Cleveland, OH,
13:45 PM

MetroHealth Improves Sepsis Outcomes Using Epic AI

Emergency room patients who were flagged by an artificial intelligence algorithm for possibly having sepsis received antibiotics sooner and had better outcomes, according to a peer-reviewed study conducted by MetroHealth physicians and published in The Journal of Critical Care Medicine.

“We showed that when providers had access to the early warning system, patients had better sepsis-related outcomes,” said Yasir Tarabichi, MD, MSCR, the study’s principal investigator. “These patients got their antibiotics faster and had on average more days ‘alive and out of hospital’ than the group that had usual care. Taken together, the increase in survival rates and reduction in hospital stay improved with the implementation of the early warning system.” Dr. Tarabichi is also an assistant professor of Medicine at the Case Western Reserve University School of Medicine.

The MetroHealth System is a safety-net health care system based in Cleveland. Its emergency department serves a large and diverse population and has approximately 100,000 visits annually.

Over five months in 2019, the study’s authors tracked nearly 600 patients who came into the emergency department. MetroHealth implemented an electronic health record-embedded, early warning system for sepsis.

Patients 18 and older presenting to the ED were randomized to standard care for sepsis versus the pathway augmented by the early warning system.

The early warning system alerted both the physicians and pharmacists. This resulted in the patient who was flagged receiving antibiotics significantly faster than those patients whose alert was hidden, according to the study.

Collectively, those who received early antibiotics were measured to have more days alive and out of the hospital more than those in the standard care group.

“This study adds to the recent national discourse about sepsis early warning systems,” Dr. Tarabichi said. “Recent studies assessed how that score worked in isolation, which is not reflective of how it would actually be used in the real-world. We envisioned the early warning system’s role as supportive to our healthcare team’s response to sepsis. Most importantly, we assessed the utility of the tool with the highest quality approach - a randomized controlled study. In fact, our work stands out as the first published randomized controlled evaluation of a model-based early warning system in the emergency room setting.”

MetroHealth Senior Vice President Brook Watts, MD, MS, said the study demonstrates that from an institutional level, MetroHealth is committed to working collaboratively to try new approaches to improve outcomes from patients. Dr. Watts is also a professor of Medicine at the CWRU School of Medicine.

“We rigorously validate and implement new tools that can help our patients,” said Watts, who also was an author of the study. “This was an integrated team-based response to sepsis, with augmentation by artificial intelligence. It demonstrates our focus on quality improvement. We have great providers and information service experts willing and interested in leveraging new technology to improve patient care.”

The study was written by: Tarabichi; Aurelia Cheng, MD; David Bar-Shain, MD; Brian M. McCrate, PharmD, BCPS, BCCCP; Lewis H. Reese, PharmD, BCPS; Charles Emerman, MD; Jonathan Siff, MD, MBA; Christine Wang; David C. Kaelber, MD, PhD, MPH; Watts, and Michelle T. Hecker, MD.

The full study can be read here:

About The MetroHealth System

Founded in 1837, MetroHealth is leading the way to a healthier you and a healthier community through service, teaching, discovery, and teamwork. Cuyahoga County’s public, safety-net hospital system, MetroHealth meets people where they are, providing care through five hospitals, four emergency departments and more than 20 health centers. Each day, our nearly 9,000 employees focus on providing our community with equitable healthcare — through patient-focused research, access to care, and support services — that seeks to eradicate health disparities rooted in systematic barriers. For more information, visit