The Problem
The right patients are already in the system — but the process still fails to find them.
Critical evidence is buried in narrative notes
The information that matters most lives inside long, unstructured documentation - recorded differently from one clinician to the next.
Reviewing every patient is not realistic
Finding the right patients means opening charts one by one and reading evaluations manually. At real-world volume, that is not sustainable.
The backlog grows faster than the team can clear it
Each chart takes time, focus, and clinical judgment to review - creating a constant bottleneck for already stretched staff.
Identification depends too much on memory and time
Patients are surfaced only if someone recognizes the pattern, remembers what to look for, and has time to act.
Missed opportunities remain invisible
There is no reliable signal when a patient who may benefit from a next step in care is overlooked, delayed, or never surfaced at all.
The problem is not a lack of patients who may benefit.
The problem is a workflow that cannot reliably find them.
Why It Matters
What better identification makes possible.
When clinical evidence is easier to find, care teams can review more appropriate opportunities without asking clinicians to change how they document.
More patients surfaced
Care teams can find documented needs that would otherwise remain buried in notes.
Faster review
Evidence is organized before the clinician opens the chart, reducing manual search.
Better care matching
Patients can be reviewed for the pathway that fits their history, symptoms, risks, and goals.
Lower cognitive burden
Clinicians see relevant evidence, safety considerations, and next-step context in one place.
More consistent follow-through
Teams can track review status, next appointments, routing, and handoffs across locations.
The goal is not to generate more recommendations.
The goal is to make clinically appropriate opportunities easier to see, review, and act on.
How It Works
AI extracts evidence. Rules enforce safety. Clinicians decide.
OutcomesAI turns unstructured behavioral health notes into a governed review queue, while keeping clinical judgment with the care team.
Safety, Privacy & Governance
Built for secure, auditable clinical decision support.
OutcomesAI is designed to protect patient data, constrain AI outputs, preserve clinician authority, and make each recommendation explainable and auditable.
Designed for Behavioral Health
One architecture, multiple specialty pathways
The platform is built to mine clinical documentation for care opportunities across behavioral health, not just one treatment line.
Validated
TMS
Identifies treatment-resistant depression and OCD candidates from psychiatric evaluations with evidence-backed ranking.
Expandable
Spravato / Esketamine
Surfaces patients whose documented history may warrant clinician review for interventional depression pathways.
Expandable
Ketamine
Supports pathway-specific evidence extraction, contraindication review, and patient prioritization.
Expandable
Psychotherapy & PTSD
Finds documented need, engagement risk, symptom burden, and follow-up opportunities across the patient panel.
Measured Impact
More right patients reached treatment
In an anonymized multi-site interventional psychiatry practice, OutcomesAI changed the process from chance discovery to systematic identification.
Before OutcomesAI
65
of every 100 strongest candidates reached treatment.
+23
more per 100
With OutcomesAI
88
of every 100 strongest candidates reached treatment, most within 30 days.
Governed Clinical AI
Built to support professional judgment, not replace it
OutcomesAI is decision support. The system reduces cognitive burden by assembling evidence, ranking work, and flagging safety concerns, while preserving clinician control over care decisions.
Before OutcomesAI, no one could read every evaluation. Now I open my panel and see the strongest candidates, the sentence from the note behind each rating, and when the patient is next in. I decide from there.
Bring evidence-backed AI to your next care pathway
Start with the documentation you already have. No workflow changes, no new templates, and no real patient data in public demos.