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

Ethical Standards II01:23

Ethical Standards II

897
Ethical standards are the backbone of nursing practice, guiding nurses as they interact with patients, families, and colleagues. These standards are crucial for providing safe, empathetic care centered on the patient's needs.
Nurses are entrusted with upholding various ethical principles and standards. Nurses forge solid therapeutic relationships using trust, empathy, autonomy, confidentiality, and professional competence.
Confidentiality is crucial, embodying respect for individual privacy...
897
Documentation in Long-Term and Home Healthcare Setting01:29

Documentation in Long-Term and Home Healthcare Setting

1.1K
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
1.1K
Ethical Standards I01:25

Ethical Standards I

1.1K
The American Nurses Association (ANA) created and implemented the first nationally accepted Code of Ethics for Nurses with Interpretive Statements. The Code of Ethics is a living document regularly updated by the ANA and establishes an ethical standard that is non-negotiable for nurses in all roles and settings.
The Code of Ethics provisions outline the nurse's duty to the patient, the healthcare team, the profession, and society. The Code's fundamental principles include advocacy,...
1.1K
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

657
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...
657
Data Reporting and Recording01:24

Data Reporting and Recording

5.0K
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...
5.0K
Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

1.1K
Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Generalizable multilingual medical text anonymization using generative instruction tuning.

Communications medicine·2026
Same author

Comprehensive representation of health-related phenotypes in one million dogs using topic modelling of electronic health records.

Journal of big data·2026
Same author

Multimodal models for skin cancer classification using clinical freetext and dermatoscopic images.

Communications medicine·2026
Same author

From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers.

BMJ oncology·2025
Same author

The variable relationship between the National Early Warning Score on admission to hospital, the primary discharge diagnosis, and in-hospital mortality.

Internal and emergency medicine·2025
Same author

Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions.

BMJ health & care informatics·2024

Related Experiment Video

Updated: Oct 16, 2025

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

8.4K

Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study.

Zheming Zuo1, Matthew Watson1, David Budgen1

  • 1Department of Computer Science, Durham University, Durham, United Kingdom.

JMIR Medical Informatics
|October 15, 2021
PubMed
Summary

Data anonymization in digital health is theoretically possible but needs practical improvements. Balancing patient privacy and data usability is crucial for reliable electronic health record (EHR) applications.

Keywords:
DPA 2018EHRGDPRSLManonymizationdata sciencehealthcareprivacy-preservingreidentification riskusability

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.7K
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.1K

Related Experiment Videos

Last Updated: Oct 16, 2025

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

8.4K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.7K
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.1K

Area of Science:

  • Digital Health
  • Data Science
  • Health Informatics

Background:

  • Data science in healthcare presents privacy, transparency, and trustworthiness challenges.
  • Regulations like GDPR and the UK Data Protection Act mandate legal bases for data processing and patient consent.
  • Existing data anonymization tools lack standardized privacy functionalities and efficacy in risk assessment.

Purpose of the Study:

  • To systematically map the landscape of data anonymization techniques for digital healthcare.
  • To address challenges in privacy, transparency, and trustworthiness of data science applications in healthcare.

Main Methods:

  • Systematic literature mapping using major academic databases (Google Scholar, Web of Science, Scopus, PubMed) up to June 2020.
  • Focused on five aspects: anonymization operations, privacy models, reidentification risk/usability metrics, anonymization tools, and lawful basis for EHR data anonymization.

Main Results:

  • Identified 239 eligible studies, analyzing anonymization operations, 72 privacy models (conventional and ML-based), and 15 usability metrics.
  • Reviewed 20 data anonymization software tools and evaluated practical feasibility for EHR data.
  • Summarized the lawful basis for practical EHR data anonymization.

Conclusions:

  • Anonymization of electronic health record (EHR) data is achievable in theory.
  • Further research is needed for practical implementation to balance privacy preservation with data usability.
  • Enhanced practical anonymization methods are essential for reliable healthcare applications.