systems worldwide. While existing studies on innovation have typically focused on technology, health providers still only have a vague understanding of the features of emergency responses during resource exhaustion in the early stage of a pandemic. Thus, a better understanding of innovative responses by healthcare systems during a crisis is urgently needed. Methods: Using content analysis, this narrative review examined articles on innovative responses during the COVID-19 pandemic that were published in 2020. Results: A total of 613 statements about innovative responses were identified from 296 articles and were grouped under the following thematic categories: medical care (n = 273), workforce education (n = 144), COVID-19 surveillance (n = 84), medical equipment (n = 59), prediction and management (n = 34), and governance (n = 19). From the four types of innovative responses extracted, technological innovation was identified as the major type of innovation during the COVID-19 pandemic, followed by process innovations, frugal innovation, and repurposing. Conclusion: Our review provides insights into the features, types, and evolution of innovative responses during the COVID-19 pandemic. This review can help health providers and society show better and quicker responses in resource-constrained conditions in future pandemics.

A Robust Design-Based Expert System for Feature Selection and COVID-19 Pandemic Prediction in Japan.

Expert systems are frequently used to make predictions in various areas. However, the practical robustness of expert systems is not as good as expected, mainly due to the fact that finding an ideal system configuration from a specific dataset is a challenging task. Therefore, how to optimize an expert system has become an important issue of research. In this paper, a new method called the robust design-based expert system is proposed to bridge this gap. The technical process of this system consists of data initialization, configuration generation, a genetic algorithm (GA) framework for feature selection, and a robust mechanism that helps the system find a configuration with the highest robustness. The system will finally obtain a set of features, which can be used to predict a pandemic based on given data. The robust mechanism can increase the efficiency of the system. The configuration for training is optimized by means of a genetic algorithm (GA) and the Taguchi method. The effectiveness of the proposed system in predicting epidemic trends is examined using a real COVID-19 dataset from Japan. For this dataset, the average prediction accuracy was 60%. Additionally, 10 representative features were also selected, resulting in a selection rate of 67% with a reduction rate of 33%. The critical features for predicting the epidemic trend of COVID-19 were also obtained, including new confirmed cases, ICU patients, people vaccinated, population, population density, hospital beds per thousand, middle age, aged 70 or older, and GDP per capital. The main contribution of this paper is two-fold: Firstly, this paper has bridged the gap between the pandemic research and expert systems with robust predictive performance. Secondly, this paper proposes a feature selection method for extracting representative variables and predicting the epidemic trend of a pandemic disease. The prediction results indicate that the system is valuable to healthcare authorities and can help governments get hold of the epidemic trend and strategize their use of healthcare resources.