NEXT-GEN INFECTION CONTROL

14.11.24 12:07 PM By Ralph

Proactive Infection Control Through A

With respiratory diseases consistently ranking among the top 5 causes of fatality in the U.S., infection control remains a critical priority. Traditional temperature screening methods offer a quick, non-invasive, and cost-effective option, and while effective for detecting fever, they miss subtler signs of illness, allowing infections to spread undetected. However, AI-driven surveillance systems provide a proactive solution by accurately monitoring health risks in real time. This technology significantly reduces absenteeism and costly hospital-acquired infections (HAIs), enabling organizations to protect their workforce and/or patients while achieving substantial cost savings through enhanced infection prevention. 


Undetected Spreaders: The Impact and Cause

​If you're managing disease spread based on symptoms, it's too late.

The Hidden Threat: Infection Spread in Close Enviroment
​​In controlled environments such as hospitals, elder care facilities, schools, and production lines, infections can spread quickly due to close quarters and frequent interactions. Healthcare workers, for example, walk up to five miles per day, interacting with numerous patients and surfaces. 
​This constant movement, combined with confined spaces, enables pathogens to spread unnoticed. 
​Similarly, schools and production lines bring people into close contact for extended periods. In schools, students and staff share classrooms and common areas, while in production lines, workers often stand shoulder-to-shoulder. These conditions create a perfect scenario for infections to spread rapidly, underscoring the critical need for early detection methods

Timeline of infection spread among employees

This figure presents data from an extensive study conducted at a healthcare facility in northern USA. While the facility's name cannot be disclosed due to confidentiality agreements, the datasets are publicly available upon request. The figure highlights how infections propagated through the workforce over time. Each row corresponds to an individual employee, with the chart displaying regular attendance (green), AI-detected infections (yellow), and absenteeism (white). The two clusters—fall and winter—illustrate infection waves as detected by AI, showing how sickness spread across the workforce 

The Limitations of Traditional Temperature Screening: 

A rise in body temperature is one of the body's first lines of defense against infection. However, this response is often far more subtle than the clear fevers we associate with clinical illness. Traditional temperature screening relies on the 100.4°F fever threshold, which has been used for over a century to detect infections and minimize false positives. This one-size-fits-all approach overlooks the fact that body temperature is like a fingerprint— unique to each individual and influenced by factors such as age, gender, time of day, race and many others. As a result, traditional temperature screenings are designed to miss infections that don’t trigger a clear fever

This chart illustrates the normal temperature ranges for three individuals, showing how body temperature varies. It highlights the challenge of using a one-size-fits-all approach to temperature screening, as each person's temperature is influenced by individual factors.

Al-Driven Solutions for Early Infection Detection

Harnessing AI: Personalized Monitoring
Traditional temperature screening is limited by a one-size-fits-all threshold, but AI offers a groundbreaking approach that adapts to each individual. Instead of relying on a fixed fever threshold, AI generates a personalized temperature baseline for every user. Each time an individual is scanned, their temperature is compared to their personalized baseline, allowing the AI to detect subtle yet significant anomalies—often smaller than 0.1°F—that indicate potential illness.
As more data is collected from each individual, the AI algorithm improves, enhancing its ability to identify potential infections. Since physiological factors like age, gender, and race remain constant for each person, they are accounted for in creating what we call the "temperature fingerprint," a unique profile for each individual. In addition to personalized baselines, AI compensates for external factors like weather, time of day, and season.

A Success Story: AI Effectiveness in Detecting Infections

This chart shows the correlation between AI-detected illnesses and regional infection peaks for COVID and influenza types A and B. The blue and grey areas represent regional positivity rates for COVID and influenza, respectively, while the green points indicate AI’s weekly detections. The red line represents the count of fever cases, based on the traditional 100.4°F fever threshold. The alignment of AI detections with infection peaks demonstrates the system’s ability to identify illnesses well before this fever threshold is met. This underscores AI’s potential to improve infection control by catching cases earlier than traditional methods. 

Ralph

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