Yonsei Med J.  2022 Jan;63(1):8-15. 10.3349/ymj.2022.63.1.8.

Physician Knowledge Base: Clinical Decision Support Systems

Affiliations
  • 1Center of Smart Healthcare, Pyeonghwa IS, Seoul, Korea.
  • 2Department of Artificial Intelligence and Software Technology, Sun Moon University, Asan, Korea.
  • 3Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • 4Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.

Abstract

With the introduction of electronic medical records (EMRs), it has become possible to accumulate massive amounts of qualitative medical data. As such, EMRs have become increasingly used in clinical decision support systems (CDSSs). While CDSSs aim to reduce medical errors normally occurring in the process of treating patients by physicians, technical maturity and the completeness of CDSSs do not meet standards for medical use yet. As data further accumulates, CDSS algorithms must be continuously updated to allow CDSSs to perform their core functions. Doing so, however, requires extensive time and manpower investments. In current practice, computational systems already perform a wide variety of functions in medical settings to allow medical staff to focus on other tasks. However, no prior research has evaluated the potential effectiveness of future CDSSs nor analyzed possibilities for their further development. In this article, we evaluate CDSS technology with the consideration that medical staff also understand the core functions of such systems.

Keyword

Artificial intelligence; decision support systems; clinical; deep learning
Full Text Links
  • YMJ
Actions
Cited
CITED
export Copy
Close
Share
  • Twitter
  • Facebook
Similar articles
Copyright © 2024 by Korean Association of Medical Journal Editors. All rights reserved.     E-mail: koreamed@kamje.or.kr