Benefit Harmonization Engine

python
# Benefit Harmonization Modeling
def model_harmonization_scenarios(legacy_plan_a, legacy_plan_b, combined_population):
    scenarios = {}
    
    # Scenario 1: Status Quo (Grandfather both plans)
    scenarios['status_quo'] = {
        'plan_a_cost': legacy_plan_a.annual_pmpy * legacy_plan_a.member_count,
        'plan_b_cost': legacy_plan_b.annual_pmpy * legacy_plan_b.member_count,
        'total_cost': (legacy_plan_a.annual_pmpy * legacy_plan_a.member_count) + 
                      (legacy_plan_b.annual_pmpy * legacy_plan_b.member_count),
        'admin_complexity': 'HIGH',  # Dual plan administration
        'employee_perception': 'NEGATIVE'  # Inequity concerns
    }
    
    # Scenario 2: Level Up (Everyone to richer plan)
    richer_plan = legacy_plan_a if legacy_plan_a.annual_pmpy > legacy_plan_b.annual_pmpy else legacy_plan_b
    
    # Induced demand: richer benefits increase utilization
    induced_demand_factor = calculate_utilization_elasticity(
        old_plan=legacy_plan_b if richer_plan == legacy_plan_a else legacy_plan_a,
        new_plan=richer_plan
    )  # Returns 1.08-1.15 (8-15% utilization increase)
    
    scenarios['level_up'] = {
        'total_cost': (richer_plan.annual_pmpy * combined_population.total_members * induced_demand_factor),
        'cost_increase': scenarios['status_quo']['total_cost'] * (induced_demand_factor - 1),
        'admin_complexity': 'LOW',  # Single plan
        'employee_perception': 'POSITIVE'  # Everyone wins
    }
    
    # Scenario 3: Level Down (Everyone to leaner plan)
    leaner_plan = legacy_plan_a if legacy_plan_a.annual_pmpy < legacy_plan_b.annual_pmpy else legacy_plan_b
    
    # Reverse elasticity: worse benefits suppress utilization (but not 1:1)
    suppression_factor = 0.95  # Conservative 5% utilization reduction
    
    scenarios['level_down'] = {
        'total_cost': (leaner_plan.annual_pmpy * combined_population.total_members * suppression_factor),
        'cost_savings': scenarios['status_quo']['total_cost'] - (leaner_plan.annual_pmpy * combined_population.total_members * suppression_factor),
        'admin_complexity': 'LOW',
        'employee_perception': 'VERY_NEGATIVE',  # Half the workforce gets worse benefits
        'retention_risk': 'HIGH'
    }
    
    # Scenario 4: Tiered Harmonization (Job grade tiers)
    executive_tier = design_plan(deductible=500, oop_max=3000, copay_specialist=30)
    manager_tier = design_plan(deductible=1500, oop_max=5000, copay_specialist=50)
    employee_tier = design_plan(deductible=2500, oop_max=7000, copay_specialist=75)
    
    scenarios['tiered'] = {
        'total_cost': (
            (executive_tier.pmpy * combined_population.executives) +
            (manager_tier.pmpy * combined_population.managers) +
            (employee_tier.pmpy * combined_population.employees)
        ),
        'admin_complexity': 'MEDIUM',
        'employee_perception': 'MIXED'  # Transparency around tiers required
    }
    
    # Scenario 5: Cost-Neutral Hybrid (Find the middle)
    target_cost = scenarios['status_quo']['total_cost']
    hybrid_plan = optimize_plan_design(
        target_pmpy=target_cost / combined_population.total_members,
        constraints={
            'min_deductible': min(legacy_plan_a.deductible, legacy_plan_b.deductible),
            'max_deductible': max(legacy_plan_a.deductible, legacy_plan_b.deductible),
            'preserve_rx_coverage': True
        }
    )
    
    scenarios['cost_neutral_hybrid'] = {
        'total_cost': target_cost,
        'plan_design': hybrid_plan,
        'admin_complexity': 'LOW',
        'employee_perception': 'NEUTRAL',  # Nobody gets everything, nobody loses everything
        'recommended': True
    }
    
    return scenarios

# Example Output:
# Company A (2,400 employees, $9,200 PMPY, $500 deductible)
# Company B (1,600 employees, $11,800 PMPY, $1,500 deductible)
# 
# Status Quo: $41.0M total
# Level Up: $51.2M (+$10.2M, 25% increase due to induced demand)
# Level Down: $36.8M (-$4.2M savings, HIGH retention risk)
# Tiered: $43.5M (+$2.5M, MEDIUM complexity)
# Cost-Neutral Hybrid: $41.0M (balanced design: $1,000 deductible)
# 
# Recommendation: Cost-Neutral Hybrid