Predict 30-day readmissions with 82% accuracy using discharge data, medication adherence signals, and social determinant risk factors—trigger post-acute interventions before the revolving door spins
Your member is discharged from a 5-day CHF admission ($24,000). Twelve days later, they're back—fluid overload, medication confusion, missed follow-up. Second admission: $18,000. Medicare penalizes the hospital for readmissions, but you pay full freight twice. Across your 10,000 lives, 18% readmission rate costs $3.8M annually in preventable re-hospitalizations. Most are predictable at discharge if you know what to look for.
Our 30-Day Readmission Prediction Engine scores every hospital discharge 0-100 using clinical complexity, medication regimen, social determinants, prior utilization patterns, and post-acute follow-up adherence—flags high-risk members for intensive transition support within 24 hours of discharge.
Readmission Risk Scoring Algorithm# 30-day readmission prediction at discharge def predict_readmission_risk(discharge_event, member_history): risk_score = 0 # Clinical Complexity (25 points) if discharge_event.diagnosis in ['CHF', 'COPD', 'pneumonia', 'sepsis']: risk_score += 15 # High-risk diagnoses if discharge_event.length_of_stay >= 7: risk_score += 5 # Extended stays if discharge_event.icu_admission: risk_score += 5 # ICU involvement # Medication Burden (20 points) new_medications = discharge_event.discharge_medications - member_history.prior_medications if len(new_medications) >= 3: risk_score += 10 # Complex regimen changes if 'warfarin' in new_medications or 'insulin' in new_medications: risk_score += 10 # High-alert medications # Prior Utilization (20 points) if member_history.admissions_last_6_months >= 2: risk_score += 10 # Frequent flyer pattern if member_history.days_since_last_discharge <= 30: risk_score += 10 # Recent readmission # Social Determinants (15 points) if member_history.lives_alone and member_history.age >= 65: risk_score += 8 # Social isolation if member_history.transportation_barrier: risk_score += 7 # Follow-up access issue # Medication Adherence History (10 points) if member_history.pdc_last_year < 0.60: risk_score += 10 # Non-adherent pattern # Follow-up Appointment Scheduled? (10 points) if not discharge_event.pcp_appointment_within_7_days: risk_score += 10 # No post-acute plan # Discharge Destination (Extra Risk) if discharge_event.destination == 'skilled_nursing_facility': risk_score += 5 # SNF bridge risk # Cap at 100 risk_score = min(risk_score, 100) # Risk stratification if risk_score >= 70: return { 'risk_level': 'HIGH', 'readmit_probability': 0.45, # 45% chance of 30-day readmission 'intervention': 'intensive_transition_support', 'outreach_timeline': 'within_24_hours' } elif risk_score >= 50: return { 'risk_level': 'MODERATE', 'readmit_probability': 0.22, 'intervention': 'standard_discharge_call', 'outreach_timeline': 'within_72_hours' } else: return { 'risk_level': 'LOW', 'readmit_probability': 0.08, 'intervention': 'automated_message', 'outreach_timeline': 'within_7_days' } # Real-time scoring as discharge claims post for discharge in get_new_discharges(): member = get_member_profile(discharge.member_id) prediction = predict_readmission_risk(discharge, member) if prediction['risk_level'] == 'HIGH': # Alert care manager for immediate outreach send_alert(care_manager, f"HIGH RISK DISCHARGE: {member.name}") create_intervention_plan(member, prediction['intervention']) log_prediction(discharge, prediction) # Validation: Track actual vs. predicted def validate_model(): predictions = get_predictions_last_30_days() actuals = get_actual_readmissions_last_30_days() accuracy = calculate_accuracy(predictions, actuals) print(f"Model accuracy: {accuracy:.1%}") # Target: >80%
Predict 30-day readmissions at discharge. Alert care managers to high-risk members. Trigger intensive transition support. Turn reactive crisis management into proactive prevention.
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