University of Pittsburgh researchers have developed an Enhanced Detection System for Healthcare Associated Transmission (EDS-HAT), combining whole genome sequencing (WGS) with machine learning and electronic health record (EHR) analysis to detect and predict hospital outbreaks. Traditional methods of outbreak detection are slow and often miss key transmission events, leading to avoidable infections and deaths. EDS-HAT addresses this challenge by using WGS of bacterial pathogens to identify outbreaks, while employing machine learning to analyze EHR data and identify transmission routes, significantly improving the detection and prevention of healthcare-associated infections (HAIs). This innovative system could lead to reduced infection rates, lower mortality, and decreased healthcare costs.
Description
EDS-HAT integrates whole genome sequencing and machine learning to enhance outbreak detection in hospitals. By using a single nucleotide polymorphism (SNP)-based analysis and advanced genomic techniques, EDS-HAT detects outbreak clusters by analyzing the genetic diversity within pathogen strains. The system uses publicly available bioinformatics tools integrated into a modular workflow that processes raw genome sequencing data, verifies quality, and identifies potential outbreaks by clustering genome sequences with species-specific SNP cutoffs. Additionally, EDS-HAT incorporates EHR data mining to pinpoint the transmission route, thereby enabling targeted interventions.
Applications
• Serves as a real time quality improvement tool for patient safety
• Early detection of hospital-based outbreaks
• Infection prevention and control
• Reduction of healthcare-associated infections and related mortality
• Data-driven analysis of pathogen transmission routes
Advantages
EDS-HAT offers several key advantages over traditional outbreak detection methods. It utilizes WGS to provide precise identification of outbreak strains and incorporates advanced bioinformatics tools for comprehensive genomic analysis. This system is capable of distinguishing outbreak strains from non-outbreak strains with high discriminatory power, enhancing the ability to prevent infections and reduce mortality rates. By integrating machine learning with EHR data, EDS-HAT also identifies transmission routes that would be missed by conventional approaches, leading to more effective infection control strategies. The system also detects many outbreaks that are missed entirely by traditional infection prevention methods.
Invention Readiness
The SNP-based analysis approach used in EDS-HAT is fully developed and has been validated in real time in the hospital, demonstrating its effectiveness in detecting and preventing healthcare outbreaks. Thousands of bacterial isolates have been analyzed using this system, leading to the identification of several high-impact outbreaks.
IP Status
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