Episode 6: Are Sepsis Protocols Ready for AI?

Description: 

Episode 6 of The Sepsis Spectrum: Microbial Mysteries Podcast. 

How can we diagnose what we haven’t yet defined? Nicole welcomes Dr. Shamim Nemati and Dr. Gabriel Wardi to explore how artificial intelligence is reshaping sepsis detection. They dig into why existing definitions fall short, how data-driven tools can outperform traditional alerts, and where AI could take us next, from smarter antimicrobial stewardship to real-time clinical support. A conversation for anyone rethinking how we recognize and respond to sepsis.

Learning Objective: 

At the end of the session, the learner should be able to:

• Differentiate between artificial intelligence (AI), machine learning (ML), clinical decision support (CDS), and best practice alerts within the context of infectious diseases and sepsis care;
• Evaluate current research and innovations in AI/ML applications that aim to address clinical practice gaps in infection prevention, early recognition, diagnostics/pathogen identification, early management and sepsis and antimicrobial resistance;
• Analyze the practical roles of AI/ML technologies across the patient care continuum, including pathogen identification, treatment optimization, communication strategies, and clinician and patient education;
• List actionable insights and strategies related to AI/ML that clinicians and healthcare professionals can apply to enhance diagnostic accuracy, antimicrobial stewardship, and personalized care in their own practice or organization.

Target Audience: 

Nurses, advanced practice providers, physicians, emergency responders, pharmacists, medical technologists, respiratory therapists, physical/occupational therapists, infection prevention specialists, data/quality specialists, and more.

Guests:

Shamim Nemati, PhD

Associate Professor of Biomedical Informatics,

UCSD Health

Shamim Nemati’s work is focused on utilizing large-scale multimodal datasets from electronic health records and wearable sensor technology, paired with cutting-edge machine learning techniques, to improve patient experience and outcomes across the continuum of care. He obtained his Ph.D. degree in Electrical Engineering and Computer Science from MIT in 2013, followed by two years of postdoctoral fellowship at the Harvard Intelligent Probabilistic Systems group. He is currently the Director of Predictive Health Analytics at UC San Diego (UCSD) Health and an Associate Professor of Biomedical Informatics at UCSD where he leads an NIH-funded critical care informatics research group. He has published in several areas of research, including advanced signal processing and machine learning techniques, computational neuroscience/brain-machine interface, and predictive monitoring in hospitalized patients, resulting in over 100 peer-reviewed publications.

Gabriel Wardi, MD, MPH, FACEP

Associate Professor & Chief, Division of Emergency Critical Care

Department of Emergency Medicine

Division of Pulmonary, Critical Care, and Sleep Medicine

University of California, San Diego

Gabriel Wardi, MD, MPH, FACEP, is a board-certified emergency physician cross-trained in internal medicine and critical care at the University of California, San Diego (UC San Diego), where he is also an associate professor in the Department of Emergency Medicine and Division of Pulmonary, Critical Care, and Sleep Medicine. He is the founding Chief of the Division of Emergency Critical Care within the Department of Emergency Medicine

The major focus of his career has been on improving the diagnosis and outcomes of sepsis patients. He is the Medical Director of Hospital Sepsis at UC San Diego, a position he has had since 2018. In this role, he has overseen a 40% drop in sepsis mortality. He has been funded by the National Institutes of Health and the Gordon and Betty Moore Foundation to investigate novel approaches to improve care of patients with sepsis through big data and machine-learning approaches. Dr. Wardi has been selected by his peers as a "Top Doctor" in San Diego multiple times.

Dr. Wardi has over 160 peer-reviewed manuscripts, abstracts, and book chapters published focusing on care of patients with sepsis and novel approaches using AI in medicine to improve patient-centered outcomes.

CE Information:

Provider approved by the California Board of Registered Nursing, Provider Number CEP17068 for 0.6 contact hours.

Other healthcare professionals will receive 0.5 participation contact hours for this episode.

Medical Disclaimer: 

The information on or available through this site is intended for educational purposes only. Sepsis Alliance does not represent or guarantee that information on or available through this site is applicable to any specific patient’s care or treatment. The educational content on or available through this site does not constitute medical advice from a physician and is not to be used as a substitute for treatment or advice from a practicing physician or other healthcare professional. Sepsis Alliance recommends users consult their physician or healthcare professional regarding any questions about whether the information on or available through this site might apply to their individual treatment or care.