High-Cost Claimant Prediction Engine

python
# Gradient Boosting Model
def predict_high_cost_risk(member):
    features = {
        'chronic_conditions': count_chronic_dx(member),
        'specialty_visits': rolling_count(90, 'cardiology|oncology'),
        'pharmacy_complexity': unique_drug_classes(member),
        'ed_utilization': ed_visits_last_6mo(member),
        'procedure_escalation': imaging_frequency_trend(member),
        'medication_adherence': calculate_mpr(member),
        'biometric_trajectory': hba1c_trend(member),
        'care_fragmentation': unique_providers_90d(member)
    }
    
    risk_score = xgboost_model.predict_proba(features)[1]
    
    if risk_score > 0.75:
        expected_cost = estimate_episode_cost(member, risk_score)
        interventions = recommend_care_pathways(member)
        
        return {
            'risk_tier': 'HIGH',
            'probability': risk_score,
            'expected_12mo_cost': expected_cost,
            'interventions': interventions
        }