At Ocuvera, we make an automated video monitoring system: a video-based monitoring system where computers monitor patient behavior instead of monitoring technicians. The Ocuvera automated video monitoring system is built around a three-dimensional (3D) video stream. Using a 3D camera provides much more information than traditional 2D cameras. If the cameras can see more, then the system can process more information and be more confident when automatically monitoring patients.
Custom algorithms and automated pattern recognition monitor for patient activity that increases their risk of falling, especially if the patient is trying to exit the bed without a nurse helping or watching them. Once the system has enough information to think that the patient is trying to exit the bed, nurses are automatically notified by live video of the patient’s behavior sent to the nurse’s station, phone, or smart watch. This allows nurses to see for themselves what happened to trigger the alert and decide how best to respond, rather than having to simply trust an alarm. Nursing staff set the system to one of four sensitivity levels to control the amount of movement necessary to trigger an alert – depending on their patient’s specific needs, some nurses might only be interested in knowing whether their patient is about to exit the bed, while some might want to be alerted if their patient moves at all. Ocuvera alerts do not sound in the patient room to avoid unnecessarily agitating patients. The system is automated only up to the point of sending the alert – the ultimate decision of when and how to best address patient needs is left up to the nurse.
Ocuvera’s algorithms are designed to predict patient attempts to exit a bed ahead of time. The algorithms underlying the Ocuvera system have been under development for 5 years and are based on over 150,000 hours of patient video through collaboration with over 20 inpatient, critical access, and rehabilitation hospitals. This data contains thousands of real bed exits from patients, including almost two thousand unattended bed exits. As Ocuvera takes patient privacy very seriously, all patient data is securely stored as non-personally identifiable depth images. Analyzing this data from real patients helps our algorithms learn what risky patient behavior looks like and be more likely to send an alert when it’s necessary.
Our algorithms are complex, but simply put; they work by identifying the bed, the patient, and what the patient is doing. In order for our algorithms to work, they must first find the bed in the patient room. They begin by finding the floor, and then decide which pixels in the scene most likely represent the bed. Once the system finds the bed, it is constantly monitored for changes in position and updates automatically so that nurses can adjust the bed as they normally would and be confident the Ocuvera system will still track their patient. The system then works on identifying which pixels represent the patient, and what position that patient is in – whether they are laying down, sitting up, facing the edge of the bed, or about to get out of bed. The system must then decide whether or not to send an alert to nurses. Here is a simplified example of how that decision is made:
Has there been movement above the bed for a certain amount of time?
If yes, is there a patient in the bed?
If yes, has the patient been sitting up for a while?
If yes, has the patient been facing the edge of the bed for a while?
If yes, has it been a while since a nurse was last in the room?
If yes, send an alert.
At every level, our technology is designed to make the nurse’s job easier so they can provide the best care for their patients. We are working closely with medical researchers at the University of Nebraska Omaha to develop an adaptive training curriculum for integration of the Ocuvera system into existing nurse workflows. This will help ensure that the system is as easy for nurses to use as possible, and that it works with existing fall risk reduction strategies rather than replaces them. Through the adaptive training curriculum, we’ll develop straightforward educational materials for nurses on how to best use the system to maximize its effectiveness in reducing patient fall risk.