Cost Elasticity Engine

python
# Cost Elasticity Forecasting Engine
service_elasticity_map = {
    'preventive_care': -0.18,           # Low elasticity: critical care
    'primary_care_visit': -0.25,
    'specialist_visit': -0.35,
    'er_visit': -0.32,                 # Moderate: members defer if possible
    'urgent_care': -0.28,
    'diagnostic_imaging': -0.42,       # Higher: members skip if cost-share increases
    'elective_surgery': -0.55,         # Highest: very discretionary
    'physical_therapy': -0.48,
    'mental_health_visit': -0.38,
    'generic_rx': -0.15,               # Low: clinically necessary
    'brand_rx': -0.40,                 # Higher: members switch or skip
    'specialty_rx': -0.22              # Low-moderate: high clinical need
}

def model_elasticity_impact(current_plan, proposed_plan, population_census):
    utilization_changes = []
    
    for service, elasticity in service_elasticity_map.items():
        # Calculate cost-share change
        current_cost_share = calculate_member_cost_share(current_plan, service)
        proposed_cost_share = calculate_member_cost_share(proposed_plan, service)
        
        pct_change_cost_share = (proposed_cost_share - current_cost_share) / current_cost_share
        
        # Income stratification (low earners 2.4× more sensitive)
        income_adjusted_elasticity = {}
        for member in population_census:
            if member.salary < 50000:
                multiplier = 2.4
            elif member.salary < 80000:
                multiplier = 1.6
            elif member.salary < 120000:
                multiplier = 1.0
            else:
                multiplier = 0.6  # High earners least sensitive
            
            member_elasticity = elasticity * multiplier
            
            # Utilization change = elasticity × % cost-share change
            utilization_change = member_elasticity * pct_change_cost_share
            
            if member.id not in income_adjusted_elasticity:
                income_adjusted_elasticity[member.id] = {}
            income_adjusted_elasticity[member.id][service] = utilization_change
        
        # Aggregate population-level impact
        baseline_volume = get_service_volume(service, current_plan, population_census)
        avg_utilization_change = sum(income_adjusted_elasticity[m.id][service] 
                                     for m in population_census) / len(population_census)
        projected_volume = baseline_volume * (1 + avg_utilization_change)
        
        # Financial impact
        employer_baseline_cost = baseline_volume * get_plan_cost_per_service(service, current_plan)
        employer_projected_cost = projected_volume * get_plan_cost_per_service(service, proposed_plan)
        
        utilization_changes.append({
            'service': service,
            'elasticity': elasticity,
            'baseline_volume': baseline_volume,
            'projected_volume': projected_volume,
            'volume_change_pct': avg_utilization_change,
            'employer_cost_baseline': employer_baseline_cost,
            'employer_cost_projected': employer_projected_cost,
            'employer_savings': employer_baseline_cost - employer_projected_cost
        })
    
    # Total impact
    total_baseline_cost = sum(u['employer_cost_baseline'] for u in utilization_changes)
    total_projected_cost = sum(u['employer_cost_projected'] for u in utilization_changes)
    total_employer_savings = total_baseline_cost - total_projected_cost
    
    # Health outcome risk assessment
    high_value_services = ['preventive_care', 'primary_care_visit', 'generic_rx']
    high_value_volume_loss = sum(
        abs(u['volume_change_pct']) for u in utilization_changes 
        if u['service'] in high_value_services and u['volume_change_pct'] < 0
    ) / len(high_value_services)
    
    health_risk_score = 'HIGH' if high_value_volume_loss > 0.08 else 'MEDIUM' if high_value_volume_loss > 0.04 else 'LOW'
    
    return {
        'total_employer_savings': total_employer_savings,
        'utilization_changes': utilization_changes,
        'health_outcome_risk': health_risk_score,
        'high_value_care_impact': high_value_volume_loss,
        'recommendation': 'APPROVE' if health_risk_score == 'LOW' else 'MODIFY'
    }

# Example: Model $500 deductible increase
current = load_plan('2024')
proposed = load_plan('2024')
proposed.deductible_individual += 500
proposed.deductible_family += 1000

census = load_population_census()
result = model_elasticity_impact(current, proposed, census)

print("Projected Employer Savings: {:,.0f}".format(result['total_employer_savings']))
print("Health Outcome Risk: {}".format(result['health_outcome_risk']))
print("High-Value Care Impact: {:.1%}".format(result['high_value_care_impact']))
print("Recommendation: {}".format(result['recommendation']))

if result['recommendation'] == 'MODIFY':
    print("\nWarning: Proposed deductible increase reduces high-value care utilization by {:.1%}".format(
        result['high_value_care_impact']))
    print("Consider exempting preventive services from deductible (value-based design)")