When a resident falls, every second counts. Falls in assisted living facilities and hospitals pose significant patient safety challenges. Each year, about 1 in four residents face falls, and about $50 billion is spent on medical costs each year related to older adult falls.
Artificial intelligence (AI)-driven hybrid sensing technologies like AUGi marks a promising frontier in addressing this critical issue. It offers advanced monitoring as well as timely alerts and data at the point of care and in real-time. Care teams are able to monitor multiple high-acuity residents anywhere, anytime via the web-enabled platform that provides de-identified live view, patient/resident status, and automated safety alerts, on-the-go.
In a recent research study, published in the Journal of Informatics NursingⓇ, AUGi was shown to achieve a sustained 38% reduction in patient falls in a high acuity medical surgical unit.
Traditional fall prevention methods often rely on periodic check-ins and manual assessments, which can be reactive and may not capture subtle changes in patient or resident behavior. As the study noted, current fall prevention methods such as bed or personal alarms or signage have had “limited success”.
AI-driven hybrid sensing technology, on the other hand, employs a multifaceted approach to real-time monitoring, combining sensors, cameras, and web-enabled applications to create a comprehensive safety net.
Here are 3 ways in which this innovative technology is revolutionizing fall prevention in senior care settings, providing a more proactive and personalized approach to care.
Real-Time Monitoring & Early Detection
The key strength of AI-driven hybrid sensing technology lies in its ability to continuously monitor patients and residents at all times.
The study noted how falls in a medical surgical unit is often preceded by a transfer event (unassisted bed to chair move for instance) and anticipated physiologic is the most common fall event category, where falls are associated for those with risk factors such as abnormal gait, high-risk medications, and altered mental status. The ability to anticipate a patient’s behavior before the transfer event could therefore go a long way in preventing falls, suggests the study.
By continuously analyzing sensor data with AI, the system can detect behavior that may indicate an increased risk of falling. For instance, AUGi uses various computer vision techniques such as pose estimation, motion detection, and object recognition to quantify resident and staff movement within the room.
If a high fall risk resident is transferring from the bed or chair, the AUGi AI will detect this event and send real-time notifications to staff via the mobile app. This real-time monitoring enables a more proactive response, allowing care teams to intervene and prevent a fall before it happens.
Personalized Intervention
One size does not fit all in healthcare, and this holds true for fall prevention and intervention strategies.
AI-driven hybrid sensing technology like AUGi excels in personalizing interventions based on the individual resident and the caregiver workflow. It has the ability to identify the fall risk level associated with a resident and send AI-alerts to care teams enabling quicker intervention.
Care team members can view de-identified live images of that specific room at the time of the event, providing visual context for responding. Alerts are configured to follow an escalation path, so the right staff members are notified at the right times. Through the mobile or web applications, AUGi allows staff to generate audible messages to the resident in the room to let them know help is on the way or to provide instruction.
Event Review & Proactive Decision-Making
Beyond real-time monitoring and intervention, AI-driven hybrid sensing technology offers powerful event review and analysis capabilities. Care teams can view an on-demand replay of any fall event or time frame including staff event and notification data, to provide insight on the event. The de-identified playback enables context to patient behavior during the event, to better inform plans of care for the resident post-event such as test ordering or trips to the hospital. The data also offers insights for staff training and reinforcement to prevent adverse events in the future.
By processing and analyzing vast amounts of data, the system can also identify patterns and trends in resident behavior, shedding light on potential risk factors for falls. The advanced analytics provide proactive risk indicators that recognize a decline in a resident’s activity levels or an increase in nighttime wanderings, which can prompt healthcare providers to perform a targeted intervention before becoming an adverse event. By addressing the unique needs of each resident, the technology enhances the overall safety of the facility and resident experience.
AI-driven hybrid sensing technology is ushering in a new era of fall prevention and intervention in assisted living facilities and hospitals, and significantly improving staff efficiency and safety protocol and rounding compliance.
The proactive and personalized nature of this technology not only mitigates fall risks but also contributes to a more efficient and responsive healthcare system.