Chronic Disease Progression Engine

python
# Chronic Disease Progression Modeling
disease_stages = {
    'diabetes': {
        'stage_1': {'name': 'Prediabetes', 'annual_cost': 2800, 'progression_rate': 0.08},
        'stage_2': {'name': 'Type 2 Controlled', 'annual_cost': 8500, 'progression_rate': 0.12},
        'stage_3': {'name': 'Type 2 Uncontrolled', 'annual_cost': 14200, 'progression_rate': 0.18},
        'stage_4': {'name': 'Complications (Retinopathy/Neuropathy)', 'annual_cost': 28500, 'progression_rate': 0.22},
        'stage_5': {'name': 'End-Stage (Dialysis/Amputation)', 'annual_cost': 92000, 'progression_rate': 0.0}
    },
    'hypertension': {
        'stage_1': {'name': 'Prehypertension', 'annual_cost': 1800, 'progression_rate': 0.06},
        'stage_2': {'name': 'Stage 1 HTN Controlled', 'annual_cost': 4200, 'progression_rate': 0.10},
        'stage_3': {'name': 'Stage 2 HTN Uncontrolled', 'annual_cost': 9800, 'progression_rate': 0.15},
        'stage_4': {'name': 'Heart Failure', 'annual_cost': 35000, 'progression_rate': 0.08},
        'stage_5': {'name': 'Advanced HF (LVAD)', 'annual_cost': 185000, 'progression_rate': 0.0}
    }
}

def model_progression(member, current_stage, forecast_years=5):
    results = []
    stage = current_stage
    
    for year in range(1, forecast_years + 1):
        stage_config = disease_stages[member.condition][stage]
        
        # Base progression probability
        base_progression_prob = stage_config['progression_rate']
        
        # Risk Modifiers
        if member.medication_adherence < 0.70:
            base_progression_prob *= 1.6  # Poor adherence accelerates
        
        if member.missed_appointments > 2:
            base_progression_prob *= 1.3  # Care gaps worsen outcomes
        
        if member.comorbidities >= 3:
            base_progression_prob *= 1.4  # Multi-morbidity compounds risk
        
        # Age adjustment
        if member.age > 65:
            base_progression_prob *= 1.2
        
        # Cap at 90%
        progression_prob = min(base_progression_prob, 0.90)
        
        # Monte Carlo: will they progress?
        progresses = random.random() < progression_prob
        
        if progresses and stage < 'stage_5':
            next_stage_num = int(stage.split('_')[1]) + 1
            stage = f'stage_{next_stage_num}'
        
        # Record trajectory
        results.append({
            'year': year,
            'stage': stage,
            'stage_name': disease_stages[member.condition][stage]['name'],
            'projected_cost': disease_stages[member.condition][stage]['annual_cost'],
            'progression_probability': progression_prob
        })
    
    return results

# Intervention Impact Modeling
def model_intervention_impact(member, intervention_type):
    # Baseline: no intervention
    baseline_trajectory = model_progression(member, member.current_stage, 5)
    baseline_cost = sum(year['projected_cost'] for year in baseline_trajectory)
    
    # Intervention: modify progression rates
    if intervention_type == 'intensive_case_management':
        # 40% slower progression through medication support, appointment coordination
        for stage in disease_stages[member.condition].values():
            stage['progression_rate'] *= 0.60
    
    intervention_trajectory = model_progression(member, member.current_stage, 5)
    intervention_cost = sum(year['projected_cost'] for year in intervention_trajectory)
    
    # Program costs
    case_management_cost = 2500  # Annual per-member cost
    total_program_cost = case_management_cost * 5
    
    # Net savings
    gross_savings = baseline_cost - intervention_cost
    net_savings = gross_savings - total_program_cost
    
    return {
        'baseline_5yr_cost': baseline_cost,
        'intervention_5yr_cost': intervention_cost,
        'gross_savings': gross_savings,
        'program_cost': total_program_cost,
        'net_savings': net_savings,
        'roi': net_savings / total_program_cost
    }

# Example Output:
# Member: 52yo male, Type 2 diabetes uncontrolled (Stage 3), HbA1c 9.2%, med adherence 58%
# 
# Baseline 5-Year Trajectory (No Intervention):
#   Year 1: Stage 3 (Uncontrolled) - 18200
#   Year 2: Stage 4 (Complications) - 28500
#   Year 3: Stage 4 (Complications) - 28500
#   Year 4: Stage 5 (ESRD) - 92000
#   Year 5: Stage 5 (ESRD) - 92000
#   Total: 239200
# 
# With Intensive Case Management:
#   Year 1: Stage 3 (Uncontrolled) - 18200
#   Year 2: Stage 3 (Uncontrolled) - 18200  # Progression slowed
#   Year 3: Stage 3 (Uncontrolled) - 18200
#   Year 4: Stage 4 (Complications) - 28500
#   Year 5: Stage 4 (Complications) - 28500
#   Total: 111600
# 
# Net Savings: 115100 (ROI: 9.2x)