Tracks how precisely MedReception AI detects urgent intent and routes calls to the right on-call clinician without flooding them with noise.
Use this KPI when clinical leaders ask whether AI will miss true emergencies. It measures the percentage of high-priority calls that AI escalates correctly on the first attempt, factoring in routing rules, language handling, and follow-up confirmations.
Expressed as % of urgent calls correctly triaged and escalated.
Live benchmark
98%+ accuracy
Based on 50k+ after-hours calls across multi-specialty groups.
What drives accuracy
Intent models · escalation rules · confirmations
Performance improves as transcripts and QA loops train the model.
Cost breakdown
| Line item | Annual spend | Per-unit | Notes |
|---|---|---|---|
| Escalation detection | 98.3% | Hit rate | Signals from symptom keywords, caller history, and sentiment. |
| False-positive suppression | ≤1.1% | Noise rate | Dual confirmation (caller + workflow) avoids waking clinicians unnecessarily. |
| Follow-up verification | 100% | Logged | Every escalated call includes transcript + EMR task for audit. |
Assumptions
AI transcripts feed both the on-call line and the EMR inbox for redundancy.
Accuracy benchmark assumes English + Spanish support with human escalation safety net.
Weekly calibration sessions with ops + clinical leadership keep routing rules current.
Supporting proofs
Bring your current on-call plan and we’ll map AI routing, fail-safes, and QA loops in a working session.
