A Study on the Consumer Perception of Digital Healthcare using Unstructured Big Data: Focusing on Keyword Changes During and After COVID-19

Abstract

This study collected various keywords by using TextStorm, a solution program that can effectively perform big data analysis using unstructured data to confirm consumer perception of digital healthcare during and after COVID-19, by utilizing cafes and blogs, news media within portal sites such as Naver, Daum, and Google. Based on these analysis results, the academic implications proposed are as follows. First, it is the application of medical innovation and artificial intelligence in the medical field. Second, it is the i ssue o f data security, social issues, and ethics. On the other hand, the practical implications for deepening social science research related to digital healthcare are as follows. First, education for medical institutions and experts should be strengthened and accessibility should be improved. Second, attention should be paid to ethical use and cost-effectiveness improvement of artificial intelligence technology. Based on these implications, we aim to contribute to the development of the digital healthcare field.