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

Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.6K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.6K
Assessment of the Gastrointestinal System I: Subjective Data01:17

Assessment of the Gastrointestinal System I: Subjective Data

686
Assessing the gastrointestinal (GI) system is a complex process that begins with collecting subjective data. This data, collected through patient interviews, provides crucial insights into the patient's health history, perception patterns, and lifestyle habits, all contributing significantly to GI health.
Health History
The initial step in assessing the GI system is obtaining a comprehensive health history. This includes inquiring about the patient's history or presence of problems...
686
Assessment of the Cardiovascular System I: Subjective Data01:23

Assessment of the Cardiovascular System I: Subjective Data

915
A thorough health history and physical assessment are essential for identifying cardiovascular disease (CVD) symptoms and distinguishing them from other health issues.
Initial Enquiry
Ask the patient about their primary concern and thoroughly explore all reported symptoms.
Medical History
Investigate past illnesses affecting the cardiovascular system, such as angina, anemia, rheumatic fever, congenital heart disease, stroke, thrombophlebitis, dysrhythmias, varicosities
Inquire about symptoms...
915
Angina III: Clinical Manifestations and Assessment01:29

Angina III: Clinical Manifestations and Assessment

263
Angina manifests as chest pain, tightness, or squeezing discomfort typically located behind the breastbone. It can radiate to the neck, jaw, shoulders, and inner aspects of the upper arms, most commonly the left arm. Patients may experience shortness of breath, fatigue, profuse sweating, dizziness, indigestion, heartburn, palpitations, anxiety, and vomiting as accompanying symptoms. This pain often lasts a few minutes and is triggered by physical exertion, emotional stress, heavy meals, or cold...
263
Quality Control01:05

Quality Control

2.8K
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...
2.8K
Quality Assurance01:19

Quality Assurance

2.5K
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...
2.5K

You might also read

Related Articles

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

Sort by
Same author

Automated Calls Added to SMS Reminders Reduce Missed Appointments among High-Risk Patients.

NEJM catalyst innovations in care delivery·2026
Same author

Role of electronic health record in stigma experiences, trust, and care decisions by women with history of pregnancy and substance use disorder: a qualitative study of perceptions of clinical note language.

Journal of the American Medical Informatics Association : JAMIA·2026
Same author

A qualitative interview study investigating patient, health professional, and developer perspectives on real-world implementation of patient-centered AI systems.

NPJ digital medicine·2026
Same author

Identifying postpartum depression subtypes using natural language processing and clinical notes.

BMJ mental health·2026
Same author

Evaluating the use and perceptions of cannabis and vaping post-cannabis legalisation in people with cystic fibrosis and CFTR-related disorder: survey results from a large Canadian adult cystic fibrosis clinic.

BMJ open respiratory research·2025
Same author

A qualitative Interview Study Investigating Patient, Health Professional, and Developer Perspectives on Real-World Implementation of Patient-Centered AI Systems.

Research square·2025

Related Experiment Video

Updated: Feb 9, 2026

Using a Chemical Biopsy for Graft Quality Assessment
05:00

Using a Chemical Biopsy for Graft Quality Assessment

Published on: June 17, 2020

5.6K

A Framework for Data Quality Assessment in Clinical Research Datasets.

Kathleen Lee1, Nicole Weiskopf2, Jyotishman Pathak1

  • 1Weill Cornell Medicine, New York, NY.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|June 2, 2018
PubMed
Summary

Researchers developed a data quality assessment framework for electronic health records (EHR) to improve heart failure cohort validity. The framework demonstrated high scores for data completeness and plausibility, offering a generalizable approach for clinical research.

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.6K
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

797

Related Experiment Videos

Last Updated: Feb 9, 2026

Using a Chemical Biopsy for Graft Quality Assessment
05:00

Using a Chemical Biopsy for Graft Quality Assessment

Published on: June 17, 2020

5.6K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.6K
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

797

Area of Science:

  • Clinical Informatics
  • Health Data Science
  • Cardiovascular Research

Background:

  • Electronic health record (EHR) data availability for multi-institutional research requires accurate patient cohorts.
  • Ensuring the validity of EHR data is crucial, particularly when combined with open-access research data.
  • A consensus-driven approach is needed to assess the quality of clinical data.

Purpose of the Study:

  • To modify an existing data quality assessment (DQA) framework for the clinical domain of heart failure.
  • To create and evaluate an inventory of common phenotype data elements (CPDEs) from open-access datasets using the modified DQA framework.
  • To establish a generalizable method for DQA in clinical research.

Main Methods:

  • Re-operationalized DQA dimensions for heart failure.
  • Developed an inventory of common phenotype data elements (CPDEs) from open-access datasets.
  • Evaluated CPDEs against the modified DQA framework, measuring Conformance, Completeness, and Plausibility.

Main Results:

  • The DQA framework demonstrated high scores for Completeness and Value Conformance.
  • High scores were also observed for Atemporal and Temporal Plausibility.
  • The evaluation confirmed the utility of the modified DQA framework for assessing EHR data quality.

Conclusions:

  • The developed approach provides a generalizable method for data quality assessment in clinical research.
  • Future work includes mapping datasets to standard terminologies and developing a quantitative DQA tool.
  • This framework enhances the reliability of EHR data for multi-institutional studies, particularly in cardiovascular research.