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

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...
Data Validation01:15

Data Validation

Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
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...
Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters assessment...
Purpose of Health Records I01:11

Purpose of Health Records I

The vital purpose of health records is to provide a complete and accurate account of a patient's medical history, including communication, diagnostic and therapeutic orders, care planning, research, and quality review.
Here's a breakdown of how health records serve these purposes:

You might also read

Related Articles

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

Sort by
Same author

Caution: very high HDL-cholesterol.

Progress in cardiovascular diseases·2026
Same author

Perceptions of end-of-life care quality among bereaved closest contacts of community-dwelling older Australians: a cross-sectional survey of the ASPREE cohort.

Research square·2026
Same author

Speech-in-Noise Ability and Longitudinal Cortical Thinning in Speech-Processing Networks.

JAMA otolaryngology-- head & neck surgery·2026
Same author

Sepsis risk associated with clonal hematopoiesis of indeterminate potential (CHIP): a secondary analysis of the ASPREE trial.

Leukemia·2026
Same author

Anti-inflammatory diet and mental health outcomes in an aging population: evidence from a preventive population-based target trial emulation.

GeroScience·2026
Same author

Polygenic Risk Identifies Older Adults Who May Benefit From Aspirin for the Primary Prevention of Ischemic Stroke.

Stroke·2026

Related Experiment Video

Updated: Jun 3, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Identifying and improving unreliable items in registries through data auditing.

Cameron D Willis1, Damien J Jolley, John J McNeil

  • 1School of Population Health and Clinical Practice, The University of Adelaide, SA 5005, Australia. cameron.willis@adelaide.edu.au

International Journal for Quality in Health Care : Journal of the International Society for Quality in Health Care
|March 29, 2011
PubMed
Summary
This summary is machine-generated.

Data reliability in the Australian and New Zealand Haemostasis Registry varies. While demographic data is reliable, critical clinical variables like infusion volumes and treatment times require improvement for accurate clinical research.

Related Experiment Videos

Last Updated: Jun 3, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Area of Science:

  • Medical Informatics
  • Clinical Data Management
  • Health Services Research

Background:

  • Ensuring the credibility of clinical registry data is crucial for reliable research.
  • The Australian and New Zealand Haemostasis Registry collects data on off-licence use of recombinant activated factor VII (rFVIIa).

Purpose of the Study:

  • To assess the data reliability of the Australian and New Zealand Haemostasis Registry.
  • To identify specific variables within the registry that exhibit poor data quality.

Main Methods:

  • A random sample of 5% of registry cases (76 cases) were independently re-abstracted by a blinded auditor.
  • Agreement percentages were calculated for categorical variables.
  • Mean differences, standard deviations, and coefficients of variation were calculated for continuous variables.

Main Results:

  • High inter-rater reliability was observed for age (88%), gender (99%), and ICU admission (99%).
  • Significant unreliability was found in crystalloid infusion volumes (CV 123.01%), red blood cell units (CV 92.05%), and time from bleeding onset to rFVIIa administration (CV 153.06%).

Conclusions:

  • Clinical registry audits effectively identify variables with poor data reliability.
  • While repeated audits do not enhance reliability, they aid in evaluating improvements from modified data collection processes.