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

Data Validation01:03

Data Validation

5.4K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
5.4K
Quality Assurance01:19

Quality Assurance

207
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...
207
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.4K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.4K
Quality Control01:05

Quality Control

315
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...
315
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

7.5K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
7.5K
Data Reporting and Recording01:24

Data Reporting and Recording

4.9K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
4.9K

You might also read

Related Articles

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

Sort by
Same author

Measuring the Quality of Datasets: Development of the IDEFIM Indicator Set for Empirical Health Research.

Journal of medical Internet research·2026
Same author

The European Health Data Space and Data Quality: A Comparison with the IDEFIM Framework for Empirical Health Research.

Studies in health technology and informatics·2026
Same author

Quality of Data Sets in Empirical Health Research: The IDEFIM Framework.

Studies in health technology and informatics·2025
Same author

Point Prevalence Rate Useful Only to a Limited Extent.

Deutsches Arzteblatt international·2025
Same author

A Case Study on Coding with ICD-11.

Studies in health technology and informatics·2025
Same author

Data Quality and Diversity in Health Care and Health Research.

Studies in health technology and informatics·2025

Related Experiment Video

Updated: Sep 17, 2025

A Quantitative Fluorescence Microscopy-based Single Liposome Assay for Detecting the Compositional Inhomogeneity Between Individual Liposomes
09:12

A Quantitative Fluorescence Microscopy-based Single Liposome Assay for Detecting the Compositional Inhomogeneity Between Individual Liposomes

Published on: December 13, 2019

8.0K

How to Define a Data Quality Indicator?

Jürgen Stausberg1, Sonja Harkener1

  • 1Institute for Medical Informatics, Biometry and Epidemiology, Faculty of Medicine, University Duisburg-Essen, Essen, Germany.

Studies in Health Technology and Informatics
|July 1, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a structured approach for publishing data quality indicator definitions, enabling unambiguous meaning and use. This framework facilitates independent comparison of indicator results across different entities.

Keywords:
Data qualityhealth researchquality indicatorsregistries

More Related Videos

Fast Inspection of Quality of Indigo Naturalis by Multiple Light Scattering
03:40

Fast Inspection of Quality of Indigo Naturalis by Multiple Light Scattering

Published on: August 18, 2023

526
Interventional Diagnostic Procedure: A Practical Guide for the Assessment of Coronary Vascular Function
10:28

Interventional Diagnostic Procedure: A Practical Guide for the Assessment of Coronary Vascular Function

Published on: March 15, 2022

5.3K

Related Experiment Videos

Last Updated: Sep 17, 2025

A Quantitative Fluorescence Microscopy-based Single Liposome Assay for Detecting the Compositional Inhomogeneity Between Individual Liposomes
09:12

A Quantitative Fluorescence Microscopy-based Single Liposome Assay for Detecting the Compositional Inhomogeneity Between Individual Liposomes

Published on: December 13, 2019

8.0K
Fast Inspection of Quality of Indigo Naturalis by Multiple Light Scattering
03:40

Fast Inspection of Quality of Indigo Naturalis by Multiple Light Scattering

Published on: August 18, 2023

526
Interventional Diagnostic Procedure: A Practical Guide for the Assessment of Coronary Vascular Function
10:28

Interventional Diagnostic Procedure: A Practical Guide for the Assessment of Coronary Vascular Function

Published on: March 15, 2022

5.3K

Area of Science:

  • Data Science
  • Information Management

Background:

  • Data quality indicators require clear conceptualization, algorithmic expression, and unambiguous structural definitions for effective use.
  • Existing proposals for indicator structures necessitate updates based on comprehensive literature reviews.

Purpose of the Study:

  • To develop and publish a standardized structure for data quality indicator definitions.
  • To enhance the comparability of data quality indicator results calculated by independent actors.

Main Methods:

  • A comprehensive literature review was conducted to inform the update of an existing proposal.
  • The IDEFIM project facilitated the development of a new structure for publishing indicator definitions.

Main Results:

  • A robust structure for publishing data quality indicator definitions has been developed.
  • The structure ensures unambiguous meaning and facilitates consistent application of indicators.

Conclusions:

  • The developed structure supports the publication of data quality indicator definitions.
  • This standardization enables reliable comparison of indicator results from diverse sources, improving data quality assessment.