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Policy Scenarios

This page provides comparison tables for key policy intervention scenarios modelled in the toolkit.

Intervention Comparison

Overview Table

Intervention Mechanism Target Game Expected Effect
Pooled Funding Reduce cost shifting incentives Cost Shifting ↓ Fragmentation
UCC Integration Unified governance Governance Integration ↑ Coordination
Transparency Initiative Increase political salience Definition, Bargaining ↑ Accountability
Audit Intensification Increase audit pressure Coding/Audit, Compliance ↓ Upcoding
Discharge Coordination Structured handoffs Discharge, Aged Care, NDIS ↓ Delays

Detailed Scenario Analysis

1. Pooled Funding

Objective: Reduce cost shifting by removing fee-for-service incentives.

Parameter Baseline Intervention Change
cost_shifting_intensity 0.35 0.15 -57%
Expected equilibrium (S, S) (I, I) → Invest

Mechanism: Lower CSI shifts the Cost Shifting game equilibrium from (Shift, Shift) to (Invest, Invest) by reducing the payoff advantage of cost shifting.

# Code example
pooled = Params(cost_shifting_intensity=0.15)
results = run_simulation(years=10, params=pooled)

Expected Outcomes:

  • ↓ Downstream pressure
  • ↓ ED presentations from cost shifting
  • ↑ Primary care investment

2. UCC Integration

Objective: Implement Urgent Care Clinics with unified governance.

Parameter Baseline Intervention Change
fragmentation_index 1.0 0.7 -30%
Expected equilibrium (S, S) (I, I) → Integrate

Mechanism: UCC integration reduces fragmentation risk in the Governance Integration game, making integration the dominant strategy.

ucc = Params(fragmentation_index=0.7)
results = run_simulation(years=10, params=ucc)

Expected Outcomes:

  • ↓ ED overcrowding
  • ↑ Low-acuity diversion
  • ↑ Coordination of care

3. Transparency Initiative

Objective: Increase public visibility of system performance.

Parameter Baseline Intervention Change
political_salience 0.3 0.7 +133%
Expected equilibrium Variable (R, R) → Realism

Mechanism: Higher political salience increases the cost of "efficiency theatre" in the Definition game, pushing toward realistic acknowledgment of cost pressures.

transparency = Params(political_salience=0.7)
results = run_simulation(years=10, params=transparency)

Expected Outcomes:

  • ↑ Pressure to acknowledge cost reality
  • ↓ Efficiency gap drift
  • ↑ Evidence-based policy

4. Audit Intensification

Objective: Reduce upcoding through stricter compliance monitoring.

Parameter Baseline Intervention Change
audit_pressure 0.3 0.7 +133%
Expected equilibrium (U, L) (H, T) → Honest/Tight

Mechanism: Higher audit pressure in the Coding/Audit game increases the penalty for upcoding, shifting equilibrium toward honest coding.

audit = Params(audit_pressure=0.7)
results = run_simulation(years=10, params=audit)

Expected Outcomes:

  • ↓ Upcoding prevalence
  • ↑ ABF accuracy
  • ↓ Revenue leakage (short-term)

Trade-off

High audit pressure increases administrative burden. Optimal level depends on efficiency gap magnitude.

Scenario Comparison Matrix

Effect on System Pressure

Scenario Year 1 Year 5 Year 10 Direction
Baseline 1.00 1.08 1.15 ↑
Pooled Funding 1.00 0.98 0.95 ↓
UCC Integration 1.00 1.02 1.05 →
Transparency 1.00 1.05 1.10 ↗
Audit Intensification 1.00 1.06 1.12 ↗

Equilibrium Shift Summary

Scenario Games Affected Equilibrium Change
Pooled Funding Cost Shifting (S,S) → (I,I)
UCC Integration Governance (S,S) → (I,I)
Transparency Definition, Bargaining Various → (R,R), (A,A)
Audit Intensification Coding/Audit, Compliance (U,L) → (H,T)

Combined Interventions

Pooled Funding + UCC Integration

Combining structural reforms can produce synergistic effects:

combined = Params(
    cost_shifting_intensity=0.15,
    fragmentation_index=0.7
)
results = run_simulation(years=10, params=combined)
Metric Baseline Pooled Only UCC Only Combined
Pressure (Y10) 1.15 0.95 1.05 0.88
Coordination Low Medium Medium High
Cost Shifting High Low High Low

Parameter Sensitivity

Key Levers

Based on Sobol sensitivity analysis:

Parameter Sensitivity (ST) Policy Leverage
cost_shifting_intensity 0.65 Very High
efficiency_gap 0.20 Medium
pressure (initial) 0.10 Low
political_salience 0.05 Low

Implication: Policy efforts targeting cost shifting mechanisms (e.g., pooled funding) have the highest leverage for reducing system pressure.

Running Your Own Scenarios

from nhra_gt.engine import Params, run_simulation

# Define custom scenario
my_scenario = Params(
    cost_shifting_intensity=0.25,
    political_salience=0.5,
    audit_pressure=0.4
)

# Run simulation
results = run_simulation(
    years=10,
    n_samples=500,
    params=my_scenario
)

# Analyse results
print(f"Final pressure: {results['pressure'].mean():.2f}")

See Also