US hospital network speeds up critical diagnoses by 48% with AI

The hospital network sought to improve its AI-powered clinical decision support system for emergency triage and high-risk diagnoses.

The primary goal was to help frontline teams:

• Identify serious but subtle symptoms more accurately
• Suggest evidence-based treatment pathways
• Reduce diagnostic delays in critical conditions such as sepsis, stroke, and heart attacks

The system also needed to function consistently across diverse hospital environments while maintaining high specificity to avoid false alarms.

Existing systems struggled to deliver reliable support in fast-paced emergency care.

• Triage models showed poor accuracy, leading to bed misallocation and delays
• Alerts for conditions like sepsis triggered too many false positives, creating clinician fatigue
• Diagnostic errors were frequent in children, elderly patients, and atypical cases
• Models trained on academic datasets performed poorly in community hospital settings
• Recommended protocols often failed to match local workflows and resource availability

These gaps contributed to avoidable delays, unnecessary admissions, and higher malpractice exposure across the network.

CleverX designed a structured, subspecialist-led program to refine the hospital's emergency AI models using real-world expertise.

Expert recruitment:

• 21 emergency specialists including board-certified physicians, NPs, and EMS medical directors
• Expertise across trauma, fast-track, rural emergency medicine, and pre-hospital care
• Active clinical certifications ensured knowledge aligned with latest practice standards

Execution framework:

• Clinical realism at scale: Annotated 35,000 emergency cases, creating age-specific scoring and highlighting subtle presentations
• Condition-specific optimization: Trained detection for 25 high-risk conditions such as atypical sepsis, aortic dissection, and stroke
• Smart integration: Embedded AI into hospital systems with workflow-sensitive alert routing, and trained 150 emergency staff on adoption

All work followed secure data handling and prospective validation protocols to ensure clinical safety.

The engagement rapidly demonstrated improvements across triage accuracy, clinician trust, and system performance.

Weeks 1–2: Clinical annotation and dataset enrichment

• Reviewed 35,000 cases for subtle findings
• Built high-risk scoring systems for vulnerable populations
• Achieved >90% expert agreement on actionable findings

Weeks 3–4: Model refinement and integration

• Optimized 25 condition-specific detection models
• Embedded triage AI into ED workflows with real-time alert routing
• Conducted staff training and prospective pilot testing

Clinical outcomes:

AI-assisted workflows improved detection speed and accuracy in time-sensitive emergencies.

• 48% reduction in diagnosis delays
• Door-to-decision time cut from 3.2h → 2.1h
• Admission prediction accuracy rose from 51% → 78%
• Sepsis alert false positives reduced by 31 percentage points (while maintaining 94% sensitivity)

Operational improvements:

EDs saw smoother workflows and higher throughput during critical load times.

• 34% increase in throughput at peak hours
• Reading burden reduced for residents and attendings
• Staff reported higher trust in AI support for frontline triage

Financial & patient impact:

Better accuracy and faster decisions reduced risk and created measurable value.

• Avoided $2.7M in unnecessary admissions and testing
• Reduced malpractice exposure by $1.9M
• Patient satisfaction scores improved by 31%

Strategic recognition:

The hospital network became a national beta site for emergency informatics, licensed its triage model to peers, and earned the ENA Innovation Award for emergency care AI.