Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Methods of Documentation II: POMR01:26

Methods of Documentation II: POMR

1.1K
The Problem-Oriented Medical Record (POMR) revolutionized medical record-keeping by introducing a systematic approach focusing on the patient's problems rather than merely listing symptoms. Dr. Lawrence Weed's introduction of this method in the 1960s marked a significant advancement in medical documentation. The POMR framework consists of four key components: the database, problem list, plan of care, and progress notes.
1.1K
Base Quantities and Derived Quantities01:14

Base Quantities and Derived Quantities

22.3K
In any system of units, the units for some physical quantities must be specified through a measurement process. These measurements are the base quantities of the system, and their units are the base units of the system. The algebraic combinations of the base values can then be used to express all other physical quantities. Each of these physical quantities is then referred to as a derived quantity, with each unit being referred to as a derived unit.
The International Organization for...
22.3K
Quality Assurance01:19

Quality Assurance

210
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...
210
Ordinal Level of Measurement00:55

Ordinal Level of Measurement

26.0K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
26.0K
Quality Control01:05

Quality Control

325
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...
325
Nominal Level of Measurement00:56

Nominal Level of Measurement

30.9K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal...
30.9K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Identifying subgroups of ICU patients with high mortality rates using machine learning: A nationwide, population-based study.

Journal of critical care·2026
Same author

Mapping the terminology of the early rescue chain to the Foundation of ICD-11: Registered report protocol.

PloS one·2026
Same author

Perceived Inappropriateness of Intensive Care Treatment Among Clinicians: A Cross-Sectional Nationwide Survey on the Prevalence, Associated Factors, and Outcomes.

Critical care medicine·2026
Same author

Data Resource Profile Update: The Dutch National Intensive Care Evaluation (NICE) registry.

International journal of epidemiology·2026
Same author

Detecting Patients with Chronic Kidney Disease Using General Practitioner Electronic Health Records and Electronic Phenotypes.

Studies in health technology and informatics·2026
Same author

Current Data Collection Efforts in Neuromuscular Disease Research: A Scoping Review.

Studies in health technology and informatics·2026

Related Experiment Video

Updated: Sep 22, 2025

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

9.4K

FAIRifying a Quality Registry Using OMOP CDM: Challenges and Solutions.

Daniel Puttmann1,2, Nicolette De Keizer1,2, Ronald Cornet2

  • 1The National Intensive Care Evaluation (NICE) registry, The Netherlands.

Studies in Health Technology and Informatics
|May 25, 2022
PubMed
Summary

The Dutch National Intensive Care Evaluation (NICE) registry adopted the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) to make health data FAIR. This initiative addressed challenges in data modeling and interoperability for intensive care units (ICUs).

Keywords:
ETL ProcessOHDSIOMOP CDMQuality Registry

More Related Videos

A Quantitative Fitness Analysis Workflow
11:39

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

14.6K
Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.8K

Related Experiment Videos

Last Updated: Sep 22, 2025

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

9.4K
A Quantitative Fitness Analysis Workflow
11:39

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

14.6K
Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.8K

Area of Science:

  • Health Informatics
  • Data Management
  • Clinical Research

Background:

  • The COVID-19 pandemic highlighted the critical need for internationally Findable, Accessible, Interoperable, and Reusable (FAIR) health data.
  • Integrative analysis of health datasets is crucial for advancing medical research and public health initiatives.
  • Quality registries, such as intensive care unit (ICU) registries, face challenges in data standardization and accessibility.

Purpose of the Study:

  • To describe the process and challenges of adopting the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) within the Dutch National Intensive Care Evaluation (NICE) quality registry.
  • To detail the solutions developed to overcome modeling, technical, and communication hurdles during the OMOP CDM adoption.
  • To provide guidance for other healthcare institutions, particularly ICU quality registries, seeking to implement FAIR data principles.

Main Methods:

  • Adoption of the OMOP Common Data Model (CDM) as the standard for the NICE quality registry.
  • Systematic identification and documentation of challenges encountered during the OMOP CDM implementation.
  • Engagement with the OMOP CDM implementation community for knowledge sharing and problem-solving.
  • Iterative refinement of data models and technical processes through trial-and-error.

Main Results:

  • Successful adoption of the OMOP CDM by the NICE quality registry, enhancing data FAIRness.
  • Development of practical solutions for common data modeling, technical integration, and inter-institutional communication issues.
  • Establishment of a replicable framework for other healthcare registries aiming for FAIR data.

Conclusions:

  • Adopting the OMOP CDM is a viable strategy for improving the FAIRness of health data in quality registries.
  • Addressing technical, modeling, and communication challenges through community engagement and practical problem-solving is key to successful implementation.
  • The NICE registry's experience offers valuable insights and transferable solutions for enhancing data interoperability and reusability in critical care settings.