Intake and authorization
Translate new demand and authorized care into the constraints the matching system must respect.
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ScaffoldingHealthcare
Axis Match
Venture Signal
Turning therapist availability into fulfilled care hours, not scheduling bottlenecks.
What We're Building
Building a real-time matching and routing layer that accounts for availability, credentials, location, patient needs, and authorization constraints—so providers increase utilization and access without adding coordination burden.
The Problem and Why Now
Therapy providers face chronic staffing and scheduling constraints that limit access and leave authorized hours unfulfilled. Manual coordination and mismatched availability trap both outcomes and revenue in operational gridlock.
Demand for behavioral health continues to rise while staffing gaps widen. Providers with meaningful scale now have enough operational data to support high-quality matching—if the workflow is designed for real constraints and not generic scheduling.
Our Hypothesis
If matching is constraint-aware and continuously updated, then fulfillment rises, waitlists shrink, and effective workforce capacity expands without proportional headcount growth.
What Must Be True
That intelligent matching can reliably increase fulfilled authorized hours in production environments—while reducing coordination load and maintaining clinical fit, compliance, and patient satisfaction.
Who We're Learning From
Operations leaders at multi-location therapy providers managing high monthly service hours and complex scheduling constraints.
Focus Geography
Northeast U.S.
Current Research
How should ABA providers match clinicians, schedules, credentials, and patient needs without adding coordination overhead?
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Workflow map
The recurring work where accuracy, judgment, and continuity compound.
Clinician availability
Keep real availability current across schedules, cancellations, capacity, and geography.
Clinical fit
Match each case against credentials, location, payer requirements, and patient needs.
Scheduling exceptions
Resolve changes and conflicts without restarting the coordination process by hand.
Utilization and waitlists
Turn authorized demand into delivered hours while making blocked capacity visible.
Possible starting points
Where this could begin.
Constraint-aware matching
A live matching layer that ranks viable clinician and patient combinations instead of relying on static lists.
Exception routing
A focused operating queue for the cancellations, changes, and authorization issues that prevent care from being delivered.
Capacity visibility
A shared view of authorized demand, usable clinician capacity, and the constraints keeping them apart.
Questions that decide the venture
What we still need to learn.
- 01How much authorized capacity is genuinely recoverable through better matching?
- 02Which constraints are absolute, and which can be traded off?
- 03What integration burden can providers accept?
- 04Who owns adoption and the final matching decision?
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