A major healthcare network serving children with intellectual disabilities and autism experienced a steep post-COVID decline in caregiver quality.
Despite trying bonuses, training programs, gift cards, and pay increases over multiple years, the same problems persisted: caregivers were disengaged, sometimes neglecting patients entirely. Leadership needed a fundamentally different approach to identifying and reinforcing exceptional care, one that moved beyond punishment and surveillance toward positive, incentive-based behavior change.
The AI Lab built a computer vision system that monitors existing campus camera infrastructure to identify exceptional caregiver behavior in real time, feeding a positive incentive-based pay model where caregivers earn more for demonstrably great care, verified by human reviewers for compliance.
Staff and long-tenured employees (some with 20–30 years on campus) report a palpable shift in the quality of daily care. Where caregivers were previously disengaged or distracted, the campus now has a measurable sense of attentiveness and engagement, a culture shift that had eluded the organization for years despite conventional HR interventions.
The computer vision system achieves 99% accuracy in identifying positive caregiver interactions, with a human-in-the-loop review process for ambiguous edge cases. This precision level was not achievable with earlier multimodal models and required two and a half years of iteration to reach, enabling the incentive pay system to function reliably and fairly.
The organization's north-star metric is a measurable reduction in adverse incidents, outbursts, escape attempts, and other patient safety events, which correlate directly with caregiver engagement. While the rollout is still in early stages and sufficient time-series data has not yet accumulated, behavioral indicators are positive.