Healthcare
North America
Health System: Clinical Decision Support Trust
Clinicians ignored AI alerts because they didn't trust the black box. Rotavision built the explainability and monitoring layer that turned skeptics into advocates — with measurable clinical outcomes.
Challenge
Clinical AI that clinicians ignored
The health system piloted AI-assisted clinical decision support for sepsis early warning, medication interaction alerts, and diagnostic suggestions for complex cases.
The pilot exposed critical trust gaps:
Clinician skepticism
- "Black box" recommendations ignored
- No explanation of AI reasoning
- Physicians overriding 70% of alerts
Alert fatigue
- Too many low-confidence alerts
- Clinicians dismissing all alerts
- Missing genuine warnings in the noise
Regulatory concerns
- FDA guidance on AI/ML in clinical settings
- Documentation requirements for AI-assisted decisions
- Liability questions unanswered
Outcome uncertainty
- Couldn't measure if AI improved outcomes
- No feedback loop from clinical results
- Unsure if AI helping or hurting
Approach
Clinical AI trust infrastructure
Phase 1
Weeks 1-3
Clinical Trust Assessment
- Interviewed clinicians on AI trust barriers
- Analyzed alert response patterns
- Mapped regulatory requirements
- Defined clinical trust KPIs
Phase 2
Weeks 4-6
Trust Architecture
- Designed explainability layer
- Created confidence calibration framework
- Specified feedback loop architecture
- Defined monitoring requirements
Phase 3
Weeks 7-14
Implementation
- Deployed Guardian for clinical AI monitoring
- Implemented reasoning capture with clinical citations
- Built feedback loop from outcomes
- Created clinician dashboard
Phase 4
Weeks 15-20
Validation
- Measured clinician adoption changes
- Validated outcome correlation
- Documented for regulatory compliance
- Trained clinical informatics team
Solution
Trust infrastructure for clinical AI
Explainability layer
Every recommendation includes:
- Key factors driving the recommendation
- Relevant clinical evidence (with citations)
- Confidence level with uncertainty range
- Similar historical cases and outcomes
Confidence calibration
- High-confidence: Direct to clinician
- Medium-confidence: Enhanced review workflow
- Low-confidence: Suppressed (logged for learning)
Outcome feedback loop
- Clinical outcomes linked to AI recommendations
- Recommendations accepted vs rejected tracked
- Model updated based on real outcomes
- Continuous accuracy measurement
Guardian for clinical AI
- Monitors recommendation accuracy vs outcomes
- Detects drift as patient populations change
- Tracks clinician override patterns
- Alerts on degradation before harm
Results
From ignored to essential
| Metric | Before | After | Change |
|---|---|---|---|
| Alert acceptance rate | 30% | 68% | +127% |
| Clinician trust score | 2.8/5 | 4.1/5 | +46% |
| Sepsis early detection | 67% | 89% | +33% |
| Alert volume (per clinician/day) | 47 | 18 | -62% |
| Documentation compliance | 45% | 98% | +118% |
Clinical outcomes
- Sepsis mortality reduced 12%
- Medication errors down 23%
- Average length of stay reduced 0.4 days
"Our clinicians didn't trust the AI because the AI didn't explain itself. Rotavision helped us build the explainability and monitoring layer that turned skeptics into advocates. The AI is the same — the trust infrastructure made it usable."
— Chief Medical Information Officer
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