Modelling Emergency Medical Services
description
Transcript of Modelling Emergency Medical Services
![Page 1: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/1.jpg)
Modelling Emergency Medical Services
Paul Harper, Vince Knight, Janet Williams
Leanne Smith, Julie Vile, Jonathan Gillard, Israel Vieira
![Page 2: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/2.jpg)
![Page 3: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/3.jpg)
Response Location
600
700
800
900
1000
1100
1200
1300
1400
1500
Forecasting
![Page 4: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/4.jpg)
Response Location
600
700
800
900
1000
1100
1200
1300
1400
1500
Forecasting
![Page 5: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/5.jpg)
Data & Demand Patterns
600
700
800
900
1000
1100
1200
1300
1400
1500
WAST daily demand (01/04/2005-31/12/2009)
![Page 6: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/6.jpg)
Forecasts for December
950
1000
1050
1100
1150
1200
1250True DemandSSAHolt-WintersARIMA
![Page 7: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/7.jpg)
Response Location
600
700
800
900
1000
1100
1200
1300
1400
1500
Forecasting
![Page 8: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/8.jpg)
Time-dependency
![Page 9: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/9.jpg)
Demand per Shift
![Page 10: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/10.jpg)
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
![Page 11: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/11.jpg)
Staffing
![Page 12: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/12.jpg)
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
![Page 14: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/14.jpg)
Response Location
600
700
800
900
1000
1100
1200
1300
1400
1500
Forecasting
![Page 15: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/15.jpg)
![Page 16: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/16.jpg)
![Page 17: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/17.jpg)
![Page 18: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/18.jpg)
![Page 20: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/20.jpg)
Location Analysis
![Page 21: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/21.jpg)
Location AnalysisEAs
RRVs
![Page 22: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/22.jpg)
Computer Simulation
![Page 23: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/23.jpg)
‘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
![Page 24: Modelling Emergency Medical Services](https://reader033.fdocuments.in/reader033/viewer/2022061600/5681631d550346895dd39608/html5/thumbnails/24.jpg)
Illustrative Results