Automated Tool Reduces Hospital Outbreaks Pre-Pandemic, Struggles During COVID-19

An automated, algorithm-driven surveillance, tool, impact, hospital, outbreak, significantly reduced healthcare-associated pathogen outbreaks in 82 US hospitals before the COVID-19 pandemic, according to a recent study published in NEJM Evidence. However, the tool had little impact during the pandemic itself. The research was a collaboration between Harvard Pilgrim Health Care Institute, HCA Healthcare, UCI Health, and the Centers for Disease Control and Prevention (CDC), aimed at reducing the risk of healthcare-associated infections and improving patient care.

Why this matters: The effectiveness of automated outbreak detection tools during public health emergencies has significant implications for patient safety and the overall response to infectious disease outbreaks. The effectiveness of automated outbreak detection tools during public health emergencies has significant implications for patient safety and the overall response to infectious disease outbreaks. As healthcare systems continue to contend with the challenges of preventing and responding to outbreaks, the development and refinement of these tools remain vital for saving lives and reducing the economic burden of healthcare-associated infections.

Healthcare-associated infections present significant patient safety risks and remain an industry challenge despite progress in reducing outbreaks. Joseph Perz, DrPH, MA, senior advisor for Public Health Programs in CDC's Division of Healthcare Quality Promotion, states, "These infections can present as clusters that signal potential for transmission to patients."

The research team developed an automated outbreak detection model that uses clinical laboratory and microbiology data to detect increases in the presence of 100 bacterial and fungal species potentially indicative of increased pathogen transmission. The model triggers automatic, real-time notifications for hospital staff to deploy their outbreak response and infection prevention protocols.

The trial was conducted from 2019 to 2022 in 82 hospitals within the HCA Healthcare system. Half of the hospitals implemented the automated tool, while the rest served as study controls. Researchers measured the number of additional infection cases occurring after outbreak detection and compared differences in case numbers between the baseline period (February 2017 to January 2019) and the intervention period (July 2019 to January 2022).

The analysis revealed that hospitals using the automated detection tool saw a 64% reduction in the size of potential outbreaks during the pre-pandemic period. However, upon the emergence of COVID-19, these trends shifted, and the automated tool was observed to have no overall effect on outbreak size.

The study authors postulated that during the pandemic, hospital personnel were unable to respond as effectively to automated alerts, despite the tool notifying each hospital about three possible outbreaks on average per year. The early success of the model is driving researchers to continue assessing its potential, with plans to evaluate implementation more widely across the HCA Healthcare system.

Meghan A. Baker, MD, ScD, Harvard Medical School assistant professor of population medicine at the Harvard Pilgrim Health Care Institute, said, surveillance, tool, impact, hospital, outbreak, re "Outbreaks in hospitals are often missed or detected late, after preventable infections have occurred. This study provides a practical and standardized approach to identify early transmission and halt events that could become an outbreak in hospitals."

The findings raise important questions about the challenges of implementing automated outbreak detection tools during public health emergencies like the COVID-19 pandemic. While the tool showed promising results in reducing outbreaks pre-pandemic, its effectiveness diminished during the height of the pandemic, likely a result of the strain on healthcare systems and personnel. As healthcare-associated infections continue to pose risks to patient safety, the development and refinement of automated surveillance tools remain essential.

Key Takeaways

• Automated outbreak detection tool reduced hospital pathogen outbreaks by 64% pre-pandemic.
• The tool had little impact during the COVID-19 pandemic due to healthcare system strain.
• Healthcare-associated infections remain a significant patient safety risk.
• Automated surveillance tools are vital for reducing outbreaks and improving patient care.
• Further research is needed to refine and implement these tools effectively.