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

Karyotyping01:17

Karyotyping

70.1K
Overview
70.1K

You might also read

Related Articles

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

Sort by
Same author

Multi-scale data improves performance of machine learning model for long COVID identification.

Communications medicine·2026
Same author

Governing real-world health data as a public utility.

Science (New York, N.Y.)·2026
Same author

LinkML: an open data modeling framework.

GigaScience·2025
Same author

Development of a robust corpus for automated evaluation of online health information in Chinese using the DISCERN scale.

Journal of the American Medical Informatics Association : JAMIA·2025
Same author

Advancing the science of genomic learning healthcare systems.

Learning health systems·2025
Same author

Mondo: integrating disease terminology across communities.

Genetics·2025
Same journal

LabSage: Structural-Semantic Decoupling for Enhanced Retrieval-Augmented Generation in Clinical Laboratories.

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

Evaluating Representation Embeddings from LLMs and Time-Series Foundation Models for Wearable Accelerometer-Based Health Prediction.

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

ClinNoteAgents: An LLM Multi-Agent System for Predicting and Interpreting Heart Failure 30-Day Readmission from Clinical Notes.

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

Mapping the Storm: Linking Tornado Paths to Emergency Room Surges Through Geocoded Patient Data.

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

Multi-Modal Deep Learning-Based Model to Predict Burkitt Lymphoma Recurrence.

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

A Multi-Model LLM Consensus Framework to Identify EHR-Predictable Eligibility Criteria in NSCLC Immunotherapy Trials.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
See all related articles

Related Experiment Video

Updated: Apr 5, 2026

Using Computer Vision Libraries to Streamline Nuclei Quantification
06:25

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

764

Granular Quality Reporting for Cervical Cytology Testing.

Kavishwar B Wagholikar1, Kathy L MacLaughlin2, Christopher G Chute1

  • 1Biomedical Statistics and Informatics, Arizona State University and Health Science Research, Mayo Clinic Scottsdale.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|August 26, 2015
PubMed
Summary
This summary is machine-generated.

Quality reporting for cervical cancer prevention should include patients with atypical squamous cells of undetermined significance (ASCUS) cytology. Automated review shows higher testing adherence in ASCUS patients, highlighting care variations and the need for improved health IT.

More Related Videos

Cell Block Preparation from Cytology Specimen with Predominance of Individually Scattered Cells
08:20

Cell Block Preparation from Cytology Specimen with Predominance of Individually Scattered Cells

Published on: July 21, 2009

75.2K
Chromogenic In Situ Hybridization as a Tool for HPV-Related Head and Neck Cancer Diagnosis
06:57

Chromogenic In Situ Hybridization as a Tool for HPV-Related Head and Neck Cancer Diagnosis

Published on: June 14, 2019

11.5K

Related Experiment Videos

Last Updated: Apr 5, 2026

Using Computer Vision Libraries to Streamline Nuclei Quantification
06:25

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

764
Cell Block Preparation from Cytology Specimen with Predominance of Individually Scattered Cells
08:20

Cell Block Preparation from Cytology Specimen with Predominance of Individually Scattered Cells

Published on: July 21, 2009

75.2K
Chromogenic In Situ Hybridization as a Tool for HPV-Related Head and Neck Cancer Diagnosis
06:57

Chromogenic In Situ Hybridization as a Tool for HPV-Related Head and Neck Cancer Diagnosis

Published on: June 14, 2019

11.5K

Area of Science:

  • Gynecology
  • Public Health
  • Health Informatics

Background:

  • Current cervical cancer prevention quality reporting primarily focuses on patients with normal cervical cytology.
  • Patients with cytological abnormalities, such as atypical squamous cells of undetermined significance (ASCUS), are often excluded, despite potentially being at higher risk.
  • Complexity of surveillance guidelines and reliance on free-text data are major obstacles to granular quality reporting.

Purpose of the Study:

  • To compare cytology testing rates between patients with ASCUS and those with normal cervical cytology.
  • To assess the feasibility of automated chart review for granular quality reporting in cervical cancer screening.
  • To evaluate variations in quality of care based on cervical cytology risk stratification.

Main Methods:

  • Automated chart review was performed on a dataset of 28,101 female patients.
  • Surveillance guidelines were modeled, and information was extracted from free-text cytology reports.
  • Cytology testing adherence rates were compared between patients with ASCUS and those with normal cytology.

Main Results:

  • Patients with ASCUS cytology demonstrated significantly higher adherence rates (94.9%) compared to patients with normal cytology (90.4%).
  • Automated chart review successfully extracted relevant data from free-text reports.
  • Significant variations in quality of care were observed between high-risk (ASCUS) and average-risk (normal cytology) patient groups.

Conclusions:

  • Quality reporting for cervical cancer prevention needs to encompass patients with cytological abnormalities like ASCUS.
  • Health information technology, including automated chart review and natural language processing, can enable more granular and accurate quality reporting.
  • The findings underscore the importance of risk-stratified quality assessment in cervical cancer screening programs.