Healthcare Economics Engine

Reference-Based Pricing Savings

Calculate precise RBP savings potential, model provider acceptance rates, and optimize pricing strategies with multi-year ROI projections

The PPO Premium Trap

You're paying 240% of Medicare rates because your broker says "that's the best network discount available." Meanwhile, reference-based pricing clients are paying 140% of Medicare and saving $4M annually. Your broker never mentioned it.

Avg PPO Markup

240%

of Medicare rates

RBP Target Rate

140%

of Medicare benchmark

Potential Savings

30-40%

on medical spend

✗ What Fails Without This Engine

•Broker says "RBP is too risky" without showing you the actual numbers
•You accept 180% of Medicare as "aggressive discount" when it's still overpriced
•No data on provider acceptance rates in your specific market
•CFO can't get board approval without credible savings projections

Data-Driven RBP Strategy

Our RBP Savings Engine analyzes your actual claims against Medicare benchmarks, models provider acceptance rates by facility type, and projects multi-year savings with implementation roadmaps. You get board-ready financial analysis before you make the move.

RBP Financial Model
// Claims-level pricing comparison
FOR each claim IN last_24_months:
  medicare_rate = GET_medicare_base(DRG, CPT, geography)
  current_paid = claim.allowed_amount
  rbp_target = medicare_rate × TARGET_MULTIPLIER  // e.g., 140%
  
  savings_opportunity = current_paid - rbp_target
  provider_tier = CLASSIFY_provider(volume, quality, leverage)
  
  acceptance_probability = HISTORICAL_data(
    provider_tier,
    facility_type,
    service_line,
    market_competitiveness
  )
  
  risk_adjusted_savings = savings_opportunity × acceptance_probability

// Portfolio optimization
OPTIMIZE pricing_strategy TO:
  MAXIMIZE(total_savings)
  WHILE maintaining(
    provider_access_threshold,
    member_satisfaction_floor,
    balance_billing_tolerance
  )

GENERATE:
  - Multi-year savings projection
  - Implementation timeline
  - Risk mitigation plan

Engineering Architecture

Core Components

Medicare Rate Database: Complete DRG, CPT, and geographic adjustment tables with quarterly updates
Claims Repricing Engine: Line-item comparison of current vs. RBP rates with savings waterfall
Provider Leverage Scoring: Market power analysis, volume concentration, quality metrics
Acceptance Rate Modeler: Facility-specific negotiation success probability based on 500+ implementations

Strategic Outputs

Target Multiplier

120-180%

Savings Range

$2M-$8M

Acceptance Rate

75-95%

Implementation

6-12mo

Real-World Applications

Manufacturing Company RBP Migration

•3,200 lives, $22M annual medical spend
•Current PPO paying 220% of Medicare
•Engine models 145% target with 88% provider acceptance
•Year 1 savings projection: $4.2M (19% reduction)
•3-year cumulative savings: $14.8M after implementation costs

Hybrid Strategy Optimization

•CFO worried about full RBP member disruption
•Engine models hybrid: PPO for primary care, RBP for facility services
•Targets 160% on hospital/surgery, keeps existing PCP network
•12% total cost reduction with <5% member disruption
•Implemented in 4 months, hit targets within 6 months

See Your RBP Savings Potential

Upload 12 months of claims data. Get your customized RBP financial model in under 2 hours. See exactly what you're leaving on the table with your current PPO contract.

Request Engine Demo→