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

Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

8.0K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
8.0K
Types of Reports III: Telephone and Verbal Reports01:26

Types of Reports III: Telephone and Verbal Reports

901
Telephone and Verbal Reports in healthcare settings are two communication methods for conveying therapeutic instructions from healthcare providers to nurses or other healthcare staff.
Here's an overview of each type:
Telephone Orders
901
Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

1.5K
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...
1.5K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

7.2K
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...
7.2K
Methods of Documentation V: CBE01:23

Methods of Documentation V: CBE

1.3K
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...
1.3K
Random and Systematic Errors01:20

Random and Systematic Errors

14.1K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
14.1K

You might also read

Related Articles

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

Sort by
Same author

Screening for Missed Opportunities for Diagnosis in the ED Using eTriggers and Large Language Models.

JAMA network open·2026
Same author

Fellowship Training After Four-Year Emergency Medicine Residency.

The western journal of emergency medicine·2026
Same author

Assessing the impact of the implementation of a remediation and teaching tool on error rate.

The American journal of emergency medicine·2026
Same author

Emergency medicine physicians treating urine cultures when it is not clear they should.

Clinical and experimental emergency medicine·2026
Same author

Artificial Intelligence-powered tiered early warning framework addressing high false alarm rates for in-hospital mortality prediction.

NPJ digital medicine·2026
Same author

Would you have done something differently? A novel marker for improving emergency care.

The American journal of emergency medicine·2026

Related Experiment Video

Updated: Dec 2, 2025

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
09:52

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

17.5K

Using a rule-based system to define error in the emergency department.

Kiersten L Gurley1,2,3, Jonathan L Burstein1,2, Richard E Wolfe1,2

  • 1Emergency Medicine Harvard Medical School Boston Massachusetts USA.

Journal of the American College of Emergency Physicians Open
|November 4, 2020
PubMed
Summary

Developing consensus-based rules for medical error in emergency medicine (EM) quality assurance (QA) improves standardization. Not acquiring necessary information was the most frequent error identified.

Keywords:
adverse eventsemergency medicine educationmedical errorquality assurancequality improvementrisk reduction

More Related Videos

Emergency Undocking in Robotic Surgery: A Simulation Curriculum
06:48

Emergency Undocking in Robotic Surgery: A Simulation Curriculum

Published on: May 20, 2018

9.8K
A Novel Approach for the Administration of Medications and Fluids in Emergency Scenarios and Settings
06:59

A Novel Approach for the Administration of Medications and Fluids in Emergency Scenarios and Settings

Published on: November 9, 2016

30.9K

Related Experiment Videos

Last Updated: Dec 2, 2025

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
09:52

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

17.5K
Emergency Undocking in Robotic Surgery: A Simulation Curriculum
06:48

Emergency Undocking in Robotic Surgery: A Simulation Curriculum

Published on: May 20, 2018

9.8K
A Novel Approach for the Administration of Medications and Fluids in Emergency Scenarios and Settings
06:59

A Novel Approach for the Administration of Medications and Fluids in Emergency Scenarios and Settings

Published on: November 9, 2016

30.9K

Area of Science:

  • Medical error analysis
  • Quality assurance in healthcare
  • Emergency medicine research

Background:

  • Defining medical error for quality assurance (QA) in emergency medicine (EM) is challenging due to issues with reviewer agreement and reproducibility.
  • A standardized approach is needed to systematically identify medical errors in EM.

Purpose of the Study:

  • To develop a consensus-based set of rules for systematically identifying medical errors in emergency medicine.
  • To establish a standardized definition of error for QA and research purposes.

Main Methods:

  • Prospective, observational study of 920 QA-reviewed cases in an academic EM department.
  • Trained reviewers used a Likert scale to assess for errors and adverse events.
  • A committee of EM leadership, attendings, residents, and nurses validated proposed rules by consensus.

Main Results:

  • 108 rule violations (errors) were identified in 103 cases.
  • The most common error theme was 'not acquiring necessary information' (31%).
  • Other key themes included 'not acting on data' (23%), 'knowledge gaps' (15%), 'communication gaps' (16%), and 'systems issues' (16%).

Conclusions:

  • Consensus-based rules offer a standardized, practical definition of medical error in EM for QA and research.
  • Identifying common rule violations highlights areas for risk reduction strategies.
  • A rule-based definition can improve medical QA, patient care, and physician education.