Score every AI agent in your front office and back office.
Intake bots, prior-auth handlers, charting copilots — they sell on minutes saved. AgentScore reads your scheduling and EHR-adjacent data to tell you which agents save real clinician time and which are quietly creating denials, no-shows, or correction work.
What you see in the dashboard
- Per-agent score across completed-encounter rate, denials avoided, clinician time saved, and override discipline.
- ImplementationScore for the practice — governance maturity (HIPAA-relevant policy adherence), workflow integration depth, learning loops.
- Anonymization-at-ingest rules: identifiers stripped before any aggregate analytics. We never train on identified rows, full stop.
The agents
What AI in healthcare ops typically looks like.
Patient-facing voice and front-office paperwork — same eight factors apply, with the governance hard cap weighted heavier than any other vertical.
Patient intake bot
Books appointments, captures insurance details, runs the eligibility check before the front desk picks up.
Scheduling assistant
Fills cancellations, manages waitlists, reduces no-shows with timed reminders + reschedule offers.
Prior-auth handler
Drafts prior-auth submissions from chart context, tracks status, escalates pends back to a human.
Charting copilot
Drafts the visit note from the conversation, formatted for the EHR, ready for clinician review + sign-off.
Care-gap recall
Reads panel data, contacts patients due for screenings or follow-up, books the visit when they say yes.
The signal
What we read from your scheduling system + claims feed.
OAuth or CSV. Vapi for the voice flows. We do not read clinical chart content — only operational metadata.
Completed encounters
Visits the agent helped book that actually closed in the EHR.
Denials avoided
Prior-auth or eligibility issues caught before the encounter — not after.
Clinician time saved
Charting, intake, or admin minutes returned to the provider per shift.
No-show rate
Patient no-show rate on appointments the agent owns vs. human-booked.
Override rate
How often a clinician or front-desk staffer corrects what the agent produced.
Multi-site primary care · 9 clinicians · 3 ops agents
A scheduling bot whose 'minutes saved' came from no-shows.
The vendor reported 480 minutes of front-desk time saved per week. AgentScore showed the no-show rate on the agent's bookings was 7 points higher than human-booked appointments — the saved minutes were ghosting downstream.
Reminder cadence retuned, eligibility checks moved earlier in the booking flow, no-show gap closed inside three weeks. Clinician-time savings then held up under audit.
SOC 2 in progress
Type II audit kicked off Q3 — interim report on request.
Read-only data access
OAuth scopes never request write to your CRM, jobs platform, or call data.
Algorithm-only scoring
Every score computed by ASAM. No manual overrides — not for customers, not for us.
Anonymized at ingest
Identifiers stripped before any aggregate analytics. We never train on identified rows.
See which ops agents earn their seat.
Operational metadata only. Read-only access. PHI never leaves your system to compute the score.