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Improving Access to Acute Care

Improving Access to Acute Care. Using computer simulation & multi-objective goal programming techniques. Collaborators. John-Paul Oddoye Ph.D thesis Prof Mehrdad Tamiz and Dr Dylan Jones Management Mathmatics Group, UoP Dr Paul Schmidt, PHT & UoP Clinical supervisor. Simulation.

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Improving Access to Acute Care

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

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

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

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

  5. Linking to: Quality Improvement tools DEFINE MEASURE ANALYSE IMPROVE CONTROL Lean – focuses on value-added work and eliminating waste VALUE DEMAND FLOW RESOURCES EFFICIENCY and SPEED Six Sigma – focuses on eliminating defects and reducing variation in processes EFFECTIVENESS

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

  7. Model description

  8. T P Model Description Sub-networks Tactical nodes Probability nodes

  9. Data Collection

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

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

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

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

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

  15. Testing Scenarios: Consultant WR 1 WR/day 2 WRs/day

  16. Six Sigma % Defects Sigma Score 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 • Suggest optimal solutions for resolving sometimes conflicting objectives: Cost-effectiveness, staffing, patient and staff satisfaction and bed use • Systematic improvement • Flexible tool – many future uses

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