In this episode, Lillian Erdahl, MD, FACS, is joined by Jessica Liu, MD, MS, MPH, from the Department of Surgery, Harbor UCLA Medical Center. They discuss Dr Liu’s recent article, “Identifying Diagnostic Gaps and Mitigation Strategies for Older Adult Emergency General Surgery Patients: A Scoping Review,” in which the authors identified the current diagnostic issues, clinical tools, and clinician feedback strategies in the older adult emergency general surgery (EGS) setting. While challenges unique to older adults exist, variability in the use of tools to improve identification of older adult conditions in EGS and gaps in feedback to improve diagnosis remain. Disclosure Information: Drs Erdahl and Liu have nothing to disclose. To earn 0.25 AMA PRA Category 1 Credits™ for this episode of the JACS Operative Word Podcast, click here to register for the course and complete the evaluation. Listeners can earn CME credit for this podcast for up to 2 years after the original air date. Liu, Jessica K MD, MS, MPH; Peters, Xane D MD, MS; Remer, Sarah L MD; Beestrum, Molly MLIS; Cooper, Zara MD, FACS, MPH; Russell, Marcia M MD, FACS; Hall, Bruce L MD, FACS, PhD; Ko, Clifford Y MD, FACS, MSHS, MS. Identifying Diagnostic Gaps and Mitigation Strategies for Older Adult Emergency General Surgery Patients: A Scoping Review. Journal of the American College of Surgeons 241(5):p 904-916, November 2025. | DOI: 10.1097/XCS.0000000000001480 Learn more about the Journal of the American College of Surgeons, a monthly peer-reviewed journal publishing original contributions on all aspects of surgery, including scientific articles, collective reviews, experimental investigations, and more. #JACSOperativeWord Copyright © 2026 by the American College of Surgeons (ACS). All rights reserved. The contents of these materials may be cited in academic publications but otherwise may not be reproduced, disseminated, or transmitted in any form by any means without the express written permission of ACS. These materials may not be resold nor used to create revenue-generating content by any entity other than the ACS without the express written permission of the ACS. The contents of these materials are strictly prohibited from being uploaded, shared, or incorporated in any third-party applications, platforms, software, or websites without prior written authorization from the ACS. This restriction explicitly includes, but is not limited to, the integration of ACS content into tools leveraging artificial intelligence (AI), machine learning, large language models, or generative AI technologies and infrastructures.
