Physician Knowledge Base: Clinical Decision Support Systems

Sira Kim, Eung Hee Kim, Hun Sung Kim

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

With the introduction of electronic medical records (EMRs), it has become possible to accumulate massive amounts of qualita-tive 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 com-pleteness of CDSSs do not meet standards for medical use yet. As data further accumulates, CDSS algorithms must be continu-ously updated to allow CDSSs to perform their core functions. Doing so, however, requires extensive time and manpower invest-ments. 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 an-alyzed 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.

Original languageEnglish
Pages (from-to)8-15
Number of pages8
JournalYonsei Medical Journal
Volume63
Issue number1
DOIs
StatePublished - Jan 2022

Bibliographical note

Funding Information:
This work was supported by the Technology Development Program (S2726209) funded by the Ministry of SMEs and Startups (MSS, Korea).

Publisher Copyright:
© Yonsei University College of Medicine 2022.

Keywords

  • Artificial intelligence
  • clinical
  • decision support systems
  • deep learning

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