Employer Cost Shifting Engine

python
# Employer Cost Shifting Optimizer
def optimize_cost_shifting(current_plan, target_savings, workforce_demographics):
    shift_levers = []
    
    # Lever 1: Income-Tiered Contributions
    income_bands = [
        {'max_salary': 50000, 'contribution_pct': 0.0},   # Protect low earners
        {'max_salary': 80000, 'contribution_pct': 1.5},
        {'max_salary': 120000, 'contribution_pct': 2.5},
        {'max_salary': float('inf'), 'contribution_pct': 3.5}  # Executives absorb most
    ]
    
    tiered_savings = 0
    for band in income_bands:
        employees_in_band = workforce_demographics.filter(
            salary__lte=band['max_salary'],
            salary__gt=previous_band_max if band != income_bands[0] else 0
        )
        employee_premium = current_plan.annual_premium / 12
        monthly_shift = employee_premium * (band['contribution_pct'] / 100)
        tiered_savings += monthly_shift * len(employees_in_band) * 12
    
    shift_levers.append({
        'name': 'Income-Tiered Contributions',
        'annual_savings': tiered_savings,
        'member_friction': 'LOW',  # High earners can absorb, low earners protected
        'retention_risk': 'MINIMAL',
        'implementation_complexity': 'MEDIUM'
    })
    
    # Lever 2: Value-Based Plan Design
    vbid_changes = {
        'preventive_care': {'current_copay': 25, 'new_copay': 0, 'utilization_change': 1.12},
        'chronic_rx': {'current_copay': 10, 'new_copay': 0, 'utilization_change': 1.08},
        'brand_rx_generic_alt': {'current_copay': 35, 'new_copay': 70, 'utilization_change': 0.60},
        'er_non_urgent': {'current_copay': 150, 'new_copay': 350, 'utilization_change': 0.75}
    }
    
    vbid_net_savings = 0
    for service, params in vbid_changes.items():
        current_cost = estimate_annual_cost(service, current_plan)
        copay_shift = (params['new_copay'] - params['current_copay']) * params['utilization_change']
        volume = estimate_service_volume(service, current_plan)
        vbid_net_savings += copay_shift * volume
    
    shift_levers.append({
        'name': 'Value-Based Plan Design',
        'annual_savings': vbid_net_savings,
        'member_friction': 'LOW',  # Better access to high-value care
        'retention_risk': 'MINIMAL',
        'health_outcome_impact': 'POSITIVE'  # Encourages appropriate utilization
    })
    
    # Lever 3: Pharmacy Tier Realignment
    tier_shifts = [
        {'drug': 'Insulin Brand A', 'from_tier': 2, 'to_tier': 3, 'biosimilar_protected': True},
        {'drug': 'Statin Brand B', 'from_tier': 2, 'to_tier': 3, 'generic_alternative': True}
    ]
    
    tier_savings = 0
    for shift in tier_shifts:
        annual_rx_cost = lookup_drug_cost(shift['drug'], current_plan)
        tier_2_copay = 35
        tier_3_copay = 70
        switch_rate = 0.65  # 65% switch to lower-cost alternative
        tier_savings += (tier_3_copay - tier_2_copay) * annual_rx_cost['fills'] * switch_rate
    
    shift_levers.append({
        'name': 'Pharmacy Tier Realignment',
        'annual_savings': tier_savings,
        'member_friction': 'MEDIUM',  # Some member confusion
        'therapeutic_protection': 'MAINTAINED',  # Alternatives available
        'communication_required': True
    })
    
    # Rank by savings-to-friction ratio
    for lever in shift_levers:
        friction_score = {'LOW': 1, 'MEDIUM': 2, 'HIGH': 3}[lever['member_friction']]
        lever['efficiency_ratio'] = lever['annual_savings'] / friction_score
    
    shift_levers.sort(key=lambda x: x['efficiency_ratio'], reverse=True)
    
    # Build recommendation to hit target
    cumulative_savings = 0
    recommended_levers = []
    for lever in shift_levers:
        if cumulative_savings < target_savings:
            recommended_levers.append(lever)
            cumulative_savings += lever['annual_savings']
    
    return {
        'target_savings': target_savings,
        'total_savings': cumulative_savings,
        'recommended_levers': recommended_levers,
        'employee_impact_score': sum(1 for l in recommended_levers if l['member_friction'] == 'LOW') / len(recommended_levers)
    }

# Example: Close $5M budget gap
result = optimize_cost_shifting(
    current_plan=load_plan('2024'),
    target_savings=5000000,
    workforce_demographics=load_census()
)

print("Recommended Cost Shifts to Close 5M Gap:")
for lever in result['recommended_levers']:
    print("  {}: {:,.0f} ({} friction)".format(
        lever['name'], lever['annual_savings'], lever['member_friction']))
print("Total Savings: {:,.0f}".format(result['total_savings']))
print("Low-Friction Levers: {:.0%}".format(result['employee_impact_score']))