Improving Access Improving Access to Acute Careto Acute Care

Using computer simulation & Using computer simulation & multi-objective goal programming multi-objective goal programming

techniquestechniques

CollaboratorsCollaborators

John-Paul OddoyeJohn-Paul Oddoye• Ph.D thesisPh.D thesis

Prof Mehrdad Tamiz and Dr Dylan JonesProf 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

SimulationSimulation

Useful in complex non-linear systems Useful in complex non-linear systems where behaviour is difficult to predictwhere behaviour is difficult to predict

Cost-effective exploration of Cost-effective exploration of scenariosscenarios

Identification of critical rate-limiting Identification of critical rate-limiting stepssteps

Goal programmingGoal programming

Complementary to simulationComplementary to simulation

““Multi-objective” - Multi-objective” - • Reconciliate divergent goals and outputsReconciliate divergent goals and outputs

Assign a relative weighting to goalsAssign a relative weighting to goals• Allows priorities to be recognisedAllows priorities to be recognised

Trade-off analysisTrade-off analysis

Linking to: Quality Improvement toolsLinking to: Quality Improvement toolsLean – focuses on value-added work and eliminating waste

Six Sigma – focuses on eliminating defects and reducing variation in processes

DEFINE MEASURE ANALYSE IMPROVE CONTROL

VALUE DEMAND FLOW RESOURCES EFFICIENCY and SPEED

EFFECTIVENESS

MethodologyMethodology

Model Description processModel Description process Activities and RolesActivities and Roles Dependencies and Competing ActivitiesDependencies and Competing Activities Networks and Sub-networksNetworks and Sub-networks Tactical and Probabilistic nodesTactical and Probabilistic nodes PoliciesPolicies

Data CollectionData Collection Demand generatorDemand generator Activity time-and-motion studiesActivity time-and-motion studies

Model training Model training Model validationModel validation

Model descriptionModel description

Model DescriptionModel Description

Sub-networks

Probability nodes

Tactical nodes

T

P

Data CollectionData Collection

Model ValidationModel Validation

Comparison to real patients flowsComparison to real patients flows• Length of stay (LOS)Length of stay (LOS)

Model ValidationModel Validation

Comparison to real patients flowsComparison to real patients flows• Length of stay (LOS)Length of stay (LOS)• Queue lengthsQueue lengths

Model ValidationModel Validation

Comparison to real patients flowsComparison to real patients flows• Length of stay (LOS)Length of stay (LOS)• Queue lengthsQueue lengths• Queue waitsQueue waits

Testing Scenarios: Bed numbersTesting Scenarios: Bed numbers

Increase number of beds: Increase number of beds:

Decrease beds – 55: massive Decrease beds – 55: massive increase in waits and queue lengthsincrease in waits and queue lengths

Testing Scenarios: Bed numbersTesting Scenarios: Bed numbers

FinetuningFinetuning

• Impact on Staff

Testing Scenarios: Consultant WRTesting Scenarios: Consultant WR1 WR/day 2 WRs/day

Six SigmaSix Sigma

6.06.0 0.00034%0.00034%

4.04.0 0.6%0.6%

2.02.0 30.8%30.8%

1.51.5 50%50%

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%

SummarySummary

Evaluate use of our main resources: Evaluate use of our main resources: beds, nurses and doctors timebeds, nurses and doctors time

Suggest optimal solutions for Suggest optimal solutions for resolving sometimes conflicting resolving sometimes conflicting objectives:objectives:Cost-effectiveness, staffing, patient and Cost-effectiveness, staffing, patient and

staff satisfaction and bed usestaff satisfaction and bed use Systematic improvementSystematic improvement Flexible tool – many future usesFlexible tool – many future uses