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Related Concept Videos

Quality Assurance01:19

Quality Assurance

Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
Documentation in Long-Term and Home Healthcare Setting01:29

Documentation in Long-Term and Home Healthcare Setting

Documentation in long-term care facilities and home healthcare settings is crucial for ensuring continuous, coordinated, and comprehensive care for patients. Each setting has its specific documentation processes and tools:
Long-Term Care Facilities
Quality Control01:05

Quality Control

Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
Nursing Clinical Information System01:27

Nursing Clinical Information System

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:
Formulating and Validating Nursing Diagnosis II01:25

Formulating and Validating Nursing Diagnosis II

Nursing diagnoses represent a problem validated by major defining characteristics. There are four categories of nursing diagnoses: problem-focused, risk, health promotion or wellness, and syndrome. The anatomy of a nursing diagnosis includes three components: problem statement or diagnostic label, defining characteristics, and related factors.
Risk nursing diagnoses represent clinical judgments of an individual, family, or community more vulnerable to developing the health problem than others...

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Related Experiment Video

Updated: May 15, 2026

Cell-free Biochemical Fluorometric Enzymatic Assay for High-throughput Measurement of Lipid Peroxidation in High Density Lipoprotein
07:29

Cell-free Biochemical Fluorometric Enzymatic Assay for High-throughput Measurement of Lipid Peroxidation in High Density Lipoprotein

Published on: October 12, 2017

Quality assurance in LOINC using Description Logic.

Tomasz Adamusiak1, Olivier Bodenreider

  • 1National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. tadamusiak@mcw.edu

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|January 11, 2013
PubMed
Summary
This summary is machine-generated.

By converting the Logical Observation Identifiers Names and Codes (LOINC) terminology to OWL Description Logic (DL) and integrating it with SNOMED CT, researchers identified logical inconsistencies and enhanced its hierarchical structure.

Related Experiment Videos

Last Updated: May 15, 2026

Cell-free Biochemical Fluorometric Enzymatic Assay for High-throughput Measurement of Lipid Peroxidation in High Density Lipoprotein
07:29

Cell-free Biochemical Fluorometric Enzymatic Assay for High-throughput Measurement of Lipid Peroxidation in High Density Lipoprotein

Published on: October 12, 2017

Area of Science:

  • Medical Informatics
  • Ontology Engineering
  • Computational Linguistics

Background:

  • The Logical Observation Identifiers Names and Codes (LOINC) is a widely used standard for identifying laboratory and clinical observations.
  • LOINC's hierarchical structure is relatively flat, which can limit its expressiveness and utility in complex reasoning tasks.
  • Description Logic (DL) offers a formal framework for representing knowledge and performing logical inference, potentially enhancing terminological structures.

Purpose of the Study:

  • To evaluate the utility of Description Logic (DL) in identifying potential errors and inconsistencies within the Logical Observation Identifiers Names and Codes (LOINC) terminology.
  • To assess the impact of representing LOINC in OWL DL and integrating it with SNOMED CT on its classification and hierarchical structure.
  • To explore the benefits of merging LOINC with SNOMED CT for improved terminological enrichment and connectivity.

Main Methods:

  • LOINC concepts were translated into OWL DL definitions.
  • The LOINC ontology was merged with SNOMED CT using UMLS-derived mappings to enrich the LOINC hierarchy.
  • The combined ontology was classified using the ConDOR reasoner to identify logical relationships and inconsistencies.

Main Results:

  • The transformation to DL identified 427 sets of logically equivalent LOINC codes and 676 sets of logically equivalent LOINC parts.
  • A total of 239 inconsistencies were detected within the LOINC multiaxial hierarchy.
  • The automatic classification of the combined LOINC and SNOMED CT ontology improved the connectivity of the LOINC hierarchy and expanded its coverage by 9,006 additional LOINC codes.

Conclusions:

  • LOINC is a robust and well-maintained terminology with a limited number of logical inconsistencies found.
  • Representing LOINC in OWL DL and integrating it with SNOMED CT offers significant benefits for enhancing its structure and coverage.
  • The study highlights the potential of Description Logic for improving the quality and utility of standard terminologies in healthcare.