High-Cost Claimant Prediction Engine

python
# High-Cost Claimant Prediction Model
def predict_high_cost_trajectory(member, claims_history, clinical_data):
    risk_score = 0
    
    # Clinical Complexity Signals (30 points)
    chronic_conditions = count_chronic_conditions(member)
    if chronic_conditions >= 3:
        risk_score += 15  # Multi-morbidity
    
    if has_recent_diagnosis(member, ['cancer', 'ms', 'crohns', 'rheumatoid_arthritis']):
        risk_score += 15  # High-cost condition onset
    
    # Utilization Velocity (25 points)
    recent_admits = count_admissions_last_6_months(member)
    if recent_admits >= 2:
        risk_score += 12  # Increasing hospitalization rate
    
    specialty_visits = count_specialty_visits_last_3_months(member)
    if specialty_visits >= 4:
        risk_score += 8  # Accelerating specialty engagement
    
    er_visits = count_er_visits_last_6_months(member)
    if er_visits >= 3:
        risk_score += 5  # Unmanaged condition signals
    
    # Pharmacy Patterns (20 points)
    specialty_drugs = get_specialty_medications(member)
    if len(specialty_drugs) >= 2:
        risk_score += 10  # Multiple high-cost drugs
    
    if has_new_specialty_start_last_90_days(member):
        risk_score += 10  # Recent high-cost therapy initiation
    
    # Prior Cost Trajectory (15 points)
    ytd_cost = sum_claims_ytd(member)
    prior_year_cost = sum_claims_prior_year(member)
    
    if ytd_cost > (prior_year_cost * 1.5):
        risk_score += 15  # Accelerating cost trend
    
    # Care Gaps (10 points)
    if medication_adherence(member) < 0.70:
        risk_score += 5  # Non-adherent pattern
    
    if missed_follow_ups_last_6_months(member) >= 2:
        risk_score += 5  # Care coordination gaps
    
    # Cap at 100
    risk_score = min(risk_score, 100)
    
    # Predict 12-month cost trajectory
    if risk_score >= 75:
        projected_cost = 185000  # High probability $100K+
        intervention_priority = 'CRITICAL'
    elif risk_score >= 60:
        projected_cost = 95000
        intervention_priority = 'HIGH'
    elif risk_score >= 45:
        projected_cost = 55000
        intervention_priority = 'MODERATE'
    else:
        projected_cost = ytd_cost * 1.2
        intervention_priority = 'STANDARD'
    
    return {
        'risk_score': risk_score,
        'projected_12mo_cost': projected_cost,
        'intervention_priority': intervention_priority,
        'recommended_actions': generate_care_plan(member, risk_score)
    }

# Monthly cohort refresh
high_risk_cohort = []
for member in active_members:
    prediction = predict_high_cost_trajectory(member, claims, clinical)
    
    if prediction['intervention_priority'] in ['CRITICAL', 'HIGH']:
        high_risk_cohort.append({
            'member': member,
            'prediction': prediction
        })
        
        # Alert care manager
        assign_case_manager(member, prediction)

print(f"High-risk cohort: {len(high_risk_cohort)} members")
print(f"Projected savings opportunity: {sum(p['projected_12mo_cost'] * 0.20 for p in high_risk_cohort):,.0f}")