Defining and evaluating potentially preventable ED visits in oncology patients including using early machine learning models.

Authors

null

Sonal Gandhi

Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada

Sonal Gandhi, Lauren Fleshner, Monika K. Krzyzanowska, Alex Kiss, Ivy Cheng, William Tran

Sign-In to See More Abstracts, Journal Articles, Posters, Videos and Slides and to Bookmark Your Favorite Content.

Disclaimer

This material on this page is ©2024 American Society of Clinical Oncology, all rights reserved. Licensing available upon request. For more information, please contact licensing@asco.org

Poster Details

Meeting

2023 ASCO Quality Care Symposium

Session Type

Poster Session

Session Title

Poster Session A

Track

Quality, Safety, and Implementation Science,Cost, Value, and Policy,Patient Experience,Survivorship

Sub Track

Quality Improvement Research and Implementation Science

Citation

JCO Oncol Pract 19, 2023 (suppl 11; abstr 463)

DOI

10.1200/OP.2023.19.11_suppl.463

Abstract #

463

Poster Bd #

K21

Abstract Disclosures

Similar Posters

First Author: Abbas Hassan

Poster

2021 ASCO Quality Care Symposium

Trends in diagnosis and treatment of early breast cancer (eBC) in the United States (US) during the COVID-19 era.

Trends in diagnosis and treatment of early breast cancer (eBC) in the United States (US) during the COVID-19 era.

First Author: Benjamin Ackerman

Poster

2020 ASCO Quality Care Symposium

Machine learning to predict tamoxifen adherence among U.S. commercially insured breast cancer patients.

Machine learning to predict tamoxifen adherence among U.S. commercially insured breast cancer patients.

First Author: Tyler J. O'Neill

Poster

2020 ASCO Virtual Scientific Program

Clinical utility of 18F-FDG-PET/CT in staging localized breast cancer prior to initiating preoperative systemic therapy.

Clinical utility of 18F-FDG-PET/CT in staging localized breast cancer prior to initiating preoperative systemic therapy.

First Author: Heidi Ko