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Related Concept Videos

Nursing Clinical Information System01:27

Nursing Clinical Information System

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Nursing Clinical Information System (NCIS)
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include:
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Documentation of Nursing Diagnosis01:10

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The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
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The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Optimizing Nursing Data for Al and CDSS: SNOMED CT Mapping Using K-MIMIC.

Youngeun Kim1, Mijeong Park1, Sang-Min Lee2

  • 1WITH LAB, Gangneung-Wonju National University, Wonju, Gangwon state, Korea.

Studies in Health Technology and Informatics
|August 8, 2025
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Summary
This summary is machine-generated.

Mapping Korean intensive care nursing data to SNOMED CT supports AI-driven Clinical Decision Support System development. This standardization is crucial for optimizing data utilization and enhancing clinical decision-making.

Keywords:
Clinical Decision Support SystemIntensive care unitNursing RecordsSNOMED CT

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Area of Science:

  • Medical Informatics
  • Nursing Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Intensive care nursing data requires standardization for advanced applications.
  • International terminologies like SNOMED CT are essential for data interoperability.
  • Developing AI-based Clinical Decision Support Systems (CDSS) necessitates structured clinical data.

Purpose of the Study:

  • To map Korean Medical Information Mart for Intensive Care (K-MIMIC) nursing data to SNOMED CT.
  • To evaluate the suitability of SNOMED CT for representing intensive care nursing data.
  • To assess the potential of standardized data for AI-based CDSS development.

Main Methods:

  • Mapping approximately 12,000 K-MIMIC nursing entries from two hospitals to SNOMED CT.
  • Categorizing mapped data into exact, broad, and unmatched entries.
  • Analyzing mapping types (1:N) and primary data categories (Procedures, Clinical findings).

Main Results:

  • 8,424 nursing data entries were successfully mapped.
  • 90.4% exact matches, 8.5% broad matches, 1.1% unmatched entries.
  • 76.9% of data were 1:N mappings, predominantly Procedures (57.1%) and Clinical findings (38.2%).

Conclusions:

  • SNOMED CT is suitable for representing intensive care nursing data.
  • Standardized nursing data supports AI development and clinical decision-making.
  • Future research should refine mapping processes and validate CDSS effectiveness.