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

Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

3.3K
Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
3.3K
Integrated Healthcare System01:20

Integrated Healthcare System

2.3K
An integrated healthcare system (IHS) is a set of organizations that provides for or arranges to provide coordinated and continuous service to a defined population. The IHS takes responsibility for that particular population's health status and outcome, both clinically and fiscally. An integrated healthcare system is a well-organized, well-coordinated, and collaborative network. The integrated delivery system is a network that connects different healthcare providers to deliver organized,...
2.3K
Data Validation01:03

Data Validation

6.3K
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...
6.3K
Documentation in Long-Term and Home Healthcare Setting01:29

Documentation in Long-Term and Home Healthcare Setting

1.4K
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
1.4K
Methods Of Healthcare Delivery System01:26

Methods Of Healthcare Delivery System

3.9K
At the different levels of the healthcare system, we see varying methods of healthcare used. These methods include managed care systems, case management, and primary healthcare.
Managed Care System:
The managed care system is designed to control the cost while maintaining the quality of care. The patient's care from admission to discharge is planned by the primary care provider or the case manager, also known as the gatekeeper. In a managed care system, the number of care providers is...
3.9K
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

844
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.
For example, a patient with a chronic...
844

You might also read

Related Articles

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

Sort by
Same author

Beyond the algorithm: health technology assessment frameworks for AI in cardiology under the European Union Health Technology Assessment Regulation: a systematic review.

Annals of translational medicine·2026
Same author

Health Data Quality Skill Gaps and Training Needs Among European Health Data Stakeholders: Cross-Sectional Survey.

Journal of medical Internet research·2026
Same author

Addressing Data Quality Challenges in Lung Cancer Data Within the Observational Medical Outcomes Partnership Common Data Model: Observational Study.

Journal of medical Internet research·2026
Same author

Rethinking Trust in Synthetic Health Data: Lessons From 7 European Research Initiatives.

Journal of medical Internet research·2026
Same author

Regulations for international non-proprietary name prescribing and substitution, relevant for cross-border ePrescribing and eDispensation services in the European Union.

European journal of public health·2025
Same author

The Potential to Leverage Real-World Data for Pediatric Clinical Trials: A Proof-of-Concept Study.

Journal of medical Internet research·2025

Related Experiment Video

Updated: Jan 11, 2026

The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living
08:36

The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living

Published on: July 28, 2022

4.3K

Assessing Data Quality in Heterogeneous Health Care Integration: Simulation Study of the AIDAVA Framework.

Jens Declerck1,2, Ömer Durukan Kılıç3, Ensar Emir Erol3

  • 1The European Institute for Innovation Through Health Data, Oosterzele, Belgium.

JMIR Medical Informatics
|November 12, 2025
PubMed
Summary

The AIDAVA framework uses AI and knowledge graphs for dynamic health data quality validation. It effectively detects and manages missing values and inconsistencies during data integration.

Keywords:
data qualitydata quality assessmentdata quality dimensionsdata quality frameworkfit for purposehealth dataknowledge graphontologysecondary use

More Related Videos

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
07:51

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

8.0K
Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit
06:52

Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit

Published on: September 30, 2020

10.4K

Related Experiment Videos

Last Updated: Jan 11, 2026

The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living
08:36

The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living

Published on: July 28, 2022

4.3K
Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
07:51

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

8.0K
Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit
06:52

Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit

Published on: September 30, 2020

10.4K

Area of Science:

  • Health Informatics
  • Data Science
  • Artificial Intelligence

Background:

  • Integrated health data is crucial for research and policy.
  • Data quality issues like missing values and inconsistencies hinder secondary data use.
  • Existing static quality assessments fail to address evolving data pipelines.

Purpose of the Study:

  • Evaluate the AIDAVA (artificial intelligence-powered data curation and validation) framework.
  • Assess AIDAVA's dynamic, life cycle-based validation using knowledge graphs and SHACL rules.
  • Determine the framework's ability to detect and manage completeness and consistency issues during health data integration.

Main Methods:

  • Simulated data quality challenges in the MIMIC-III dataset, including missing values and logical inconsistencies.
  • Transformed data into source knowledge graphs and integrated them into a unified personal health knowledge graph.
  • Applied SHACL validation rules iteratively during integration and assessed quality under varying noise levels and integration orders.

Main Results:

  • The AIDAVA framework successfully detected completeness and consistency issues in all simulated scenarios.
  • Data completeness significantly impacted the interpretability of consistency scores.
  • Domain-specific attributes, such as diagnoses and procedures, showed higher sensitivity to integration order and data gaps.

Conclusions:

  • AIDAVA enables dynamic, rule-based data validation throughout the health data life cycle.
  • The framework addresses vulnerabilities and cross-dimensional effects for scalable, high-quality health data integration.
  • Future research should focus on live clinical deployment and expanding quality dimensions.