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

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...
Data Validation01:03

Data Validation

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

Detection of Gross Error: The Q Test

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...

You might also read

Related Articles

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

Sort by
Same author

Type 2 diabetes.

Lancet (London, England)·2018
Same author

Quality of recording of diabetes in the UK: how does the GP's method of coding clinical data affect incidence estimates? Cross-sectional study using the CPRD database.

BMJ open·2017
Same author

Exploring practical approaches to maximising data quality in electronic healthcare records in the primary care setting and associated benefits. Report of panel-led discussion held at SAPC in July 2014.

Primary health care research & development·2016
Same author

Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes.

NMR in biomedicine·2016
Same author

Exploiting the potential of large databases of electronic health records for research using rapid search algorithms and an intuitive query interface.

Journal of the American Medical Informatics Association : JAMIA·2013
Same author

Optimising use of electronic health records to describe the presentation of rheumatoid arthritis in primary care: a strategy for developing code lists.

PloS one·2013
Same journal

A GenAI Pipeline for Violinist Kinematic Data Management.

Studies in health technology and informatics·2026
Same journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026
Same journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Predicting Postpartum Depression Using Imbalance-Aware Machine Learning.

Studies in health technology and informatics·2026
Same journal

Validation of Deep-Learning Models for Autosegmentation of Brain Metastases.

Studies in health technology and informatics·2026
Same journal

Delay-Dependent Gating in Modular RNNs.

Studies in health technology and informatics·2026
See all related articles

Related Experiment Videos

Harnessing Pattern Recognition Techniques for Data Quality Detection.

A Rosemary Tate1

  • 1University of Sussex.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Data quality detection can be engaging by reframing it as detective work. AI tools transform tedious tasks into rewarding investigations, making health data analysis more empowering.

Keywords:
Artificial IntelligenceData qualityData quality detectiveMultivariate analysisPattern recognition

Related Experiment Videos

Area of Science:

  • Health Informatics
  • Data Science
  • Artificial Intelligence

Background:

  • Traditional data quality approaches often neglect the perception of the work as tedious.
  • This perception can hinder thoroughness and lead to rushed quality checks.
  • A more engaging perspective is needed to improve data quality practices.

Purpose of the Study:

  • To reframe data quality detection as an engaging investigative process.
  • To highlight the potential of AI-powered tools in transforming data quality work.
  • To share experiences of identifying data quality issues in health datasets.

Main Methods:

  • Conceptual reframing of data quality work as detective investigation.
  • Application of Artificial Intelligence (AI)-powered tools for data quality detection.
  • Case illustration using health datasets.

Main Results:

  • Data quality detection can be made enjoyable and rewarding, akin to detective work.
  • AI tools offer novel methods to enhance the process of identifying data quality issues.
  • The role of a 'data quality detective' can be empowering for analysts.

Conclusions:

  • Shifting the perspective on data quality work can increase engagement and effectiveness.
  • AI presents a significant opportunity to revolutionize data quality assessment in health informatics.
  • Adopting an investigative mindset enhances the rewarding aspects of ensuring data integrity.