1. Improving Access to Acute Care Using computer simulation & multi-objective goal programming techniques

2. Collaborators John-Paul Oddoye John-Paul Oddoye Ph.D thesisPh.D thesis Prof Mehrdad Tamiz and Dr Dylan Jones Prof Mehrdad Tamiz and Dr Dylan Jones Management Mathmatics Group, UoPManagement Mathmatics Group, UoP Dr Paul Schmidt, Dr Paul Schmidt, PHT & UoPPHT & UoP Clinical supervisorClinical supervisor

3. Simulation Useful in complex non-linear systems where behaviour is difficult to predict Useful in complex non-linear systems where behaviour is difficult to predict Cost-effective exploration of scenarios Cost-effective exploration of scenarios Identification of critical rate-limiting steps Identification of critical rate-limiting steps

4. Goal programming Complementary to simulation Complementary to simulation “Multi-objective” - “Multi-objective” - Reconciliate divergent goals and outputsReconciliate divergent goals and outputs Assign a relative weighting to goals Assign a relative weighting to goals Allows priorities to be recognisedAllows priorities to be recognised Trade-off analysis Trade-off analysis

5. Linking to: Quality Improvement tools Lean – focuses on value-added work and eliminating waste Six Sigma – focuses on eliminating defects and reducing variation in processes DEFINEMEASUREANALYSEIMPROVECONTROL VALUEDEMANDFLOWRESOURCESEFFICIENCY and SPEED EFFECTIVENESS

6. Methodology Model Description process Model Description process Activities and Roles Activities and Roles Dependencies and Competing Activities Dependencies and Competing Activities Networks and Sub-networks Networks and Sub-networks Tactical and Probabilistic nodes Tactical and Probabilistic nodes Policies Policies Data Collection Data Collection Demand generator Demand generator Activity time-and-motion studies Activity time-and-motion studies Model training Model training Model validation Model validation

7. Model description

8. Model Description Sub-networks Probability nodes Tactical nodes TP

9. Data Collection

10. Model Validation Comparison to real patients flows Comparison to real patients flows Length of stay (LOS)Length of stay (LOS)

11. Model Validation Comparison to real patients flows Comparison to real patients flows Length of stay (LOS)Length of stay (LOS) Queue lengthsQueue lengths

12. Model Validation Comparison to real patients flows Comparison to real patients flows Length of stay (LOS)Length of stay (LOS) Queue lengthsQueue lengths Queue waitsQueue waits

13. Testing Scenarios: Bed numbers Increase number of beds: Increase number of beds: Decrease beds – 55: massive increase in waits and queue lengths Decrease beds – 55: massive increase in waits and queue lengths

14. Testing Scenarios: Bed numbers Finetuning Finetuning Impact on Staff

15. Testing Scenarios: Consultant WR 1 WR/day2 WRs/day

16. Six Sigma 6.0 0.00034 % 4.00.6% 2.030.8% 1.550% Sigma Score % Defects P1 x P2 x P3 x P4 = Sigma score 0.80 x 0.7 x 0.75 x 0.9 = 0.375 DEFECT RATE = 62.5%

17. Summary Evaluate use of our main resources: beds, nurses and doctors time Evaluate use of our main resources: beds, nurses and doctors time Suggest optimal solutions for resolving sometimes conflicting objectives: Suggest optimal solutions for resolving sometimes conflicting objectives: Cost-effectiveness, staffing, patient and staff satisfaction and bed use Systematic improvement Systematic improvement Flexible tool – many future uses Flexible tool – many future uses