Artificial Intelligence (AI) System Combatting Healthcare Associated Infections (HAI)
We often hear of patients being admitted into the hospital for an illness and coming out with a different infection which they acquired while in the hospital. This is known as a healthcare-associated infection (HAI).?A new study from the Centers for Disease Control and Prevention (CDC) shows that US hospitals saw significant increases in healthcare-associated infections in 2020-in large part due to Covid-19. Approximately 1 of every 25 hospitalized patients in the United States are impacted, meaning that nearly 650,000 US patients contract one of these infections annually.
According to the World Health Organization (WHO), these health care-associated infections are the “most frequent adverse event in health-care delivery worldwide”. WHO’s information shows that, world-wide, hundreds of millions of patients are affected by these infections each year, causing “significant mortality and financial losses for health systems.”
Most hospitals use traditional infection prevention (IP) methods for outbreak detection. However, the University of Pittsburgh Medical Center recently collaborated with researchers at Carnegie Mellon University to develop an artificial intelligence-powered system and a new way to quickly identify and prevent hospital-based disease outbreaks.
Their new system, called the Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT), combines “whole genome sequencing (WGS) surveillance and machine learning (ML) of the electronic health record (EHR) to identify undetected outbreaks and the responsible transmission routes.” This combination of tools is able to detect infectious disease outbreaks in hospitals much quicker than the traditional ways. ?EDS-HAT does not wait for someone to identify an outbreak but uses genomic sequencing surveillance to detect whether patients in a hospital have “near-identical strains of an infection.”
“This is tremendous for us in infection prevention,” said Linda Dickey, president-elect of the Association for Professionals in Infection Control and Epidemiology, who was not involved with the study. “We have for many, many years flown blind.” She said the research would provide “a significant leg up on seeing patterns and making associations.”
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In addition to improving patient safety, the study calculated that stopping the previous outbreaks would have saved the hospital as much as $692,500. According to the study, “The cost of sequencing one bacterial isolate is low ($70) relative to the high costs of treating a single, potentially-preventable infection (e.g., over $24,000 for Pseudomonas pneumonia).”
With a grant from the National Institutes of Health, the scientists’ next goal is to expand the system to incorporate sequencing of respiratory viruses, including COVID-19, influenza and RSV.
Infection Preventionist | Practice Guidance @ APIC
1 年Hello! We are looking for lecturers for Greater Los Angeles APIC - local for in person events and virtual for chapter meetings. Let us know if you have any recommendations. Our chapter is especially interested in AI as it relates to IP. Thank you!