In a new clinical study from University of California San Diego School of Medicine, researchers and physicians at UC San Diego Health found that by utilizing artificial intelligence (AI) they could develop more timely and efficient assessments of care provided to patients with severe sepsis in the emergency department.
The study results, published in the June 25, 2026 online edition of JAMA Network Open , utilized AI and large language models (LLMs) to automatically assess complex measurements of care and delivered targeted feedback for providers in the hospital.
“Medicine can learn a lot from professional athletics, where every player knows their performance statistics almost immediately, and that feedback changes how they train and perform,” said Gabriel Wardi, MD, co-corresponding author of the paper, emergency and critical care medicine physician at UC San Diego Health and chief of the Division of Critical Care in the Department of Emergency Medicine at UC San Diego School of Medicine.
“Rarely do physicians get that same type of quick, individualized feedback, even for conditions as time-sensitive as sepsis. By measuring performance in near-real time, we can turn quality reporting from a retrospective administrative exercise into something that actually helps physicians improve care.”
In partnership with the Joan & Irwin Jacobs Center for Health Innovation at UC San Diego Health, study researchers found that LLMs can perform accurate abstractions for complex quality measures, particularly in the challenging context of the Centers for Medicare & Medicaid Services (CMS) SEP-1 measure for severe sepsis and septic shock.
Traditionally, the clinical review process for SEP-1 involves a 63-step evaluation of extensive medical charts, requiring months of effort from multiple reviewers for a few patient cases.
This study found that LLMs can dramatically reduce the time and resources needed for this process by accurately scanning hundreds of patient charts and generating critical contextual insights in seconds, oftentimes while the patient is still being cared for in the hospital.
Once the charts are automatically reviewed through the LLM, a notification is sent to clinical leadership in the Emergency Department for further evaluation and then disseminated to the medical teams providing treatment to the patient with sepsis. Through this effort, physicians are given near real-time feedback on their patient case and, when necessary, provided recommendations for meeting SEP-1 guidelines.
“By using AI to quickly assess sepsis quality care measures, we are able to provide guidance to our care teams in the most teachable moment,” said Karandeep Singh, MD, study co-author, chief health artificial intelligence officer at UC San Diego Health and Joan & Irwin Jacobs Endowed Chair in Digital Health Innovation at UC San Diego School of Medicine.
“In turn, this has resulted in improved compliance with national sepsis quality measures and helps our teams consistently improve upon the care they provide to the communities we are proud to serve each day.”
According to the U.S. Centers for Disease Control and Prevention, at least 1.7 million adults in the United States develop sepsis each year, and approximately 350,000 will die from the serious blood infection.
Other key findings of the study found that LLMs can improve efficiency by correcting errors and lowering administrative costs by automating tasks, which are scalable across various health care settings.
“Using small, privacy-preserving language models allows for rapid and actionable insights distilled from large amounts of documentation in medical charts,” said Aaron Boussina, PhD, first author of the paper and affiliate faculty at the Joan & Irwin Jacobs Center for Health Innovation at UC San Diego School of Medicine. “This seamlessly embeds best practices in the care delivery process.”
Additional co-authors of the study include Michael T. McCurdy, University of Maryland; Christopher A. Longhurst, Seattle Children’s Hospital; Allison Claire, Kimberly Quintero, Sonia Jain, Chad VanDenBerg, Michael Hogarth, Amy M. Sitapati, Atul Malhotra, James Ford, Theodore Chan, Paul Ishimine, Richard Childers and Shamim Nemati, UC San Diego Health and UC San Diego.
Link to Full Study: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2850785?resultClick=3
JAMA Network Open
Randomized controlled/clinical trial
Not applicable
Medical Record Abstraction for Quality Improvement in Sepsis Care Using Artificial Intelligence
25-Jun-2026
Boussina, Nemati, and Malhotra are cofounders of and hold equity in Clairyon, a start-up that develops products related to digital health. This study was funded, in part, by a monetary award provided to Clairyon in which University of California San Diego was a sub-recipient. The terms of the arrangement have been reviewed and approved by UC San Diego in accordance with its conflicts of interest policies.