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

686
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
686

You might also read

Related Articles

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

Sort by
Same author

Assessing Financial Toxicity in Cancer: A Global Systematic Review and Meta-Analysis Using an Asset Framework.

Cancer medicine·2026
Same author

Economic Impact of a Deep Learning Algorithm for Automated Head and Neck Surgery Referral Triage.

The Laryngoscope·2026
Same author

Trends in Industry-Sponsored Research Payments to Otolaryngologist Principal Investigators.

The Laryngoscope·2026
Same author

Recurrence After Subtotal Resection of NF-2-Associated Versus Sporadic Vestibular Schwannomas: A Matched-Cohort Analysis.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery·2026
Same author

High Utilizers of MyChart Electronic Patient Portal Messaging in A Head and Neck Surgery Clinic.

The Laryngoscope·2026
Same author

Optimizing Cochlear Implant Care: A Time-Driven Activity-Based Costing (TDABC) Analysis of Audiologist and Otolaryngologist Workflow.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery·2026

Related Experiment Video

Updated: May 10, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.1K

A Novel Natural Language Processing Model for Triaging Head and Neck Patient Appointments.

Stefanie Seo1, Andy S Ding1, Syed Ameen Ahmad1

  • 1Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Otolaryngology--Head and Neck Surgery : Official Journal of American Academy of Otolaryngology-Head and Neck Surgery
|April 25, 2025
PubMed
Summary
This summary is machine-generated.

A new natural language processing (NLP) model accurately triages head and neck (H&N) cancer patients. This tool aids in predicting pathology and urgency, potentially improving patient care and reducing delays.

Keywords:
appointmentdeep learninghead and neck cancernatural language processingreferraltriaging

More Related Videos

Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
03:55

Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer

Published on: June 9, 2023

462
A Model for Perineural Invasion in Head and Neck Squamous Cell Carcinoma
08:59

A Model for Perineural Invasion in Head and Neck Squamous Cell Carcinoma

Published on: January 5, 2017

10.5K

Related Experiment Videos

Last Updated: May 10, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.1K
Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
03:55

Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer

Published on: June 9, 2023

462
A Model for Perineural Invasion in Head and Neck Squamous Cell Carcinoma
08:59

A Model for Perineural Invasion in Head and Neck Squamous Cell Carcinoma

Published on: January 5, 2017

10.5K

Area of Science:

  • Medical Informatics
  • Oncology
  • Otolaryngology

Background:

  • Inaccurate patient triage leads to delayed care and increased morbidity/mortality, especially in cancer patients.
  • Effective management of clinical capacity is crucial for optimal patient outcomes.
  • Head and neck (H&N) cancer patient triage requires precise and timely assessment.

Purpose of the Study:

  • To develop and assess a natural language processing (NLP) model for H&N patient triage.
  • To evaluate the NLP model's accuracy in categorizing pathology and predicting appointment urgency.
  • To determine the model's potential as an adjunctive tool in H&N patient workflows.

Main Methods:

  • A retrospective cohort study was conducted at an academic institution.
  • An NLP model was developed and applied to referral documents (clinic notes, imaging, pathology reports) of 83 new H&N patients.
  • The model predicted pathology type, malignancy risk, and appointment urgency, with final diagnoses serving as the gold standard.

Main Results:

  • The NLP model achieved 81.9% accuracy for pathology type and 86.8% for urgency level.
  • High sensitivity was observed for various H&N pathologies, including non-endocrine neoplasms (88.9%) and thyroid (88.9%) and parathyroid (100%) pathologies.
  • The model demonstrated strong predictive performance for appointment urgency, with a Matthews correlation coefficient of 0.698.

Conclusions:

  • The NLP model shows robust performance in predicting H&N diagnoses and urgency from referral documents.
  • This tool can assist H&N practice coordinators in screening referrals, potentially optimizing patient care pathways.
  • The model's ability to identify urgent cases may significantly improve management of H&N cancer patients.