The benefits stretch beyond sepsis management, but its use in this setting could make a meaningful difference for patients and healthcare providers.
Every day, nurses work tirelessly to ensure patient safety.
It's a challenging profession that requires coordination across care teams, sufficient knowledge of multiple, complex protocols, and unwavering compassion in an ever-changing environment.
A nursing shortage was evident long before the COVID-19 pandemic but is now of significant concern. While the pandemic tapers, nurses continue to face demanding workloads due to persistent staffing constraints. The nursing crisis undercuts patient safety, as errors linked to overworked staff and understaffing are firmly at play.
Low nurse staffing levels have been linked to poor patient outcomes, specifically in sepsis cases. Integrating technology into today’s healthcare system can help buffer the strain of the nursing shortage, with the potential to improve sepsis care–a steady problem for hospitals.
Today’s medical devices can remotely and continuously track a patient’s vitals, including body temperature, which is often a key indicator of infection. This opens the door for rapid fever identification to support early infection detection, intervention, and better patient outcomes.
The nursing shortage and sepsis care
Sepsis affects approximately 1.7 million in the U.S annually, with one-third of hospital deaths being related. Sepsis occurs when the body has an excessive immune response, typically from an infection. Comorbidities like cancer, advanced age, and obesity increase one's risk of developing this life-threatening condition.
Vital sign monitoring is another major issue when it comes to being short-staffed. Nurses primarily use vital signs to monitor patients' health and escalate care as necessary, but the heavy workload can delay vital sign collection and decrease adherence to guidelines for sepsis care.
With devices that can measure continuous vital signs, there is an opportunity to advance sepsis care and simultaneously enhance patient safety.
Early fever detection with medical wearables
According to multiple studies, each hourly delay in treatment increases the risk of sepsis-related death (Ferrer, Liu). As such, there is an active effort to identify sepsis earlier with the help of assessment scores. To calculate sepsis risk, these scores depend on vital sign metrics, which clinical staff collects on average every four hours. Considering missed or inaccurate vital sign recordings are common–even more so with the nursing crisis–the value of these scores is limited.
As fever is a frequent sign of infection, continuous temperature monitoring (CTM), also referred to as high-frequency temperature monitoring (HFTM), proves valuable for the early detection of infection.
CTM technology uses a wireless, Bluetooth-enabled device that records temperatures seconds apart and displays them on the facility's central workstation in real-time. These data are color-coded to quickly identify readings outside the normal range. The technology also allows clinical staff to set alerts, tailored to patient’s specific needs.
In contrast, standard temperature measurements typically occur every four to eight hours outside the intensive care unit. This frequency is problematic for catching a fever at its onset.
Continuous temperature monitoring eliminates these extended gaps and enables staff to obtain a patient’s temperature at any time with a simple glance at a computer. This ability to observe temperature closely without repeated trips to the bedside saves time for today’s overstressed nurses.
Multiple studies support that CTM can detect fevers several hours earlier than the standard of care (Flora, Dambrosio, Sampson). For example, in a study of cancer patients receiving hematopoietic stem cell transplant or CAR-T therapy–a group at increased risk for sepsis–CTM detected fevers roughly 18 hours earlier than periodic temperature checks in those with confirmed infections. With nearly a full day of lead time, CTM can prevent treatment delays that are often a product of inadequate staffing.
Trend prediction brings possibilities for early detection
Artificial intelligence (AI) tools are rapidly advancing for disease diagnosis and can pair with CTM devices.
As a recent example, AI-driven analysis of breathing patterns was able to detect early Parkinson's Disease. Likewise, CTM can offer the same for early sepsis detection through AI-based analytics to identify predictive temperature trends. Body temperature is tightly regulated and particularly well-suited for trend prediction as patterns are naturally present in healthy individuals.
Well-known examples of this are the daily cycle of temperature, which is lowest in the morning and highest in the late afternoon, and temperature’s distinct pattern during a women's ovulatory cycle. These predictable variations reflect normal physiology, but an illness can disrupt these patterns. Therefore, AI analysis of CTM data could help identify trends like the loss of this natural rhythmicity, increased fluctuations, or other irregularities to aid in the early detection of sepsis.
There is already research on using temperature patterns as an indicator of fever. One study used circadian modeling to predict fevers at least 3.5 hours in advance. This finding demonstrates that temperature patterns are evident long before an actionable temperature rise and highlights the benefit of more lead time for clinical staff.
But not all infections involve a fever; septic patients also present with hypothermia which correlates with worse outcomes (Peres Bota, Thomas-Ruddel). Situations involving hypothermia aren’t as attention-grabbing as fevers, presenting more of a challenge for clinical staff to detect and act promptly.
Trend prediction may be a better indicator for detecting sepsis in such cases. In support of this, some research suggests that temperature patterns could prove more useful than fever in the setting of sepsis. This observation also highlights the advantage of trend prediction in situations where individual characteristics may mask a fever.
Older patients are less likely to mount a typical fever response during infection. This is a relevant concern given that adults 65 or older are more likely to develop sepsis and are at greater risk of adverse outcomes. Thus, CTM presents pioneering capabilities, namely temperature pattern recognition, that promote early infection detection–fever or not.
With more time and technology, better care is possible
Further challenged by the nursing shortage, sepsis continues to pose a significant burden on the healthcare system. Continuous temperature monitoring technology can relieve understaffed hospitals and support the early diagnosis of life-threatening conditions including sepsis.
CTM detects fevers sooner than traditional methods, giving clinical staff advanced warning of a possible infection. Additionally, with the added advantage of AI analytics, temperature pattern identification can produce even more lead time and a greater ability to recognize sepsis than fever alone. With more time, caregivers can act sooner to obtain testing, administer life-saving therapies, or initiate closer observation.
The benefits of CTM stretch far beyond sepsis management, but its use in this setting could make a meaningful difference for patients and healthcare providers.
Ruth Phillips is vice president of medical affairs at Blue Spark Technologies.