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 Reports III: Telephone and Verbal Reports01:26

Types of Reports III: Telephone and Verbal Reports

756
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
756
Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

848
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...
848
Methods of Documentation V: CBE01:23

Methods of Documentation V: CBE

913
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...
913
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

586
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...
586
Psychosurgery01:30

Psychosurgery

61
Psychosurgery, the surgical alteration or permanent removal of brain tissue to alleviate severe psychological conditions, stands as one of the most radical and controversial treatments in the history of mental health care. Its development and application have evolved significantly, marked by dramatic shifts in scientific understanding and ethical perspectives.
Historical Development of Psychosurgery
In the 1930s, Portuguese neurologist Antonio Egas Moniz introduced a surgical procedure designed...
61
SBAR II: Application of SBAR01:14

SBAR II: Application of SBAR

4.5K
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...
4.5K

You might also read

Related Articles

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

Sort by
Same author

Discharge Opioid Dosing and New Opioid Use Disorder After Elective Surgery in Opioid-Naive Adults: An Emulated Target Trial.

The Journal of surgical research·2026
Same author

Benchmarking information extraction of physical activity from electronic health record with large language models: an natural language processing pipeline and comparative evaluation.

Journal of the American Medical Informatics Association : JAMIA·2026
Same author

Pre-Post Evaluation of Documentation Burden, Time Perception, and Cognitive Workload of Ambient AI Scribe Tools.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same author

Time to Measure: A Proposed Theoretical Model for Head and Neck Cancer Intervals of Care.

World journal of otorhinolaryngology - head and neck surgery·2026
Same author

Using Artificial Intelligence to Detect Housing Status in Unstructured Electronic Health Record Data.

Joint Commission journal on quality and patient safety·2026
Same author

"I'm Having the Worst Time": The Lived Experiences of Informal Caregivers for Older Adults Undergoing Major Elective Surgery.

Annals of surgery·2026
Same journal

Notice of Retraction. Ren Y, et al. Personality Traits and Social Isolation in Older Adults. JAMA Netw Open. 2026;9(5):e269569.

JAMA network open·2026
Same journal

Error in Grant Number in Funding/Support Section.

JAMA network open·2026
Same journal

The Supplementary Role of Friends in Caregiving Networks.

JAMA network open·2026
Same journal

Urbanicity, Neighborhood Conditions, and Dementia Mortality.

JAMA network open·2026
Same journal

Equity and Cancer Survival Among Veterans Health Administration Patients: A Systematic Review and Meta-Analysis.

JAMA network open·2026
Same journal

Limbic System Microstructure in Neonates With Antenatal Opioid Exposure.

JAMA network open·2026
See all related articles

Related Experiment Video

Updated: Jul 14, 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

605

Large Language Model-Based Chatbot vs Surgeon-Generated Informed Consent Documentation for Common Procedures.

Hannah Decker1, Karen Trang1, Joel Ramirez1

  • 1Department of Surgery, University of California, San Francisco.

JAMA Network Open
|October 9, 2023
PubMed
Summary
This summary is machine-generated.

Large language model (LLM) chatbots show potential in improving surgical informed consent by generating more complete and accurate information than surgeon-generated documents. While not perfect, LLM chatbots could enhance patient understanding and ease physician documentation burden.

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

255
Emergency Undocking in Robotic Surgery: A Simulation Curriculum
06:48

Emergency Undocking in Robotic Surgery: A Simulation Curriculum

Published on: May 20, 2018

9.3K

Related Experiment Videos

Last Updated: Jul 14, 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

605
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

255
Emergency Undocking in Robotic Surgery: A Simulation Curriculum
06:48

Emergency Undocking in Robotic Surgery: A Simulation Curriculum

Published on: May 20, 2018

9.3K

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Surgical Patient Education

Background:

  • Informed consent is crucial for patient care before invasive procedures but is often inadequate.
  • Electronic consent forms aim to improve patient comprehension through readable, accurate, and complete information.
  • The efficacy of large language model (LLM)-based chatbots in enhancing informed consent documentation remains unexplored.

Purpose of the Study:

  • To compare the readability, accuracy, and completeness of information on surgical risks, benefits, and alternatives (RBAs) generated by LLM-based chatbots versus surgeons.

Main Methods:

  • A cross-sectional study compared surgeon-generated RBAs from electronic consent forms with LLM-based chatbot-generated RBAs (ChatGPT-3.5) for six common surgical procedures.
  • Readability was assessed using validated scales (Flesch-Kincaid, Gunning Fog, SMOG, Coleman-Liau).
  • Accuracy and completeness were evaluated using a rubric based on recommendations from leading healthcare organizations.

Main Results:

  • LLM-chatbot generated RBAs demonstrated significantly higher composite scores for completeness and accuracy (2.2) compared to surgeon-generated RBAs (1.6) (P < .001).
  • Chatbots outperformed surgeons in describing the benefits (2.3 vs 1.4) and alternatives (2.7 vs 1.4) of surgery (P < .001 for both).
  • No significant difference was found in the reporting of surgical risks (1.7 for both). Readability scores were comparable between LLM chatbots (12.9) and surgeons (15.7) (P = .10).

Conclusions:

  • LLM-based chatbots show promise in improving the quality of informed consent documentation, particularly in completeness and accuracy.
  • Integrating LLMs into electronic health records could offer personalized risk information and reduce physician workload, pending HIPAA compliance.
  • Further research is needed to optimize LLM performance and ensure patient safety and understanding in clinical applications.