Case study · Assisted living & memory care

Teton at Aquinas

How Aquinas Senior Living made Heritage Springs Montoursville the first US community to deploy anonymized AI monitoring at the point of care, and what 5 months of before-and-after data revealed.

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The short version

A pioneering deployment, measured honestly.

Aquinas Senior Living partnered with Teton to deploy anonymized computer-vision monitoring at Heritage Springs Montoursville, the first US community to install the platform. Aquinas committed to a novel AI system in a live operating environment well before Teton was a known provider in the US market.

Ceiling-mounted sensors process resident movement, position and behavior on-device, without capturing identifiable imagery, preserving dignity while providing 24/7 passive observation across 57 resident rooms. The system surfaces real-time fall alerts, behavioral analytics, and proactive health signals, and includes Samwise, a generative AI assistant staff query in natural language.

After a stabilization period, the program ran a structured 1-month baseline followed by a 4-month active-use phase, enabling clean before-and-after measurement against the same residents and staff.

Operator

Aquinas Senior Living

Community

Heritage Springs Montoursville

Setting

Personal & memory care

Rooms observed

57 resident rooms

Measurement

5-month before/after

90%

Faster emergency reaction time

84%

Less time on the floor after a fall

89%

Of falls answered under 5 minutes

3hrs

App engagement / resident / day

The deployment

A novel system, switched on in a live care environment.

Being first in the US required Aquinas to commit to the technology before any peer community had validated it domestically. That openness is itself a creative act in a sector where AI adoption typically follows rather than leads.

Heritage Springs Montoursville had no domestic playbook. Workflow integration, alert configuration and staff training were developed in partnership and iterated in real operating conditions. The team did not wait for someone else to prove the playbook; they helped write it.

01

Real-time alerts

Falls and other risk events surface to staff devices the moment they happen, with in-room context before entering.

02

Behavioral analytics

Respiration, sleep and bathroom-visit patterns flag emerging conditions before symptoms are obvious.

03

Samwise, the AI assistant

A generative assistant staff query in natural language, surfacing insights they might not think to ask for.

57

24/7 · On-device

resident rooms under continuous, anonymized observation. Vision data is processed locally and converted to anonymized representations before it ever leaves the room.

Emergency response

When help arrives in minutes, recovery is safer.

A caregiver arriving within minutes can assess for pain or injury before any movement, where getting up unaided risks a secondary fall. Self-recovery has dropped to under a third of falls.

90%

faster emergency reaction time after a fall

84%

less time on the floor before assistance arrives

89%

of falls now answered in under five minutes

<3min

average time to a caregiver reaching the resident

Prompt arrival also sharpens incident review: staff observe the undisturbed scene and pair it with the anonymized clip for a more reliable account than an after-the-fact reconstruction, turning every event into a learning artifact.

Teton gives our team insight into changes in our residents’ health, sleep, and fall risk, especially in memory care, where residents cannot express those changes themselves.

Emily Anthony

Personal Care Home Administrator, Aquinas Senior Living

AI adoption

A daily decision-support tool, not a background alarm.

Most AI deployments in long-term care are framed around a single output: a fall alert. Heritage Springs is using the same underlying AI more broadly. Staff query Samwise in natural language rather than navigating dashboards, and sustained usage shows the AI is being used as a daily tool by frontline caregivers.

Engagement changed the texture of care itself: visits got longer and more purposeful, not shorter.

3 hrs

active app engagement per resident, per day

+34%

average visit duration during the use phase

+17%

total staff time per resident

lower staff turnover reported by Aquinas

Fall rate & review

Fewer falls, and far fewer unnecessary ER trips.

−9%

reduction in fall rate during the use phase, controlling for a single frequent-faller outlier

−40pp

reduction in ER send-out rate. Anonymized clips let staff see a controlled, non-injurious fall instead of sending out by default

The same clips surface root causes such as environmental hazards, footwear and gait changes, and serve as training artifacts to prevent the next fall.

Proactive health · Example early-warning signals

Respiration

The early infection.

Respiration tracking flagged potential concerns earlier during flu season, enabling closer monitoring of at-risk residents.

Sleep

The wellbeing barometer.

Too little sleep can signal agitation or cognitive change; too much can point to depression or other underlying conditions.

Bathroom visits

The quiet UTI.

A rise in frequency can be an early indicator of a UTI or other issue warranting further evaluation.

5

Wellness score

Five sub-scores combine into one Wellness Score, an at-a-glance read on each resident. By surfacing trends rather than isolated events, it helps the team notice when something is shifting, often before it escalates or a resident can put it into words.

Sleep durationSleep regularityRespiration rateWalking speedStationarity

Our approach & what’s next

No playbook, so the team helped write it.

Close collaboration, on-site from day one.

Being first meant no domestic reference existed, so Teton worked on-premise alongside Aquinas staff, tuning alert thresholds, escalation paths and training cadence to the community over the first months of use. That hands-on partnership is how adoption was earned: the AI became a tool that enhanced caregivers’ work rather than another system competing for attention.

Measuring honestly, even around an outlier.

A single frequent-faller disproportionately influenced the underlying picture. Rather than report the rosier number, Aquinas and Teton controlled for the outlier together and reached a shared view of true performance, the rigor that underwrote portfolio-wide expansion.

A second wave of AI

Choosing to be first again.

The success at Montoursville led Aquinas to expand the platform across its entire portfolio, where it has become part of standard care. Aquinas will also be the first operator to deploy Teton’s new AI-powered e-call system, pairing active call-button infrastructure with the passive monitoring already in place, with both signals arriving on one staff device through the same prioritization logic.

  • Expanded portfolio-wide
  • AI-powered e-call pilot
  • Passive + active, one device
  • First operator to deploy

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