Intelligent monitoring for hospital inpatients based on continuous physiologic signals
David Maslove

Transformation – Critical illness is associated with a high risk of death and disability. Novel solutions are needed to identify deteriorating patients early, in order to intervene early and improve outcomes.

Adoptability – Novel technical solutions may help identify patients at risk of clinical deterioration, based on changes in basic physiologic signals such has heart rate. Heart rate monitoring is ubiquitous in ICUs, and with the widespread use of wearable devices, is increasingly possible for hospitalized patients. Methods derived to provide early warning based on heart rate signals have the potential for rapid uptake in any hospital setting.

Outcomes – Our aim is to identify critical illness in its early stages, leading to rapid intervention and better outcomes.

Innovation – Our approach goes beyond traditional vital signs monitoring, leveraging advances in data analytics and wearable technology.

Vital signs have traditionally been used to assess clinical stability, but are measured only sporadically on most wards. What’s more, disturbances in vital signs are relatively late findings, and may appear only once organ failure is established. Novel physiologic measures such as heart rate variability have been shown to prognosticate clinical deterioration many hours in advance, and may be useful in monitoring the health of hospital inpatients. New wearable devices are capable of heart rate monitoring, and may be useful in extending the frequency of vital signs measurements in hospitals.

Using Innovation Fund resources, we are developing infrastructure to extend both the scope and depth of vital signs monitoring in hospitals. Sophisticated software tools enable high frequency telemetry recordings from our ICU to be captured and transmitted to the Center for Advanced Computing at Queen’s, where data can be analyzed in near real time. We are using this system to study how changes in heart rate variability relate to clinical events in the ICU, and to measure the accuracy of heart rate monitoring from commercially available wearable fitness trackers, with a view to deploying these throughout the inpatient wards.

Recognizing the tremendous cost of critical care, as well as its devastating consequences for patients and families, we believe the best ICU admissions are those that are avoided altogether, thanks to timely recognition and intervention. Our IF-supported project aims to use high quality biomedical big data and advanced analytics to identify deteriorating patients with enough time to ensure their rescue. The minimally invasive, cost effective tools that will be developed will be adaptable to both large and small hospitals throughout the province, leading to better outcomes from inpatient admissions.