VLAP for Health Systems

One engine.
Two ways to
deploy it.

Vasl runs a full care platform — peer community, coaching, and licensed therapy — for schools, universities, and community organizations. Health systems and FQHCs deploy the same intelligence a different way: VLAP as a clinical decision support layer inside your own care delivery.

VLAP runs on the transcripts your teams already generate — including ambient scribe output — and surfaces culturally interpreted risk context to your clinicians before and between sessions. No new workflow. No new documentation burden. Your clinicians, better informed.

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Deployment Model
VLAP as clinical decision support inside your existing EHR and care workflow — not a separate platform your clinicians have to log into.
Data Source
Runs on the clinical documentation your teams already generate — intake notes, progress notes, discharge summaries, and telehealth encounter notes — including ambient scribe output. No new intake, no new documentation.
Integration Path
Ingests via HL7 FHIR API or CSV/JSON under HIPAA Safe Harbor de-identification, with integration paths for major EHRs.
Built for Clinical Governance

Decision support.
Not a diagnostic device.

VLAP is clinical decision support, not a diagnostic device. Signals route only to licensed clinicians, with mandatory human review before any escalation. Every step is documented for audit.

Signals route only to licensed clinicians — never to administrators, payers, or automated systems.
Four-tier risk framing — low, moderate, elevated, acute — with mandatory human review before any escalation.
Full audit trail on every signal, every review, every escalation decision.
FHIR R4-ready, with integration paths for major EHRs.
Built for the Patients the System Misses

Trained on the language
standard NLP was never built to read.

VLAP, our clinical language model, is trained on youth-generated language — AAVE, coded phrasing, limited-English-proficiency patterns — with an SDOH-aware calibration layer (Provisional Patent VH-2026-001-PROV) that adjusts for the contexts standard NLP gets wrong.

Where We Are

Currently in structured pilot pathways with academic medical centers and health-system innovation programs. Independent validation via an IRB-approved university study is underway.

Structured Pilot Pathways IRB-Approved University Study
Ready to Talk

Let’s scope
a pilot.

Tell us about your care delivery model and data infrastructure, and we’ll show you exactly how VLAP fits — as decision support, not another system to manage.

Talk to us about a pilot See the technology
Related Reading
the platform's five care layers
From peer community to licensed therapy — where each layer sits in a member's care continuum.
VLAP's signal detection layer
How language analysis surfaces risk to a human reviewer without ever assigning a diagnosis.
VLAP's training approach
Why a model trained on standard clinical text underperforms with the members you're trying to retain.
engagement and retention data
30-day retention and symptom-change figures from pilot cohorts, benchmarked against digital health norms.
pricing for health plans and systems
Population-based pricing and what a pilot deployment typically includes.
how this connects to HEDIS measures
Which behavioral health measures are most exposed to cultural and linguistic mismatch, and where engagement moves them.