본문 바로가기
  • Home

A Study on The ESG Medical Management Using Adaptive AI and Improvement of Inequality for Nursing Workers

  • Legal Theory & Practice Review
  • Abbr : LTPR
  • 2023, 11(3), pp.139-162
  • Publisher : The Korea Society for Legal Theory and Practice Inc.
  • Research Area : Social Science > Law
  • Received : July 20, 2023
  • Accepted : August 25, 2023
  • Published : August 31, 2023

정재진 1 LEE DA JUNG 2 Hwang Jung Hoon 3

1비즈니움
2금오공과대학교
3호서대학교 법학연구소

Accredited

ABSTRACT

Due to the introduction and expansion of the long-term care insurance system, nursing services are expected to expand not only in medical institutions but also in homes and facilities in the community, and the scope of nurses' roles is likely to expand more widely than in the past. Nursing services in the community play a role in improving the quality of life by providing nursing and support services in daily life to vulnerable groups such as the elderly and the disabled. Unlike nursing services provided by medical institutions, nursing services in the community are characterized by providing nursing services for various problems arising from group life and daily life. ESG management and the establishment of a sustainable value chain emphasize the social responsibility of medical institutions and help them pursue sustainable development. Medical institutions can have a positive impact in various areas such as environmental protection, social contribution, and health promotion. In the medical field, it is a very important task to review strategies for adaptive AI security guidelines and medical management ESG diagnostic models. It is possible to consider activating SIB bonds as a financial support model for improving nursing inequality through the establishment of an inequality improvement system using adaptive AI. Further research and specific applications in the field will enable the development of medical management ESG diagnostic models and improvement systems in the medical field through adaptive AI.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.