Intelligent edge devices promise to improve both the patient and the staff experience in the hospital. Skilled nursing staff struggle with the traditional alarm-based monitoring environments. Medical devices and monitors generate a significant amount of noise and activity that can both be reduced or eliminated by more intelligent solutions. The American Association of Critical Care Nurses defines alarm fatigue as a sensory overload that occurs when clinicians are exposed to an excessive number of alarms, which can result in desensitization to alarm sounds and an increased rate of missed alarms. This is especially concerning when we consider that clinical research has suggested that a majority of alarms given off by medical devices are false. This has resulted in alarm fatigue being recognized as an increasingly critical safety issue in clinical care practice, and the Emergency Care Research Institute listed “Alarm, Alert, and Notification Overload” as one of its Top 10 Health Technology Hazards for 2020.
Clinical alarms may be one of the best opportunities to couple AI, IoMT, and mobile to improve clinical outcomes and the patient experience. New advances in on device AI and ML can offer more intelligent and directed notifications to skilled nursing staffs and care monitoring centers that reduce or eliminate the need for incessant, audible alarms in most patient care settings. Advances in “tiny ML” are being driven forward by opportunities for inexpensive, low power edge devices to create significant new opportunities to displace expensive, inhouse IT and communications platforms in applying intelligent processing at the patient bedside.
Intelligent devices will create a more nuanced way to recognize faults versus actual patient alarms. Problems with sensors themselves versus actual patient health changes generate a significant number of audible alerts, and result in a general reduction in responder urgency as they are often dismissed as noncritical background noise. The use of more intelligent fault detection, coupled with intelligent routing of alarms based on clinician proximity and availability to a mobile device could dramatically reduce the ambient noise that is disrupting patient care on a widespread basis.
More intelligent, low power devices making use of tinyML, and edge services will allow more patient monitoring (and more data) to be captured without significant increases in IT infrastructure, and ambient noise from alerts. This will increase patient safety, while reducing clinician workloads to monitor patient status. These devices will become increasingly more important as we deliver more care in remote (home) settings, and we provide increasing levels of support for “hospital at home”, and aging in place. Expect an explosion in small, low power, mobile monitoring technologies as this market takes off and clinicians begin to embrace the value of low cost, intelligent monitoring solutions.