Machine Learning Enables Infectious Disease Tracking

Machine learning and whole-genome sequencing can improve the detection of infectious disease outbreaks within hospital settings. In a report published in the journal Clinical Infectious Diseases, researchers point to a new 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,” Lee Harrison, MD, senior author and professor of infectious diseases.

“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) combines genomic sequencing and machine learning connected to EHR data. When the sequencing identifies two or more patients in a hospital with identical strains of infection, machine learning quickly mines those patients’ EHRs for commonalities.

This process needs clinicians to notice that two or more patients shave a similar infection and alert their infection prevention team.

“This is an incredibly labor-intensive process that is often dependent upon busy health care workers noticing a shared infection between patients to begin with,” said lead author Alexander Sundermann, MPH, CIC, FAPIC, a clinical research coordinator and doctoral candidate at Pitt Public Health.

Advertisement

“That might work if patients are in the same unit of a hospital, but if those patients are in different units with different health care teams and the only shared link was a visit to a procedure room, the chances of that outbreak being detected before other patients are infected falls dramatically.”

UPMC Presbyterian Hospital ran EDS-HAT with a six-month lag for a few pathogens linked to healthcare-acquired infections nationwide while also maintaining real-time, traditional infection prevention methods. The team analyzed how will EDS-HAT performed.

Advertisement

The report concluded that EDS-HAT detected 99 clusters of similar infections in the two years and found at least one potential transmission route in 65.7 percent of the clusters. At the same time, infection prevention used whole-genome sequencing to assist in the investigation of 15 suspected outbreaks.

If EDS-HAT was running in real-time, researchers analyzed that 63 transmissions of infectious disease from one patient to another could have been prevented. Also, the technology could have saved around $692,000.

Researchers plan to introduce EDS-HAT in real-time at UMPC Presbyterian Hospital to improve future infection prevention and control programs. According to researchers, the original EDS-HAT will soon expand to include sequencing for respiratory viruses, including COVID-19.



Source-Medindia