› Public Healthcare · AI Platform
Predictive intelligence
for 6 million families.
Emma, the AI assistant ADL designed for HANDS AI, tells WIC caseworkers which of their 250 families need help today — before they slip through the cracks.
40%
Faster case prep
30%
Better intervention
6
Health departments
› The Brief
WIC serves 6.2 million women, infants, and children across America — delivering $1.77 in healthcare savings for every dollar spent. But the families who need it most are the ones most likely to quietly disengage.
Caseworkers managing 200–300 active cases had no systematic way to identify who needed urgent attention. HANDS AI asked ADL to design the intelligence layer that would change that.
› The Problem
"Of the 250 families in my caseload,
who needs my help most urgently today?"
The data existed across appointment histories, benefit utilization records, health screenings, and communication logs — spread across multiple disconnected systems. Nothing translated it into a clear answer. Caseworkers defaulted to whoever called, whoever had an appointment, or whoever happened to be visible. Families in genuine crisis stayed invisible until it was too late.
80%
Time on admin
Caseworkers spent most of their day on administrative tasks — leaving only fragmented moments for actual family support.
15 min
Per case review
Preparing for a single family required clicking through four disconnected systems to piece together a complete picture.
>40%
Burnout rate
Caseworker burnout exceeded 40%, driven by the frustration of not reaching struggling families before they fell out of the program.
› The Solution
Emma — from reactive
to proactive.
ADL designed Emma, an AI assistant that continuously analyzes participant data to surface the families most at risk of program disengagement — before problems escalate. Each morning, caseworkers see a prioritized queue with clear explanations for every flag.
Emma doesn't replace caseworker judgment. It gives caseworkers the intelligence to focus their limited time on the families who need them most — while the predictive model gets smarter with every interaction.
Emma · Morning Priority Queue
› Core Capabilities
Three surfaces. One clear answer.
Predictive Risk Queue
Every morning, Emma calculates a risk score for every enrolled family — based on missed appointments, benefit redemption patterns, approaching deadlines, and communication signals. Caseworkers see a prioritized queue with plain-language explanations for every flag. No intuition required.
Natural Language Search
Instead of filtering through disconnected systems, caseworkers ask questions in plain language: "Which families have children turning one next month who haven't scheduled recertification?" Emma queries across all data sources and returns ranked, context-rich results in seconds.
Supervisor Visibility
Clinic supervisors see risk distribution across all caseworkers and locations — identifying which families across the entire operation need attention, which caseworkers carry disproportionate burden, and where to allocate limited supervisory time.
› The Outcome
From firefighting to foresight.
40%
Reduction in case prep
From 15 minutes to under 1 minute per family.
30%
Better early intervention
Caseworkers shifted from reactive response to proactive outreach.
<1 min
Per case review
A complete family summary with risk factors surfaced instantly.
6
Departments deployed
Production system live across U.S. health departments.