News 2025

Triage Copilots Case Study.

AI-augmented support for emergency departments.

Triage Copilots
Images of client and real-world deployments are contractually confidential.
All visuals are AI-generated for illustrative purposes.

Executive Summary

In 2025, EverSphere deployed Triage Copilots across partner hospitals in the US, UK, and Singapore.

By combining live patient data streams, risk-scoring models, and administrative automation, the copilots reduced intake paperwork time by 38%, improved triage accuracy, and preserved full clinician authority.

Built on EverSphere’s empathetic intelligence (Milo) and optimisation frameworks (Kai), the copilots act as AI-augmented teammates in high-pressure medical environments.

Industries Impacted
  • Emergency Medicine
  • Critical Care
  • Public Health Networks

Geographies: US, UK, Singapore (expansion pending across wider hospital systems).

Admin Overload

Manual intake forms, duplicated entries, time lost to paperwork

Triage Pressure

Overcrowded EDs, variable patient acuity, risk of missed critical cases

Staff Buronout

Cognitive overload, long shifts, fragmented handovers, rising attrition

The Problem

Emergency departments worldwide face a convergence of structural pressures: rising patient demand, increasing administrative load, and a growing shortage of frontline staff.

Clinicians are often forced to spend more time on forms and coding than on patients, while triage decisions rely on overstretched judgement under extreme pressure. The result is delayed care, uneven prioritisation, and avoidable risks.

Traditional EHRs and digital tools only digitise the paperwork; they do not provide the real-time prioritisation, risk signalling, or workload relief that modern emergency care requires.

Approach

Triage Copilots provide a unified, predictive layer that supports clinicians without replacing them.

Data foundation (live, governed, audit‑ready)
  • Clinical: vitals streams, lab triage markers, arrival notes, historical comorbidities.
  • Operational: bed availability, staff allocation, wait times, discharge bottlenecks.
  • External: regional incident feeds, ambulance ETA, public health alerts.
Core Models
  • Real-time prioritisation: ensemble triage scoring (logistic regression + deep nets + calibrated uncertainty).
  • Administrative automation: NLP-based auto-documentation, ICD/SNOMED coding suggestions, slotting into EHR.
  • Optimisation layer: queue balancing, bed allocation, and downstream discharge planning.
  • Explainability: per-patient reason codes (e.g. “Hypoxia risk ↑; comorbidity: COPD”).
System Design
  • Human-in-the-loop: every recommendation requires explicit clinician confirmation.
  • Model Zoo integration:
    • Milo → empathetic interaction layer for clinician-friendly explanations.
    • Kai → queue/bed optimisation under uncertainty.
  • APIs: FHIR-compatible for Epic, Cerner, Allscripts
  • Security & compliance: HIPAA, NHS DSPT, ISO 27001 aligned.
Deployment
  • Roll‑out:
    inference nodes in hospital IT racks; cloud redundancy for resilience.
  • Integration time:
    8–12 weeks (data mapping + workflow pilots)
  • Governance:
    overseen by Dr Abigail Shaw’s Assurance team; every release tested against EverSphere’s Ethics & Assurance Framework.

Primary Outcomes

-38%
intake paperwork time per patient
+21%
triage throughput (patients processed per hour)
-17%
false positives in critical risk flagging
+14%
clinician satisfaction in workflow surveys

Secondary Outcomes

-19%
Reduced burnout indicators (self-reported stress)
+21%
Improved handover consistency between shifts
+14%
improved early signals time-to-treatment in high-acuity cases (stroke, MI)

“Emergency departments are among the most demanding environments on Earth. Triage Copilots give clinicians clarity and time to focus where it matters most.”

Marcus Knox

Methodology

  • Prospective pilot in three private hospitals (US, UK, SG).
  • Randomised time-block A/B: standard workflow vs Copilot-augmented workflow.
  • Independent audit of clinical outcomes + assurance review.

Milestones

2025

Platform moves to select‑partner roll‑out across energy and health, supported by independent assurance and red‑team coverage.

2024

Decision engine hardened with policy‑constrained planning and full audit trails; restricted trials commence with critical‑infrastructure partners.

2023

Milo and Kai complete an extended closed‑box communication study; ShadowIntel undergoes evaluation in live training and operational scenarios.

Let’s build responsibly at planetary scale.

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