Use Case 1
Continuous Multi-Source Risk Monitoring
Trigger: Always-on across active patient population
1.Ingest session transcripts, patient messages, wearable signals, and assessment results
2.Run validated NLP against C-SSRS terminology and risk-language libraries
3.Score multi-modal risk with patient-history weighting
4.Surface signals only when fusion threshold met (no single-source alerts)
5.Maintain audit trail of every signal evaluated
✓
Crisis signals get caught from multiple data sources earlier than assessment-only approaches.
Use Case 2
Acute Crisis Escalation
Trigger: High-acuity risk signal confirmed
1.Page treating clinician immediately with full data context
2.Alert care manager in parallel for outreach within 2-hour SLA
3.Pull crisis plan from chart and surface to responders
4.Suppress all automated patient communication
5.Document escalation with timestamps for clinical record
6.Track clinician response and patient outcome
✓
High-acuity signals reach a clinician within minutes with all relevant context already assembled.
Use Case 3
Model Calibration via Clinical Feedback
Trigger: Clinician reviews and dispositions an alert
1.Capture clinician disposition (confirmed crisis, not crisis with reason, intermediate concern)
2.Feed labeled data back into model
3.Track per-clinician alert volume and disposition patterns
4.Surface alert-volume anomalies to clinical director
5.Refine fusion thresholds with continued labeled data
✓
Model gets progressively better at distinguishing crisis from non-crisis signals.
Integrations
Session Recording / Transcription
Patient Messaging Platforms
Wearable Data (Apple Health, Fitbit, Oura, Whoop)
EHR Assessments & Crisis Plans
Clinical Escalation Workflow (24/7)
C-SSRS / Validated Risk Terminology Library
Industries
healthcare
See It In Action
A sample conversation with this agent
What signals are you watching across the active patient population?
Continuous monitoring across four streams: (1) session transcript NLP for risk language patterns (validated against Columbia-Suicide Severity Rating Scale terminology); (2) inbound patient messages flagged for distress, hopelessness, plan/means language; (3) wearable data for sleep disruption, elevated baseline HR, sudden activity collapse—patterns associated with relapse and crisis; (4) PHQ-9/GAD-7 trajectories with item-level monitoring (item 9 on PHQ-9 is the suicide ideation item, weighted separately). Risk signals don't trigger on a single source—the model fuses them and weights by clinical history.
James Patel's wearable shows three nights of <4 hours sleep and his PHQ-9 item 9 jumped from 1 to 3.
Critical alert. The combination of acute sleep disruption and a step-change on item 9 in someone with a documented prior SI episode is a high-acuity signal—I don't wait for a third confirmation. Paging his treating clinician now with the data: trajectory plot, wearable summary, last 10 patient messages (no concerning content but I'm including for context), and his crisis plan from the chart. Care manager is alerted in parallel for outreach within the next 2 hours per protocol. Suppressing all automated patient communication—a wellness nudge SMS landing right now could do harm. Logging the escalation with timestamps for the clinical record.
How do you handle a false positive—someone the clinician determines is not actually in crisis?
Clinician marks the alert as 'reviewed, not crisis' with a brief reason. That outcome feeds back into the model—not as a hard negative (the signal was real, just contextually not crisis) but as labeled data that helps the model learn the difference between, say, situational sleep disruption from a newborn and sleep disruption that co-occurs with depressive deterioration. False positives are tracked per-clinician to make sure I'm not over-paging any one person. I'd rather over-page and learn than miss.