Engagement drop-offs + unclear program impact
Teams can’t see why users disengage or which interventions drive outcomes.
Key problems
Claims + cost visibility gaps
Definitions vary; measurement windows aren’t consistent; impact is hard to prove.
Operational triage + throughput constraints
Queues are unmanaged; exceptions aren’t visible early; staffing decisions lack signals.
Governance + access control complexity
Privacy constraints slow analytics and AI adoption without a clear operating model.
What we deliver
Journey analytics (cohorts + funnels)
End-to-end view of onboarding → engagement → outcomes with drop-off diagnostics.
Claims + financial impact dashboards
Utilization, costs, trend drivers, and cohort comparisons with consistent definitions.
Triage analytics + exception queues
Operational dashboards that surface backlogs, bottlenecks, and priority interventions.
Privacy-by-design governance patterns
Access controls, minimization, lineage, audit logging, and approved-use boundaries.
Deliverables
Privacy-first architecture + roadmap + MVP plan
Curated models (members/patients, claims, engagement, providers/therapists if applicable)
Dashboards for ops + leadership cadence (exceptions + impact measurement)
Secure AI pattern guidance (RAG over approved sources, monitoring)
Frequently Asked Questions
How can analytics be privacy-first?
Access controls, data minimization, approved-use boundaries, lineage, and audit logging.
How do claims analytics prove impact?
By quantifying cost/utilization changes across cohorts with consistent definitions and measurement windows.
Can GenAI be used securely in healthcare?
Yes—using RAG over approved sources, strict permissions, monitoring, and no uncontrolled exposure of sensitive data.
What’s a practical MVP?
One journey domain end-to-end with a small KPI set and cadence tied to interventions.
Typical timeline?
4–10 weeks depending on claims availability and integration readiness.