Self-Hosted Architecture and Self-Correcting Models
ROI Projections
Quality Improvements:
- Error reduction: Target 80% reduction in clinical data extraction errors through self-correction
- Patient satisfaction: Increase from baseline to > 4.5/5 CSAT through continuous quality improvement
- System reliability: 99.9% uptime with conversation quality guarantees
- Cost efficiency: Optimized model routing and caching reduce per-conversation inference costs
Current Investment
Infrastructure Costs:
- Conversation and reasoning model hosting: integrated cloud infrastructure with auto-scaling
- Evaluation framework: LLM-as-judge integration and custom evaluation tooling
- Monitoring systems: extension of existing observability stack (Grafana, ELK, PagerDuty)
- Development resources: dedicated team for self-correction system development and protocol improvement
Projected Savings:
- Reduced human review: 70% reduction in manual conversation quality assurance
- Improved patient engagement: 25% increase in conversation completion rates reduces re-call costs
- Decreased support costs: 40% reduction in clinician-reported conversation issues
- Enhanced reliability: 50% reduction in conversations requiring human intervention
LRMs as a Judge
ROI Projections
Quality Improvements:
- Conversation quality increase: Target 40% improvement in average quality scores across protocols
- Error reduction: 60% decrease in quality-related clinical team escalations
- Consistency improvement: 90% reduction in quality score variance across conversations and protocols
- Protocol optimization: 50% faster identification and resolution of protocol design issues
Cost Savings:
- Reduced human review costs: significant annual savings in clinical quality assurance labor
- Decreased clinician support: 30% reduction in quality-related escalation volume
- Improved patient retention: higher engagement quality reduces patient opt-out rates
- Infrastructure efficiency: integration with existing compute resources minimizes additional hosting costs
Healthcare Organization ROI
The projections below are modeled estimates for a mid-size primary care organization. Actual results vary based on patient population, EHR integration depth, staff workflows, and protocol configuration. We recommend validating assumptions with your operations team during the pilot phase.
Direct Cost Savings (Mid-Size Primary Care, 50 Physicians)
| Current State | Annual Cost |
|---|
| Care coordinators for patient outreach (4 FTEs) | $240,000 |
| Medical assistant phone time (~30% allocation) | $180,000 |
| Missed appointment revenue loss (8% no-show rate) | $450,000 |
| Incomplete intake rework and re-calls | $95,000 |
| Total | $965,000 |
| With HANA (Conservative Estimate) | Annual Cost |
|---|
| HANA platform fee | $180,000 |
| Reduced care coordinator need (4 → 3 FTEs) | $180,000 |
| Reduced MA phone time (30% → 15%) | $90,000 |
| Reduced no-show rate (8% → 5%) | $280,000 |
| Reduced intake rework | $45,000 |
| Total | $775,000 |
Revenue Uplift
| Revenue Driver | Mechanism | Estimated Annual Impact (Range) |
|---|
| Reduced no-shows | Proactive outreach + rescheduling | 150,000–250,000 |
| BHI/CCM documentation | Structured documentation supporting CMS billing review | 100,000–180,000 |
| Care gap closure | Outreach for preventive screenings | 80,000–120,000 |
| Patient retention | Higher engagement reduces churn | 50,000–95,000 |
| Total | | 380,000–645,000 |
BHI/CCM revenue estimates assume clinical staff validate billing eligibility per CMS guidelines. Revenue impact depends on payer mix, patient panel size, and billing compliance workflow. These are not guaranteed outcomes.
