Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Data Reporting and Recording

Data Reporting and Recording

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...

Data Collection by Experiments

Data Collection by Experiments

Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public clinical trial...

Censoring Survival Data

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...

Data Collection I

Data Collection I

Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of data...

Issues And Trends In Healthcare Delivery System

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

You might also read

Related Articles

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

Sort by

Same author

Association of COVID-19 public health measures with cardiac non-invasive testing utilisation in Ontario, Canada: a population-based, retrospective time series analysis.

BMJ open·2026

Same author

Sex Differences in Cardiovascular Health Status and Long-Term Outcomes in a Primary Prevention Cohort.

JACC. Advances·2025

Same author

Feasibility study: Characterizing acute lesion dimensions in patients with and without devices using noncontrast (native T<sub>1</sub>-weighted) magnetic resonance imaging after ventricular tachycardia/premature ventricular complex radiofrequency ablation.

Heart rhythm·2025

Same author

Cardiac function evaluation in healthy volunteers and patients with implantable cardioverter-defibrillators using high-bandwidth spoiled gradient-echo cine.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance·2025

Same author

Impact of Zero Coronary Artery Calcium Scoring on Downstream Cardiac Testing and Cardiac Outcomes Compared With No Testing.

CJC open·2025

Same author

Quantifying the Relationship Between At-Home Shoulder Physiotherapy Participation and Outcome: What can a Watch Tell Us?

The Journal of the American Academy of Orthopaedic Surgeons·2025

Search research articles

Related Experiment Video

Updated: May 31, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Innovation and Our Data: This Does Not Need to Be Scary.

Philip Boyer¹, Idan Roifman^2,3,4

¹INOVAIT, Sunnybrook Research Institute, Toronto, Ontario, Canada.

Healthcare Management Forum

|May 28, 2026

Summary

Canadian healthcare innovation faces data access challenges. This article examines these issues and proposes governance solutions to improve data utilization for better health outcomes, aligning with the Pan-Canadian Health Data Charter principles.

More Related Videos

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

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

Published on: November 22, 2019

Related Experiment Videos

Last Updated: May 31, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

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

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

Published on: November 22, 2019

Area of Science:

Health Informatics
Health Policy
Artificial Intelligence in Healthcare

Background:

Artificial Intelligence (AI) highlights the value of Canadian health data.
Canadian technology industry has limited access to health data for innovation.
Pan-Canadian Health Data Charter emphasizes data-driven innovation.

Purpose of the Study:

Examine the health data access landscape for innovation in Canada.
Identify structural and cultural challenges hindering data access.
Propose governance mechanisms to promote ethical data use and innovation.

Main Methods:

Analysis of the Canadian health data innovation access landscape.
Identification of structural and cultural barriers.
Review of international best practices and current legislation.

Main Results:

Significant structural and cultural challenges impede data access for Canadian healthcare technology developers.
Current data access is often dysfunctional, limiting innovation.
Achieving data-driven innovation requires addressing multiple interconnected principles.

Conclusions:

Trustworthy and efficient data access is crucial for a Canadian health data ecosystem.
Proposed governance mechanisms aim to foster ethical data use and innovation.
Addressing all ten principles of the Charter is structurally dependent for success.