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

5.2K
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...
5.2K
Applications of Life Tables01:22

Applications of Life Tables

99
Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
99
Data Collection by Experiments01:13

Data Collection by Experiments

24.6K
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...
24.6K
Data Collection by Observations01:08

Data Collection by Observations

12.2K
Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
12.2K
Data Reporting and Recording01:24

Data Reporting and Recording

4.8K
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...
4.8K
Naturalistic Observations02:30

Naturalistic Observations

15.6K
If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
15.6K

You might also read

Related Articles

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

Sort by
Same author

Moving artificial intelligence from research to real-world clinical use in neurology.

Nature reviews. Neurology·2026
Same author

BRIDGE: benchmarking large language models for understanding real-world clinical practice texts.

Nature biomedical engineering·2026
Same author

Preoperative Decline and Postoperative Recovery of Wearable-Derived Physical Activity over a Four-Year Perioperative Period in Total Knee and Hip Arthroplasty.

Sensors (Basel, Switzerland)·2026
Same author

Sleep and Activity Patterns in Depression From Wearable Data: Unsupervised Clustering Study.

Journal of medical Internet research·2026
Same author

What Utah's clinical AI sandbox reveals about independent oversight.

Nature medicine·2026
Same author

Coconstructing CHAMP, an Artificial Intelligence Chatbot for Pediatric Infectious Symptoms Management: Protocol for a Multiphase Participatory Study.

JMIR research protocols·2026

Related Experiment Video

Updated: Aug 9, 2025

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

6.3K

Best practices in the real-world data life cycle.

Joe Zhang1,2, Joshua Symons3, Paul Agapow4

  • 1Instititute of Global Health Innovation, Imperial College London, London, United Kingdom.

PLOS Digital Health
|February 22, 2023
PubMed
Summary
This summary is machine-generated.

Real-world data (RWD) is increasingly vital for healthcare innovation. This study outlines a standardized RWD life cycle and best practices for creating high-quality datasets for diverse applications.

More Related Videos

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.3K
Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management
08:01

Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management

Published on: November 30, 2022

4.4K

Related Experiment Videos

Last Updated: Aug 9, 2025

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

6.3K
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.3K
Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management
08:01

Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management

Published on: November 30, 2022

4.4K

Area of Science:

  • Health Informatics
  • Data Science
  • Biopharmaceutical Research

Background:

  • The digitization of healthcare has led to an exponential increase in the availability and scope of real-world data (RWD).
  • The 21st Century Cures Act of 2016 has spurred innovation in the RWD lifecycle, driven by the biopharmaceutical industry's demand for regulatory-grade real-world evidence.
  • The utility of RWD is expanding beyond drug development to encompass population health and clinical applications for payors, providers, and health systems.

Purpose of the Study:

  • To describe a standardized real-world data (RWD) life cycle.
  • To delineate best practices for improving RWD quality and utility for emerging healthcare applications.
  • To highlight key themes for sustainable and scalable RWD lifecycle management.

Main Methods:

  • Review of academic literature and author experience in data curation across various sectors.
  • Description of a standardized RWD life cycle encompassing key data production steps.
  • Identification of seven critical themes for effective RWD lifecycle management.

Main Results:

  • A standardized RWD life cycle is proposed, detailing essential steps for producing high-quality data for analysis and insights.
  • Best practices are delineated to enhance current data pipelines and maximize RWD value.
  • Seven key themes are highlighted for ensuring the sustainability and scalability of RWD lifecycles: data standards adherence, tailored quality assurance, data entry incentivization, natural language processing deployment, data platform solutions, RWD governance, and ensuring equity and representation.

Conclusions:

  • Accelerating life cycle improvements is crucial for harnessing the full potential of RWD for diverse and emerging use cases.
  • Implementing the proposed standardized RWD life cycle and best practices can significantly enhance data quality and utility.
  • Focusing on the seven highlighted themes will support the sustainable and scalable management of RWD for advanced healthcare applications.