Modelling Emergency Medical Services
description
Transcript of Modelling Emergency Medical Services
Modelling Emergency Medical Services
Paul Harper, Vince Knight, Janet Williams
Leanne Smith, Julie Vile, Jonathan Gillard, Israel Vieira
Response Location
600
700
800
900
1000
1100
1200
1300
1400
1500
Forecasting
Response Location
600
700
800
900
1000
1100
1200
1300
1400
1500
Forecasting
Data & Demand Patterns
600
700
800
900
1000
1100
1200
1300
1400
1500
WAST daily demand (01/04/2005-31/12/2009)
Forecasts for December
950
1000
1050
1100
1150
1200
1250True DemandSSAHolt-WintersARIMA
Response Location
600
700
800
900
1000
1100
1200
1300
1400
1500
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
600
700
800
900
1000
1100
1200
1300
1400
1500
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