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 VII: EMR01:30

Methods of Documentation VII: EMR

922
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...
922
Types of Reports III: Telephone and Verbal Reports01:26

Types of Reports III: Telephone and Verbal Reports

816
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
816
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

633
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...
633
Guidelines for Nursing Documentation I01:30

Guidelines for Nursing Documentation I

1.2K
Quality documentation and reporting share essential characteristics that ensure they are practical and valuable resources for those who use them. These characteristics are:
Factual:  
The following points emphasize the significance of upholding accurate and unbiased documentation in healthcare.
1.2K
SBAR II: Application of SBAR01:14

SBAR II: Application of SBAR

4.8K
SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
SBAR Report from a Nurse to a Health Care Provider
S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...
4.8K
Methods of Documentation V: CBE01:23

Methods of Documentation V: CBE

989
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...
989

You might also read

Related Articles

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

Sort by
Same author

Maraviroc alleviates neuropathic pain symptoms in a mouse model of spared nerve injury.

Molecular pharmacology·2026
Same author

Optimizing Radiography Utilization: Multidisciplinary Expert Consensus Recommendations Endorsed by the Society of Academic Bone Radiologists, Society of Skeletal Radiology, American Society of Emergency Radiology, Orthopaedic Trauma Association, American Academy of Emergency Medicine, and American Rhinologic Society.

Radiology·2026
Same author

Extracting adverse event nature, severity, timelines and resulting interventions from clinical notes of patients receiving CAR-T therapy using large language models.

medRxiv : the preprint server for health sciences·2026
Same author

From Chaos to Coordination: Fundamentals of Teamwork and How Residents Can Practice Teaming to Improve Acute Care.

Annals of emergency medicine·2026
Same author

Use of Paracervical Blocks for Patients Who Undergo Intrauterine Device Insertion.

JAMA network open·2026
Same author

HIV-seq reveals gene expression differences between HIV-transcribing cells from viremic and suppressed people with HIV.

Nature communications·2026

Related Experiment Video

Updated: Sep 19, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

693

Evaluating large language models for drafting emergency department encounter summaries.

Christopher Y K Williams1, Jaskaran Bains2, Tianyu Tang2

  • 1Bakar Computational Health Sciences Institute, University of California, San Francisco, California, United States of America.

PLOS Digital Health
|June 17, 2025
PubMed
Summary

Large language models (LLMs) can summarize clinical notes, but errors like hallucinations and omissions occur. While most errors have low harm potential, careful clinician review is essential for patient safety.

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

595
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

16.0K

Related Experiment Videos

Last Updated: Sep 19, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

693
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

595
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

16.0K

Area of Science:

  • Clinical Informatics
  • Artificial Intelligence in Healthcare
  • Medical Documentation

Background:

  • Large language models (LLMs) show promise for clinical applications like text summarization.
  • The increasing deployment of AI scribes necessitates rigorous evaluation of their accuracy in healthcare settings.

Purpose of the Study:

  • To evaluate the performance of GPT-4 and GPT-3.5-turbo in generating Emergency Department (ED) encounter summaries.
  • To identify the prevalence and types of errors (inaccuracy, hallucination, omission) in LLM-generated ED summaries.

Main Methods:

  • Cross-sectional study of 100 randomly sampled adult ED visits (2012-2023).
  • Evaluation of GPT-4 and GPT-3.5-turbo generated summaries against three criteria: inaccuracy, hallucination, and omission.
  • Analysis of error types and locations within encounter summary sections.

Main Results:

  • GPT-4 generated error-free summaries in 33% of cases; GPT-3.5-turbo in 10%.
  • GPT-4 summaries had 10% inaccuracies, 42% hallucinations, and 47% omissions.
  • Inaccuracies/hallucinations were common in 'Plan' sections; omissions in 'Physical Examination' and 'History of Presenting Complaint' sections.
  • Mean potential harm score for errors was low (0.57/7).

Conclusions:

  • LLMs can generate clinical encounter summaries, but are prone to hallucinations and omissions.
  • Errors in LLM-generated clinical text, while often low-harm, require careful clinician review.
  • Understanding error patterns is crucial for safe integration of LLMs in clinical workflows.