Healthcare
Clinical Decision Support Trust
Customer Profile
A leading hospital chain with 15 multi-specialty hospitals across India, handling 25 lakh+ patient encounters annually. The network includes teaching hospitals affiliated with medical colleges.
Challenge
The hospital chain deployed AI-assisted clinical decision support for sepsis early warning and medication interaction alerts. Despite promising accuracy in validation, clinicians largely ignored the AI recommendations:
Black Box Skepticism
- Physicians overrode 70% of AI alerts due to lack of explanation
- “The AI says so” was not acceptable justification for clinical decisions
- Senior clinicians actively discouraged juniors from relying on AI
Alert Fatigue
- Too many low-confidence alerts caused clinicians to dismiss all warnings
- 47 alerts per clinician per day made it impossible to evaluate each carefully
- Critical alerts buried among routine notifications
Regulatory Uncertainty
- CDSCO guidance on AI/ML-based medical devices was evolving
- Liability questions remained unanswered—who is responsible if AI is wrong?
- Medical council concerns about AI replacing clinical judgment
Outcome Invisibility
- No mechanism to track whether AI improved or harmed patient outcomes
- Acceptance and rejection patterns not analyzed for model improvement
- Individual clinician performance not correlated with AI usage
Solution
Rotavision implemented a trust infrastructure over 20 weeks:
Explainability Layer Every AI recommendation now includes:
- Key clinical factors driving the recommendation
- Relevant clinical evidence and guideline citations
- Confidence level with calibrated probability
- Similar historical cases with known outcomes
Confidence Calibration
- High-confidence alerts (>85%) presented prominently to clinicians
- Medium-confidence (60-85%) triggered enhanced review workflow
- Low-confidence (<60%) suppressed or routed to clinical informatics
Outcome Feedback Loop
- Patient outcomes linked to AI recommendations
- Acceptance vs rejection rates tracked by clinician and department
- Continuous model improvement based on clinical feedback
- Monthly outcome reports shared with department heads
Guardian Monitoring
- Real-time accuracy tracking across all clinical AI models
- Population drift detection for patient demographics
- Clinician override pattern analysis for model improvement
- Automated alerts for accuracy degradation
Vishwas for Fairness
Healthcare AI requires careful fairness monitoring:
- Performance parity across age groups and genders
- Socioeconomic status not influencing recommendations
- Regional hospital performance consistency
- Teaching vs non-teaching hospital calibration
Results
| Metric | Before | After | Change |
|---|---|---|---|
| Alert Acceptance | 30% | 68% | +127% |
| Clinician Trust Score | 2.8/5 | 4.1/5 | +46% |
| Sepsis Early Detection | 67% | 89% | +33% |
| Daily Alerts Per Clinician | 47 | 18 | -62% |
| Documentation Compliance | 45% | 98% | +118% |
Clinical Outcomes
The trust infrastructure translated to measurable patient outcomes:
| Outcome | Improvement |
|---|---|
| Sepsis Mortality | -12% |
| Medication Errors | -23% |
| Average Length of Stay | -0.4 days |
| ICU Readmissions | -8% |
ABDM Integration
The platform integrates with Ayushman Bharat Digital Mission:
- ABHA-linked patient records for complete history
- Health Facility Registry integration
- Consent-based data sharing with referring physicians
- Standardized health records for AI context
Clinician Feedback
“Earlier the AI would just say ‘sepsis risk high’ and I’d ignore it. Now it shows me the specific vitals trending, the patient’s history, and similar cases. I can make an informed decision.” — Intensivist, Bengaluru
What’s Next
The hospital chain is expanding AI trust infrastructure to:
- Radiology AI for imaging interpretation
- Pathology AI for histopathology analysis
- Discharge planning optimization
- NABL compliance documentation
Rotavision is powered by Rotascale’s globally-proven AI trust platform.
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