Vista Prairie Communities needed to shift from reactive to proactive care across its senior living facilities. Staff lacked early warning systems to detect declining resident health, falls, or infection spread before they became serious — relying instead on observation and incident response after the fact.
Vista Prairie deployed CarePredict with Tempo™ wearables across 4 communities in Minnesota and Iowa. The wristband uses sensors and kinematic algorithms to continuously monitor resident activity and behavior patterns. Deep-learning models detect deviations from baseline — such as increased bathroom frequency or increased sedentary behavior — and surface insights to staff via a real-time dashboard. Fall detection uses a self-learning ML model that improves accuracy over time, and contact tracing is enabled by all staff and residents wearing the device.
Deployment resulted in an impressive reduction in UTI cases across communities through early behavioral detection and staff intervention. Fall detection accuracy improved over time as the model learned from staff-confirmed events, with per-resident sensitivity tuning. Digital contact tracing during COVID-19 outbreaks resulted in fewer cases and less community lockdown compared to conventional mitigation methods.
Have a similar implementation?
Share your customer's AI results and link it to your vendor profile.
Submit a case study →