Hospitals have spent decades strengthening infection prevention programs, yet hospital-acquired infections remain one of healthcare’s most persistent challenges, affecting hundreds of thousands of patients annually and imposing billions of dollars in healthcare costs in the United States, according to the Centers for Disease Control and Prevention. ¹
A key reason is visibility. Despite dedicated Infection Prevention teams and established surveillance systems, most bacterial transmission occurring inside hospitals remains invisible.⁴ Transmission often becomes apparent only after infections emerge – when intervention is more complex, length of stay increases, and costs and quality risk escalate.
Traditional infection surveillance relies on timing, location, and organism type – and misses most transmission events. The issue isn’t effort; transmission is largely undetectable with these methods. Sequencing reveals more than tenfold the transmission detected by traditional approaches.² In a recent Clinical Infectious Diseases study, only 3.8% of transmission clusters were identified through routine surveillance prior to sequencing.⁴
Infection prevention teams possess deep expertise. What they have lacked is real-time, low-cost, automated sequencing capability needed to detect transmission as it begins.
NGD Infection Prevention (NGD IP) was built to make transmission detection operational in a low cost and practical way inside hospitals. The NGD IP system combines automated sample preparation and sequencing with analytics designed for infection prevention workflows, enabling hospitals to detect transmission without specialized sequencing or bioinformatics expertise.
Clinical isolates are processed and analyzed automatically by the system, allowing infection prevention teams to identify transmission clusters and map how pathogens are spreading across hospital units and patient populations. Actionable transmission insights are delivered within approximately 24 hours of isolate readiness.
By automating laboratory workflows and data analysis, the system enables hospitals to implement transmission detection in a cost-effective and operationally practical way.
Health systems applying sequencing to detect transmission are already demonstrating measurable clinical and operational impact. In one multi-year hospital study, researchers sequenced 3,921 clinical isolates and identified 172 outbreak clusters, most of which had not been detected through routine infection prevention surveillance. ⁵ Interventions guided by sequencing data halted transmission in 95.6% of outbreaks, preventing an estimated 62 infections and 5 deaths while generating a very conservatively calculated 3.2× return on investment. ⁵ New automation has brought costs lower, so return ratios are now even higher.
Sequencing studies show three consistent patterns. Most transmission goes undetected; many suspected outbreaks are not genetically linked. And confirmed outbreaks are often identified only after retrospective sequencing – long after transmission has occurred. ³
This shift – from confirming suspected transmission to detecting transmission as it occurs and before it spreads – provides infection prevention teams with the visibility needed to intervene earlier and stop outbreaks at their inception.
As healthcare systems face increasing pressure to reduce HAIs and improve patient safety, detecting transmission using sequencing – now practical, easy to use, and available at low cost – is becoming a critical capability for infection prevention.
NGD Infection Prevention is engaging with health systems, quality leaders, and infection prevention teams to advance adoption of sequencing-based transmission detection and support the next generation of hospital infection prevention.
Learn more: ngdinfectionprevention.com
References (For Publication)
1. Centers for Disease Control and Prevention. Healthcare-Associated Infections (HAIs) Data and Statistics. CDC. Accessed 2026.
2. Hayden MK et al. Genome sequencing for prevention of healthcare-associated infections. Nat Rev Microbiol. 2025.
3. Coll F et al. Longitudinal genomic surveillance of MRSA in the UK reveals transmission patterns in hospitals and the community. Sci Transl Med. 2017.
4. Sundermann AJ et al. Whole-Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection. Clin Infect Dis. 2022.
5. Sundermann AJ et al. Real-Time Genomic Surveillance for Enhanced Healthcare Outbreak Detection and Control. Clin Infect Dis. 2025.
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