Model Cost Impact When Key Providers Leave Network or Facilities Close—Know Your Exposure Before the Disruption
python# Network Disruption Impact Model def analyze_network_concentration(claims_data, network_roster): provider_spend = {} # Aggregate spend by provider/facility for claim in claims_data: provider_id = claim.rendering_provider_npi facility_id = claim.facility_npi if provider_id not in provider_spend: provider_spend[provider_id] = { 'name': lookup_provider_name(provider_id), 'specialty': lookup_specialty(provider_id), 'annual_spend': 0, 'claim_count': 0, 'unique_members': set() } provider_spend[provider_id]['annual_spend'] += claim.paid_amount provider_spend[provider_id]['claim_count'] += 1 provider_spend[provider_id]['unique_members'].add(claim.member_id) total_spend = sum(p['annual_spend'] for p in provider_spend.values()) # Identify concentration risk concentration_risk = [] for provider_id, data in provider_spend.items(): spend_pct = data['annual_spend'] / total_spend if spend_pct >= 0.05: # 5%+ of total spend risk_level = 'CRITICAL' if spend_pct >= 0.10 else 'HIGH' concentration_risk.append({ 'provider': data['name'], 'specialty': data['specialty'], 'annual_spend': data['annual_spend'], 'percent_of_total': spend_pct, 'member_count': len(data['unique_members']), 'risk_level': risk_level }) return sorted(concentration_risk, key=lambda x: x['annual_spend'], reverse=True) def model_provider_exit_impact(provider_id, claims_history, network_alternatives): # Current in-network cost provider_claims = claims_history.filter(provider=provider_id) current_annual_cost = sum(c.paid_amount for c in provider_claims) # Scenario 1: Provider goes OON oon_multiplier = 2.80 # OON facilities charge 280% of in-network allowed plan_pays_oon = 0.60 # Plan pays 60%, member pays 40% oon_annual_cost = current_annual_cost * oon_multiplier * plan_pays_oon # Scenario 2: Members redirect to alternative in-network alt_provider_rates = [lookup_rates(alt) for alt in network_alternatives] avg_alt_rate_multiplier = sum(alt_provider_rates) / len(alt_provider_rates) redirect_annual_cost = current_annual_cost * avg_alt_rate_multiplier # Scenario 3: Direct contract with exiting provider direct_contract_rate = 1.40 # 140% of Medicare (typical direct contract) direct_contract_cost = (current_annual_cost / 1.80) * direct_contract_rate # Assume current = 180% Medicare return { 'baseline_cost': current_annual_cost, 'oon_scenario': { 'annual_cost': oon_annual_cost, 'delta': oon_annual_cost - current_annual_cost, 'percent_increase': ((oon_annual_cost - current_annual_cost) / current_annual_cost) * 100 }, 'redirect_scenario': { 'annual_cost': redirect_annual_cost, 'delta': redirect_annual_cost - current_annual_cost, 'percent_increase': ((redirect_annual_cost - current_annual_cost) / current_annual_cost) * 100 }, 'direct_contract_scenario': { 'annual_cost': direct_contract_cost, 'delta': direct_contract_cost - current_annual_cost, 'percent_increase': ((direct_contract_cost - current_annual_cost) / current_annual_cost) * 100 }, 'recommended': 'direct_contract_scenario' # Lowest cost + preserves continuity } # Example: Orthopedic group exit claims = load_claims('2024') concentration = analyze_network_concentration(claims, network) # Model impact of top risk provider leaving top_risk = concentration[0] # ABC Orthopedics impact = model_provider_exit_impact(top_risk['provider_id'], claims, find_alternatives('orthopedics')) print("Provider: {} ({:.1%} of spend)".format(top_risk['provider'], top_risk['percent_of_total'])) print("\nScenario Analysis:") print(" OON: {:,.0f} (+{:,.0f})".format( impact['oon_scenario']['annual_cost'], impact['oon_scenario']['delta'])) print(" Redirect: {:,.0f} (+{:,.0f})".format( impact['redirect_scenario']['annual_cost'], impact['redirect_scenario']['delta'])) print(" Direct Contract: {:,.0f} (+{:,.0f})".format( impact['direct_contract_scenario']['annual_cost'], impact['direct_contract_scenario']['delta'])) delta_savings = impact['oon_scenario']['delta'] - impact['direct_contract_scenario']['delta'] print("\nRecommendation: Negotiate direct contract (saves {:,.0f} vs OON)".format(delta_savings))
markdown# Network Disruption Playbook ## Scenario 1: High-Volume Provider Exit **Trigger**: Single provider/group represents 5-15% of medical spend **Impact**: Members pay OON cost-share OR disrupt to new in-network (unknown cost) **Risk Factors**: Specialty groups (ortho, cardiology, GI), ASCs, infusion centers **Example**: Orthopedic group exits → $680K in-network → $1.14M OON (68% increase) ## Scenario 2: Hospital System Merger **Trigger**: Two systems merge, one in-network, other not. Demand single contract at higher rates. **Impact**: Typical demand: 12-20% rate increase or termination threat **Affects**: ER, admissions, outpatient surgery, imaging **Example**: System merger demands 15% increase on $2.8M spend = +$420K annual cost ## Scenario 3: Facility Closure **Trigger**: Local ASC closes or hospital converts to urgent care only **Impact**: Utilization redistributes to remaining facilities (often higher cost) **Example**: Low-cost ASC closes → procedures shift to hospital HOPD (facility fee 2.5× higher) ## Scenario 4: Geographic Network Gap **Trigger**: Satellite office employees (80+ people) distant from in-network facilities **Impact**: Employees use local out-of-network hospital → all OON claims **Solution Options**: 1. Model cost of status quo OON leakage 2. Negotiate direct contract with local facility 3. Switch to broader network carrier ## Scenario 5: TPA/Carrier Network Change **Trigger**: TPA loses contract with major hospital system mid-year **Impact**: Facilities previously in-network suddenly OON without notice **Example**: Regional hospital drops TPA → $1.6M annual utilization now OON
Identify provider concentration risk. Model exit scenarios. Build mitigation strategies (direct contracts, network RFPs, alternative sites) before the 90-day notice arrives.
Run Network Risk Analysis→