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

Methods of Documentation V: CBE01:23

Methods of Documentation V: CBE

Charting by Exception, or CBE, is a method of documentation used in healthcare, particularly in nursing, that focuses on documenting only significant or abnormal findings rather than recording every detail. This approach aims to streamline the documentation process, improve efficiency, and ensure that healthcare providers can quickly identify deviations from normalcy in patient assessments.
In CBE, healthcare professionals establish predefined standards of practice that define what constitutes...
Legal Guidelines for Documentation01:06

Legal Guidelines for Documentation

The legal guidelines for nursing documentation are essential for ensuring accurate, professional, and ethical recording of patient care. The guidelines are discussed here:
Automated Microbial Diagnostics01:24

Automated Microbial Diagnostics

Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

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 illness...
Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

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 settings,...
Pharmacovigilance01:19

Pharmacovigilance

Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...

You might also read

Related Articles

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

Sort by
Same author

Using sentiment analysis to quantify the relative desirability and acceptability of drug-product attributes.

JAMIA open·2026
Same author

Health, economic, and equity impact of a COVID-19 mobile vaccine clinic program in US: a cost-effectiveness analysis.

Lancet regional health. Americas·2026
Same author

Chondrocytes reprogram chromatin in hypoxic microenvironments to activate CADM1-AS1/HDAC1 complex-mediated anti-inflammation signals.

Nature communications·2026
Same author

Effectiveness of mobile vaccine clinics on COVID-19 vaccination uptake in the USA: an observational study.

BMJ public health·2026
Same author

Physician Variation in Early Sepsis Management.

JAMA network open·2026
Same author

Erratum: Infection prevention behaviors and perceptions of nurses in a medical intensive care unit - CORRIGENDUM.

Antimicrobial stewardship & healthcare epidemiology : ASHE·2026

Related Experiment Video

Updated: May 20, 2026

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

Evaluating current automatic de-identification methods with Veteran's health administration clinical documents.

Oscar Ferrández1, Brett R South, Shuying Shen

  • 1Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA. oscar.ferrandez@utah.edu

BMC Medical Research Methodology
|July 31, 2012
PubMed
Summary

Automated de-identification methods for Electronic Health Records (EHR) were evaluated on Veterans Health Administration (VHA) clinical notes. Machine learning approaches improved precision, while rule-based systems enhanced recall for Protected Health Information (PHI).

Related Experiment Videos

Last Updated: May 20, 2026

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

Area of Science:

  • Health Informatics
  • Natural Language Processing
  • Data Privacy

Background:

  • Electronic Health Records (EHR) generate vast amounts of digital information, but Protected Health Information (PHI) limits its use.
  • Automated de-identification methods aim to remove PHI according to the Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor method.
  • Evaluating existing de-identification tools on Veterans Health Administration (VHA) clinical documents is crucial for improving data utility.

Purpose of the Study:

  • To assess the performance of existing automated text de-identification systems on VHA clinical documents.
  • To compare the effectiveness of different de-identification methods across various PHI categories.
  • To identify needs for new methods to enhance PHI de-identification in VHA clinical notes.

Main Methods:

  • Five text de-identification systems were installed and evaluated "out-of-the-box" on a VHA clinical document corpus.
  • Machine learning-based systems were trained on the 2006 i2b2 de-identification corpora and evaluated on the VHA corpus.
  • Performance was assessed using recall, precision, and F(2)-measure, with ten-fold cross-validation employed.

Main Results:

  • Rule-based and pattern-matching systems generally achieved higher recall, while machine learning systems excelled in precision.
  • The highest "out-of-the-box" F(2)-measure was 67% for partial matches; best precision and recall were 95% and 78%, respectively.
  • Ten-fold cross-validation increased the F(2)-measure to 79% for partial matches, indicating improved performance with model tuning.

Conclusions:

  • "Out-of-the-box" evaluation provided insights into the best de-identification methods for VHA clinical documents.
  • Error analysis highlighted the need for customization to address PHI formats specific to VHA data.
  • Findings informed the development of a "best-of-breed" automated de-identification application for VHA clinical text.