Fall prevention technology for skilled nursing facilities has moved from nice-to-have to necessary. The HHS Office of Inspector General found that nursing homes failed to report 43% of falls with major injury and hospitalization among Medicare residents (OIG, 2025). Meanwhile, a new analysis found the average nursing home reported 3.86 nurse staffing hours per resident per day, against 4.99 expected for acuity (LTCCC, October 2026). Falls are under a brighter spotlight, and there are fewer hands to prevent them.
Ambient vision AI is a monitoring approach that uses in-room sensors and computer vision to recognize movements that come before a fall, such as a resident sitting up or moving to the edge of the bed. VirtuSense's VSTOne platform runs this analysis on the device itself, so no patient video leaves the room. It needs no wearables and no pressure pads.
This post explains what works, what CMS is watching, and how the main technology options compare.
Key Takeaway: Skilled nursing facilities can reduce falls without adding staff by using ambient vision AI that detects risk before a fall and alerts the nearest caregiver. Unlike wearables or pressure pads, it needs no resident cooperation and no device on the body. CMS and OIG are focusing more on falls, so the cost of inaction is rising.
The best technology detects risk early, needs no resident cooperation, and does not add to alarm volume. In a building that is already short on staff, a system that creates more noise makes the problem worse.
Most options fall into four categories:
Technology does move the numbers. A Sg2 (Vizient) analysis of more than 750 hospitals found that facilities with fall-prevention sensors saw up to 30% fewer falls (Becker's Hospital Review, 2025). That research looked at hospitals, not nursing homes. It still shows that sensing technology is a real lever on falls.
Alarm fatigue is the catch. Every extra alert pulls a nurse away from another resident. Technology that alerts earlier, and only when it matters, protects both residents and staff time.
CMS tracks falls mainly through resident assessment data, including the Falls with Major Injury (Long-Stay) quality measure, and it uses falls information in survey and oversight decisions. Falls are now a visible number tied to a facility's reputation and enforcement risk.
Two recent developments show the direction:
The practical point is simple. Underreporting is now a known issue, and accurate fall data matters more. Facilities with objective, automatic detection are better placed to understand what is actually happening in their buildings.
Ambient vision AI is the only option here that needs no resident compliance, no body contact, and no remote person watching video. The table below compares the four approaches.
| Approach | Resident must wear or touch a device? | Where data is processed | Main limitation |
|---|---|---|---|
| Ambient vision AI (VSTOne) | No | On the device in the room | Requires in-room sensor installation |
| Wearables | Yes | Varies by product | Residents may remove, refuse, or forget them; charging needed |
| Bed and chair pressure pads | Yes (contact) | Local alarm | Often alert after the resident has already moved; frequent false alarms |
| Remote virtual sitters | No | Video streamed to a remote viewer | Ongoing labor cost; video leaves the room |
Residents with dementia are a common challenge. They may not understand why they are wearing a device, and they may take it off. A system that depends on the resident doing the right thing will miss the residents who need it most.
VirtuSense's VSTOne is an ambient vision AI platform that uses LiDAR and computer vision to detect fall risk in the room and alert staff. All processing happens on the device. No patient data leaves the room, and no cloud transmission is required. For facilities concerned about privacy, this edge approach matters. See how it relates to HIPAA in Is Your Patient Monitoring AI HIPAA Compliant?
VSTOne also supports pressure injury prevention on the same platform, so facilities do not need two separate systems. We cover that in One Platform for Falls and Pressure Injuries, Not Two.
The results from acute care are strong. Leading health systems using VirtuSense have reported returns of 4x or more on their investment, along with measurable reductions in falls. These are hospital outcomes, and every care setting is different. They do show what continuous, ambient monitoring can deliver when it is deployed well.
The best fit detects risk before a fall, needs no resident cooperation, and limits false alarms. Ambient vision AI such as VirtuSense's VSTOne does this with on-device processing and no wearables or pressure pads.
Sensors in the room use computer vision to recognize movements that come before a fall, such as sitting up or moving to the edge of the bed. The system alerts staff before the resident stands. With VSTOne, processing happens on the device, so video does not leave the room.
No. Wearables depend on residents keeping them on, and pressure pads often alert only after the resident has started to move. Ambient vision AI monitors without touching the resident or requiring them to do anything.
CMS uses the Falls with Major Injury (Long-Stay) quality measure, built from resident assessment data. It has also directed state agencies to consider fall prevalence in Special Focus Facility selection. OIG has found that serious falls are underreported.
Falls in nursing homes are now a public, closely watched number, and staffing is not improving. Technology that works without adding labor or relying on resident compliance is the practical response. Ambient vision AI fits that need.
See how VSTOne works in a skilled nursing setting. Request a demo.