Orchestrators / Provider / AI STAR Rating

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Section 01

Problem

You can close the gap without ever fixing what caused it.

A system can get very good at getting the mammogram scheduled and the code entered without ever learning why this patient hadn’t been screened in six years. The number moves; the fear, the distrust, the bad experience last time do not. Reminder letters have already reached everyone who responds to reminder letters.

The patients still open have a specific, removable reason: no ride, no assigned doctor, no idea what it costs, or something that went wrong once and was never followed up on.

There is a second cost. Chasing gaps and delivering routine results sits on clinical staff who should be doing something else. Thirty thousand negative mammogram results were delivered so staff didn’t have to; 91% of patients were fully informed and 34% booked next year’s screening on that same call.

The measure was never the point — the small tumor found early is. Finding out why one patient has said no for years takes a conversation, and there are tens of thousands of them.

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Section 02

Use Cases

  • Super HEDIS
  • Mammography
  • CRC Screening
  • Diabetic Followups
  • Cervical Cancer Screening
  • Super CAHPS
  • Pediatrics
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Section 03

Features

Features:

  • Super HEDIS multi-measure campaigns in a single call with omni-topic handling.
  • Screening scheduling completed in-call — mammography, CRC, cervical, diabetic follow-ups — at 99.44% Advanced Scheduling accuracy with confirm-before-booking.
  • Normal and borderline result read-back with reference ranges and an option to message the MD.
  • Motivational Interviewing supervisor model to surface fear, distrust, no transportation, no assigned doctor, cost confusion, or a bad past experience, then remove that specific barrier.
  • Trust Building supervisor model for AI hesitancy at 96.2% vs. 72.2% for the best frontier model.
  • Health Literacy supervisor model.
  • Tangent and Emotional Safety supervisor models, benchmarked on HEART.
  • Conversational assessment form fill at 99.3%.
  • Caregiver Rapport, Caregiver SDOH, and Reassurance supervisor models for pediatric measures.
  • Get Callback Info for unreachable patients.
  • Mid-call language switching at 98.8% — 275% higher community health needs assessment return rate.
  • Multi-call Memory across campaign waves.
  • Structured EHR write-back.

Potential features:

  • In-call transportation booking.
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Section 04

ROI

SAMPLE USE CASE: Med Adherence (Super HEDIS, 3x Part D) → ROI: $6M expected value on 10K members ($2–8K avoided cost per adherent member + Star protection)
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Section 05

Demo Calls

Empathy (Mammogram) 2
Patient Education (Colo Screen)
Behavioral Health Support – PEDS