By Sarah Williams

Long before COVID-19, clinicians in hospitals were inundated with safety alerts and alarms from the numerous medical devices that monitor patients. The problem even had a documented condition, alarm fatigue, which is caused by the 85% to 99% of alarms per day that do not require clinical action.

All of these nuisance alerts distract clinicians and interrupt their workflows, which contribute to cognitive overload. This cognitive overload can lead to burnout, which 62% of nurses reported feeling while 44% reporting that burnout affected their work performance, even making them desensitized to alarms. As such, 19 out of 20 hospitals surveyed express concern over alarm fatigue.

COVID-19, however, has created new cognitive overload and alarm fatigue challenges. Mechanical ventilators used for the most critically ill patients with COVID-19 are typically not connected for surveillance, which requires respiratory therapists or nurses to investigate alarms in person and to don personal protective equipment (PPE). When the alarm turns out to be non-actionable, it creates inefficiencies, which are also costly and put the clinician at unnecessary risk of exposure to the virus.

Connecting ventilators, as well as all the other devices required to monitor patients with COVID-19, is only part of the solution to reduce alarm fatigue. Rather, clinicians need a data-driven, continuous clinical surveillance strategy focused on delivering only actionable alerts to reduce their unnecessary distractions and interruptions, improve their experience, and most importantly, protect patient safety.

Beyond alarm management
In the early 2000s, technology companies attempted to reduce the number of alarms that clinicians endured every day by filtering them based on time thresholds for different devices, so only sustained alarms would notify the clinician. This strategy has limited efficacy and warns the clinician of vital sign anomalies in isolation, which, in most cases, does not indicate an adverse event is occurring with the patient. For example, when a heart arrhythmia alarm sounds, is the patient experiencing a cardiac condition or are they just out of bed brushing their teeth?

Identifying a truly actionable event requires a holistic perspective examining the patient’s heart rate, blood pressure, oxygenation levels and multiple other variables. A real-time clinical surveillance strategy that analyzes data from medical devices and multiple other sources offers greater context for decision making and is far more reliable than simple filtering. Such technology can collect and aggregate retrospective data from the electronic health record (EHR), including patient demographics and lab values, and correlate it with real-time streaming device data for a more accurate, clinically actionable perspective.

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