Health Plans & Systems

One engine.
Two ways to
deploy it.

Vasl runs a full care platform for Black, Brown, LGBTQ+, rural and Medicaid youth, inside schools, universities and community organizations: a daily check-in by text, peer community, coaching, and a warm handoff to the partner’s own licensed clinicians. 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.

Talk to us about a pilot See the technology
Vasl Bridge: a text from Vasl every day, from ED discharge to the first visit. See Vasl Bridge →
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.

Vasl Bridge

From discharge to first visit.

A text from Vasl every day, from the day a young person leaves the emergency department to their first appointment. When a reply hides distress, the partner’s clinician sees it.

See Vasl Bridge
31.2%
Only 31.2% of Medicaid youth see a clinician within 7 days of a mental health emergency visit.
Hoffmann et al., Pediatrics 2023
Where We Are

Currently in pilot-design conversations with academic medical centers and health-system innovation programs; no pilot has run yet. 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 Request the Partner Package
Related Reading
the Vasl platform
What happens after VLAP reads a reply, step by step, and who sees what.
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.
how this connects to HEDIS measures
Which behavioral health measures are most exposed to cultural and linguistic mismatch, and where engagement moves them.