Announcing the Next Gen of Healthcare AI: Orchestrators for Outcomes, Not Tasks

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Orchestrators / Provider / AI InPatient

Provider

AI InPatient

AI InPatient
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Listen to Demo Calls
01
Problem
02
Use Cases
03
Features
04
ROI
05
Demo Calls
01
Section 01

Problem

The care a patient gets depends on how busy the floor was that night.

Whether discharge instructions are complete, whether the history is thorough, whether a housing problem is identified — none of it tracks to the patient’s condition. It tracks the census, and nothing in the outcomes data shows it.

Nurses absorb that variance. Education, intake, and medication teaching take hours during busy shifts and pull nurses off the bedside and off the top of their license. It is one of the single largest sources of burnout on the unit.

The answer is delegation, not replacement. The nurse orders the task, it gets completed in the room, a structured note lands in the chart. If an unexpected symptom surfaces during the conversation, the nurse is brought in to assess. Roughly three hours back per nurse per shift, in live deployments today.

The patient gets the same education at 2am on a full unit as at 10am on an empty one. This was unsolvable because you cannot hire a second nurse for every nurse.

Experience Nurse Co-Pilot
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Section 02

Use Cases

  • Bedside Education
  • Bedside Admissions
  • Bedside Discharge
  • Meds to Beds
  • SDOH Assessment
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Section 03

Features

Features:

  • Nurse-ordered agent workflow — nurse orders, AI calls the patient, task completed, EHR updated with no charting required.
  • Admission Education (~15 min saved per shift), Patient Education (~60 min), Caregiver Engagement (~40 min), and Medication Adherence (~60 min).
  • Checklist prompting and adherence at 99.3% non-linear intake.
  • SDOH supervisor model for conversational assessment with structured form fill, so a housing or transportation problem is caught on a busy night as reliably as a slow one.
  • Health Literacy supervisor model so discharge instructions actually land.
  • Medication counseling on the full drug-knowledge stack (Drug Safety 99.95%, interactions, brand-to-generic, dosage verification).
  • Meds to Beds coordination.
  • Suicidal Ideation and Adult & Child Protective Services supervisor models.
  • Clinical Escalation Safety 99.75% across 8 clinical categories with graceful handoff to the bedside team.
  • HD Audio Support and Noise Cancellation supervisor models for a busy room, Caregiver Speech supervisor model for family in the background, Slurred Speech and MCI supervisor models for patients who are medicated or disoriented.
  • Mid-call language switching at 98.8%.
  • Angel Engine / Infinite Patience.

Live at University Hospitals and Children’s Mercy.

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Section 04

ROI

SAMPLE USE CASE: Meds to Beds → ROI: $3.3M engine-computed on 4.1K patients ($25K annual revenue per retained script)
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Section 05

Demo Calls

AI InPatient Clip 1
AI InPatient Clip 2