Ambient intelligence-based monitoring of staff and patient activity in the intensive care unit

Caregiver workload in the ICU setting is difficult to numerically quantify. Ambient Intelligence utilises computer vision-guided neural networks to continuously monitor multiple datapoints in video feeds, has become increasingly efficient at automatically tracking various aspects of human movement.

To assess the feasibility of using Ambient Intelligence to track and quantify allpatient and caregiver activity within a bedspace over the course of an ICU admission and also to establish patient specific factors, and environmental factors such as time ofday, that might contribute to an increased workload in ICU workers.

5000 images were manually annotated and then used to train You Only LookOnce (YOLOv4), an open-source computer vision algorithm. Comparison of patientmotion and caregiver activity was then performed between these patients.

The algorithm was deployed on 14 patients comprising 1762800 framesof new, untrained data. There was a strong correlation between the number ofcaregivers in the room and the standardized movement of the patient (p < 0.0001) withmore caregivers associated with more movement. There was a significant difference incaregiver activity throughout the day (p < 0.05), HDU vs. ICU status (p < 0.05), delirious vs. non delirious patients (p < 0.05), and intubated vs. not intubated patients(p < 0.05). Caregiver activity was lowest between 0400 and 0800 (average .71 ± .026caregivers per hour) with statistically significant differences in activity compared to 0800-2400 (p < 0.05). Caregiver activity was highest between 1200 and 1600 (1.02 ± .031 caregivers per hour) with a statistically significant difference in activity comparedto activity from 1600 to 0800 (p < 0.05). The three most dominant predictors of workeractivity were patient motion (Standardized Dominance 78.6%), Mechanical Ventilation(Standardized Dominance 7.9%) and Delirium (Standardized Dominance 6.2%).

Ambient Intelligence could potentially be used to derive a single standardized metricthat could be applied to patients to illustrate their overall workload. This could be usedto predict workflow demands for better staff deployment, monitoring of caregiver workload, and potentially as a tool to predict burnout.