Nurse triage and discharge copilot cuts length of stay 0.6 days at a 9-hospital system
A regional health system was holding patients an extra half-day on average due to discharge coordination gaps and after-hours triage bottlenecks. pronix.ai deployed a clinician-in-the-loop nurse triage and discharge copilot integrated with Epic — length of stay dropped 0.6 days, discharges before noon rose 46%, and nurses reclaimed 90 minutes per shift on documentation and coordination work.
- Client
- Regional health system, 9 hospitals and 2,400 beds
- Industry
- Healthcare Providers
- Platform
- Epic · Azure OpenAI · Microsoft Copilot Studio · Kore.ai Agent Platform
*Representative outcome; results vary by client, scope and platform configuration.
The challenge
Bed capacity was constrained, ED boarding was rising, and case managers spent their mornings chasing transport, DME, home health and payer authorizations instead of rounding. Nurses fielded routine after-hours calls that could have been triaged safely with the right guardrails.
Our approach
Guardrailed nurse triage assistant
An after-hours triage copilot ran an evidence-based symptom protocol library, escalated any red-flag signal to an RN within 30 seconds, and documented the encounter back to Epic — never issuing clinical advice without RN sign-off.
Discharge readiness agent
Monitored inpatient charts against discharge-criteria bundles by DRG, flagged blockers (pending DME, transport, med rec, payer auth) with owner and due-time, and drafted patient-friendly discharge instructions for RN review.
Multi-agent care coordination
Specialist sub-agents handled payer auth, home-health referral, DME ordering and transport booking in parallel — case managers approved rather than dialed, cutting phone-tag by 70%.
Clinical governance harness
Every prompt, protocol version and model output evaluated against a clinical safety rubric weekly; a nursing informatics council owned change control, red-team scenarios and audit reporting.
“Our case managers are rounding again instead of holding for a payer rep. That is the win — the copilot handles the queue, our team handles the patient.”
Illustrative case study. Scenarios, metrics, quotes and client details are representative composites based on Pronix engagements and industry benchmarks unless a named client is shown with written consent. Outcomes vary by client, scope, data quality and platform configuration. Nothing on this page is a guarantee, warranty or professional advice. See our Terms of Use for the full disclaimer.
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