AI and Genomic Surveillance Combine to Detect Infectious Disease Outbreaks
By coupling machine learning with whole genome sequencing, University of Pittsburgh School of Medicine and Carnegie Mellon University scientists said they improved the ability to detect infectious disease outbreaks within a hospital setting, compared with traditional methods, according to a news release from UPMC.
The results, published Clinical Infectious Diseases, indicate a way for health systems to identify and then stop hospital-based infectious disease outbreaks in their tracks, cutting costs and saving lives.
“The current method used by hospitals to find and stop infectious disease transmission among patients is antiquated. These practices haven’t changed significantly in over a century,” said Lee Harrison, MD, Professor of infectious diseases at Pitt’s School of Medicine and epidemiology at Pitt’s Graduate School of Public Health. “Our process detects important outbreaks that would otherwise fly under the radar of traditional infection prevention monitoring.”
The Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT) couples the recent development of affordable genomic sequencing with computer algorithms connected to the vast trove of data in electronic health records. When the sequencing detects that any two or more patients in a hospital have near-identical strains of an infection, machine learning quickly mines those patients’ electronic health records for commonalities — whether that be close proximity of hospital beds, a procedure using the same equipment or a shared healthcare provider — alerting infection preventionists to investigate and halt further transmission.
Ordinarily, this process requires clinicians to notice that two or more patients have a similar infection and alert their infection prevention team, which can then review patient records to attempt to find how the infection was transmitted.
From November 2016 to November 2018, UPMC Presbyterian Hospital ran EDS-HAT with a six-month lag for a few select infectious pathogens often associated with healthcare-acquired infections nationwide, while continuing with real-time, traditional infection prevention methods. The team then investigated how well EDS-HAT performed.
EDS-HAT detected 99 clusters of similar infections in that two-year period and identified at least one potential transmission route in 65.7% of those clusters. During the same period, infection prevention used whole genome sequencing to aid in the investigation of 15 suspected outbreaks, two of which revealed genetically related infections.