The need for reliable fall prevention in long-term care has sprouted many solutions, from physical devices like bed rails, panic buttons, and pressure pads, to entire systems trying to create comprehensive monitoring. As technology has developed, artificial intelligence has seen more and more applications in the healthcare world. Of the fall prevention systems on the market using AI, all need some kind of data stream to function, and many rely on camera footage or infrared sensor imaging to successfully monitor residents. What systems perform the best? Let’s find out.
Privacy Concerns from Residents and Families
The double-edged sword of camera monitoring is that the camera is always watching. Humans have an innate need for privacy, and the ultimate goal of any long-term care community is the safety and comfort of its residents. Cameras, even to increase safety, can feel like a loss of privacy and dignity because at any time, and often all the time, an unknown person is watching.
Sensors provide a strong alternative to camera recording. Infrared sensors never retain a photo-like image, instead taking in scans that are largely indecipherable to humans. This means while the AI can read data from the sensors and determine if a high-fall-risk resident is attempting to get out of bed, for example, there will never be a backlog of camera footage.
Data Storage, Security, and Data Loss
Video is notorious for consuming large memory banks to be processed, edited, and saved. Even if each fall is clipped, monitoring entire floors of residents at once can become a strain on IT infrastructure. And because the recordings are considered medical information, high-level security and encryption will always be necessary for storing this data.
Sensor data, however, only needs to be shared with the AI that interprets it. Once the AI has determined what it needs from the data, it is no longer needed and can be deleted, without requiring large memory backups outside the system. This also helps ensure that resident monitoring is secure and private, never risking a HIPAA violation.
Passive Monitoring vs. Proactive Monitoring
A camera alone cannot stop a fall from happening, and even cameras assisted by virtual sitters or AI programs won't stop a fall in the moment. Artificial intelligence trained to identify falls in camera footage need to see the fall happen, before flagging the event as a fall. This means their primary goal is detection, not prevention.
Sensors allow an AI to act faster, and close the gap between data input and alert sent. By improving sensor sensitivity and accuracy, AI can also become more precise, ensuring it doesn’t set off a false alarm for normal human movement. Because of this precision, AI can become truly proactive—stopping falls before they happen.
A Best-In-Class Solution
VirtuSense Technologies™ has developed an innovative AI system that utilizes infrared sensors and a highly trained AI unlike any solution on the market. The artificial intelligence detects a resident’s intent to stand and calls staff into the room 30-65 seconds before a fall could occur. VSTAlert™ has been proven to reduce alarm fatigue, maintain resident privacy, and integrate simply into existing technology. VSTAlert leverages today’s cutting-edge technology to provide the #1 fall prevention solution for long-term care.
