Sarah Williams

COVID-19 adding to clinician's cognitive overload

June 26, 2020
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.

For patients with COVID-19, hospitals have connected mechanical ventilators in this way and included such vitals in their analysis to provide more meaningful decision-support information to the clinician than just an alert. Clinicians can review such holistic insights from a centralized workstation at a safe distance from patients. This tool has been able to limit unnecessary visits to the patient’s room to protect clinicians, but also reduce the usage of PPE.

Lessons from OIRD
Safely monitoring patients while reducing the alarm burden on clinicians is a challenge that is not unique to patients with COVID-19, but rather extends to many other types of complex patients. The dilemma faced during the pandemic is akin to research where our team worked with clinicians at a partner hospital to study a cohort of patients with sleep apnea, recovering from surgery and administered opioids for pain management. These patients are known to be at increased risk for opioid-induced respiratory depression (OIRD).

At the start of our research, the medical devices (capnographs and pulse oximeters) that measured patients’ pulse (HR), oxygen saturation (SpO2), respiratory rate (RR), and end-tidal carbon dioxide (ETCO2) were set at the standard, or default, alert-time thresholds. The clinical team faced as many as 427 bedside respiratory depression alarms per hour for just one patient, although the average for patients studied was 182 per hour, or 22,812 for the entire study.

Applying basic alarm filtering delay techniques was able to reduce the number by 42%, still resulting in more than 13,000 alerts. The real impact was seen with a real-time, continuous clinical surveillance strategy that leveraged advanced analytics and was able to decrease alerts to just 209—a 99% reduction. The clinical surveillance technology supporting the strategy was also able to forward those alerts to the nurse’s mobile phone instead of sounding at the bedside only. This was possible by configuring multiple alert-threshold times through a multivariate rules engine that monitored the values of HR, RR, SpO2 and ETCO2. Moreover, our research team independently verified that no actual clinical events were overlooked and several patients received Naloxone to counteract OIRD. We also learned that in every observed case of OIRD, the in-room audible alarm annunciation did not awaken the patient.

The combination of high-fidelity data with multivariate, EHR information provided a holistic and complete source of objective information on patients that was used to eliminate non-clinically actionable alerts, but also to support prediction and clinical decision making prospectively.

The ROI of reducing alarm burden
By reducing the alarm burden and wasted effort, a continuous clinical surveillance strategy supports hospitals and health systems financially recovering from the massive losses they have experienced due to the drop in elective procedures and preventive care visits. Clinicians’ experience improves—which promotes retention—and they can work more efficiently without risk to patient safety, as we showed in our OIRD research. Moreover, other research has shown that continuous surveillance techniques decreased rescue events to 1.2 from 3.4 per 1,000 patient discharges and intensive care unit (ICU) transfers to 2.9 from 5.6 per 1,000 patient days, which decreases hospital spending and can improve their financial performance under value-based care payment models.

Preventing transfers to the ICU also ensures beds are available for critically ill patients with COVID-19 as regional or state outbreaks are likely to continue through the fall, winter and beyond. By deploying continuous clinical surveillance strategy, hospitals can be better prepared for such spikes in cases while alleviating alarm burden and protecting clinicians caring for these patients and others.

About the author: Sarah Williams, RT, is Director of Product Management—Surveillance at Capsule Technologies.