How a Children's Care Network Flagged Top Staff at 99%
A children's care network's staff had disengaged post-COVID. Computer vision on 300 existing cameras now spots top caregiver moments at 99% — restoring pre-COVID culture.
99%
Achieved in caregiver recognition
The Challenge
A healthcare network serving children with intellectual disabilities, autism, and neglected youth watched caregiver quality collapse after COVID. Hourly caregivers — responsible for feeding, playing with, and supervising children who cannot be left alone — grew disengaged, sometimes ignoring patients for extended periods. The organization tried bonuses, training programs, gift card incentives, and pay increases. Nothing worked. With a 55-year reputation for exceptional care on the line, the CEO came searching for a fundamentally different kind of solution.
What They Built
The AI Lab partnered with leading computer vision specialists to build a positive-reinforcement system using 300 cameras already installed on campus. Rather than monitoring for violations, the AI identifies moments of exceptional caregiver behavior — at 99% accuracy — and counts them toward a weekly score. Managers review an AI-generated evidence packet and approve incentive pay for top performers. No automated payroll changes; humans remain in the loop for compliance. Early results are already visible: veteran staff report the culture on campus feels like it did before COVID. The organization is now moving from pilot to campus-wide rollout.
The AI Lab's design premise was inversion: instead of using cameras to catch problems, use them to catch excellence. Working with leading computer vision specialists, they trained a model to identify specific caregiver behaviors associated with high-quality patient interaction — engaged play, attentive feeding, responsive supervision — using the 300 cameras already installed across campus. The model runs continuously and flags positive interactions at 99% accuracy, counting each toward a weekly score per caregiver. At the end of each week, managers receive an AI-generated evidence packet for top performers, review the flagged interactions, and approve incentive pay for those who qualify. No automated payroll changes occur — humans remain in the decision loop at every stage, satisfying compliance requirements for a regulated healthcare environment. The pilot launched approximately 4–6 months into development. Early results showed veteran staff reporting that the culture on campus felt like it did before COVID. The organization is now planning a campus-wide rollout.
Infrastructure
Computer vision model (trained for positive caregiver behavior detection) • 300 existing on-campus cameras (repurposed) • Custom scoring and evidence portal for managers
Integration Points
Computer vision model connected to camera feed processing pipeline • Weekly score aggregation feeding manager review portal • Evidence packet generation linked to incentive approval workflow
Impact
Computer vision correctly identifies exceptional caregiver interactions at 99% accuracy
Veteran staff report care quality returning to pre-COVID levels, visible across the entire campus
System exiting pilot phase and entering campus-wide deployment
Implementation Complexity
Best Fit For
Healthcare and social services organizations with large hourly workforces where quality of care is difficult to measure and traditional incentive programs have failed — especially when the goal is reinforcing positive human behavior rather than automating tasks.
