Modelling Emergency Medical Services

Modelling Emergency Medical Services

Paul Harper, Vince Knight, Janet Williams

Leanne Smith, Julie Vile, Jonathan Gillard, Israel Vieira

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Forecasting

Response Location

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Forecasting

Data & Demand Patterns

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WAST daily demand (01/04/2005-31/12/2009)

Forecasts for December

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1250True DemandSSAHolt-WintersARIMA

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Forecasting

Time-dependency

Demand per Shift

Time-dependent Queues

If all servers are busy and only Category B/C patients are in the system, the equilibrium conditions for the state triple S=[i,h,l] are given by:

( ) [0, , ] [0, 1, ] [0, , 1], 0 , 0( ) [0, ,0] [0, 1,0], 0( ) [0,0, 1] [0,0, 1] [0,0, 1] [1,0, 1], 0( ) [0,0,0] [0,0,1] [1,0,1] [0, 1]

0,1, ,

L H L

L H

L L L H

L L H L

s P h l P h l P h l h ls P h P h hs P l P l s P l P l ls P s P P P s

i s

is the number of Category A patients in service; , 0,1,2, are the number of Category A and B / C patients in the queue respectivelyh l

Staffing

Shift Patterns

OBJECTIVES:Minimise labour hoursMinimise crew sizeMinimise overtime

CONSTRAINTS:Max weekly working hoursMax night time hoursRest breaks / days off

Week 1 Week 2Crew 1 2 3 4 5 6 7 1 2 3 4 5 6 7

1 A N N M M M A N

2 A M A N N M

3 N A M M M A A N

4 M M A M M M M A A A

Spreadsheet Tool

Response Location

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Forecasting

Travel Times - Google Maps API

Location Analysis

Location AnalysisEAs

RRVs

Computer Simulation

‘What if?’ Scenarios

Alter demand (e.g. increase by 10%)

Major event

Change in overall fleet capacity

Determine vehicle allocations given different fleet capacities

Reduce turnaround time

Illustrative Results