an ems medley august 8, 2008. agenda background case studies key findings
TRANSCRIPT
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An EMS Medley
August 8, 2008
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Agenda
• Background• Case Studies• Key Findings
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EMS Projects 2001-2008
• Edmonton Single Start Station – Simulation• Station Location – St Albert• Performance Improvement – Calgary • Flexing/SSM/Redeployment – Edmonton/Calgary• Regionalization – Calgary & Edmonton Regions• Call Forecasting – Seattle
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Sources of Reduced Performance
Structural
– Road congestion– Call growth– Suburban sprawl– Hospital Locations
Strategic Operational
– Station location– Long hospital waits– Insufficient units– Insufficient station
capacity– Insufficient
stations– Insufficient crews
– Poor scheduling– Long setup times– Long patient times– Redeployment gaps– Improper call
evaluation– Poor dispatch policies– Poor redeployment
policies
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Data Available
• Call records (time stamps, priority, locations)• Event records (shift starts and ends, moves)• Scheduled units• AVL (10 second location and state data)
• Messy…
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459,501
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Calgary Project – Performance Improvement
Calgary EMS – How Can we Improve?• 2005 Population – ~950K (2.4% growth)• 24 current stations • 721 km2 geographic area• Up to 42 ambulances at peak time• ~15,000 P1 calls - ~95,000 total calls in last year
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Overall Performance
81%82%81%
77% 76%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
100%
2000 2001 2002 2003 2004
% Response < 8min
Performance
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Call growth
Weekly Call Volumes
0
200
400
600
800
1000
1200
1400
1600
1800
2000 2001 2002 2003
Calls
Calls have been growing at a 9.8% compound annual rate since 2000.
Population (000s) – 1.6% CAGR
EMS Calls – 9.8% CAGR
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Call patterns – GeographicCall growth has been concentrated in the periphery.
0
200
400
600
800
1000
1200
2000 2001 2002 2003 2004
Downtown
Periphery
Priority 1 calls from all years.
Growth
8.5%
5.8%
EMS Call Growth
Call Forecasting
Response Times
The Ambulator
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Call growth
Monthly Calls by Priority
0
1000
2000
3000
4000
5000
6000
7000
8000
2000 2001 2002 2003 2004 2005
Priority CAGR
1
2
3
4
5-7
10%
10%
3%
10%
6%
Growth is continuing at around 10% per year.
Calls
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Priority one calls
Other29%
Cardiac18%
Convulsions6%
Unconscious12%Traffic
12%
Fall6%
Breathing17% Breathing
Cardiac
Unconscious
Traffic
Fall
Convulsions
Other
CAGR
5%5%
0%1%1%6%20%
The largest categories within priority one are growing more slowly
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Call forecasting
0
2
4
6
8
10
12
0.00 4.00 8.00 12.00 16.00 20.00 24.00
Average Hourly Calls
An average of ~180 calls arrive in one day.
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Call forecasting
• But daily totals vary due to other effects (weather, etc.)
020406080
100120140160
120 140 160 180 200 220 240 260
Distribution of Daily Calls
Number of Calls
Frequency
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Call ForecastingCalls arrive in distinct patterns
Sun Mon Tues Wed Thurs Fri Sat
Weekly Call Pattern
Jan Apr Jul Oct
Yearly Call Pattern (Detrended)
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Geo-codingCall Locations Dispatch Locations
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Where Are Calls Overgoal (X
(X
(X
(X
(X
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(X
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(X
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(X(X
(X
(X
(X
(X
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2821
15 17
5
18
31
22
12
4
12
68
23
1120
1424
9
19
2630
Station Zones 2004
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(X(X
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5
18
31
22
12
4
12
68
23
1120
1424
9
19
2630250
180
90
30
Legend:
Late Calls
Year 2000
2000 2001 2002 2003 2004 2005
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2821
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5
18
31
22
12
4
12
68
23
1120
1424
9
19
2630250
180
90
30
Legend:
Late Calls
Year 2001
2000 2001 2002 2003 2004 2005
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(X(X
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2821
15 17
5
18
31
22
12
4
12
68
23
1120
1424
9
19
2630250
180
90
30
Legend:
Late Calls
Year 2002
2000 2001 2002 2003 2004 2005
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(X(X
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2821
15 17
5
18
31
22
12
4
12
68
23
1120
1424
9
19
2630250
180
90
30
Legend:
Late Calls
Year 2003
2000 2001 2002 2003 2004 2005
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(X(X
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2821
15 17
5
18
31
22
12
4
12
68
23
1120
1424
9
19
2630250
180
90
30
Legend:
Late Calls
Year 2004
2000 2001 2002 2003 2004 2005
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#S#S
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19
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12
22
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1120
4
12
6
8
18
5
1715
2821
250
180
90
30
Legend:
Late Calls
Year 2005
2000 2001 2002 2003 2004 2005
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Why Calls are Overgoal
Evaluate each call to see why it was overgoal:1. Chute problem2. Dispatch problem3. Call Evaluation problem4. Hospital problem5. Travel distance (from nearest two stations)6. Stations busy (nearest two)7. Driving problem (other travel factors)8. Multiple problems (combinations of the above)
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Why Calls are Overgoal
0
500
1,000
1,500
2,000
2,500
2000 2001 2002 2003 2004 2005
Call Eval ProblemDispatch ProblemHospital ProblemDriving ProblemDistance ProblemChute ProblemBusy-ness ProblemMultiple Problem
Total Late Calls by Year(Responses > 8 Minutes)
LateCalls
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Hospital time
0
500
1,000
1,500
2,000
2,500
3,000
3,500
Jan
-00
Ma
y-0
0
Se
p-0
0
Jan
-01
Ma
y-0
1
Se
p-0
1
Jan
-02
Ma
y-0
2
Se
p-0
2
Jan
-03
Ma
y-0
3
Se
p-0
3
Jan
-04
Ma
y-0
4
Se
p-0
4
Jan
-05
Ma
y-0
5
Foothills
Lougheed
Rockyview
Children's
Time spent waiting at the hospital nearly tripled from 2000-2005Hours/Month
Data from 2000-2005 – all calls.
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So what did they do?
• Lobby the Health Authority• Put paramedics in the hallway• Created drop-off protocol “Full Capacity Protocol”• Divert drop-offs to less busy hospitals
UNSUCCESSFUL
UNSUCCESSFUL
MIXED
???
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St. Albert Station Location
St. Albert Fire – Where do we put new stations through 2015?
• 2003 Population - ~50,000• 2 station• Combined fire/EMS• ~2500 calls per year
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Stochastic Model – Travel Times
Speed Calculations
•We derived a ‘most-likely route’
•We calculated distances using this routing scheme
•Using actual travels times, we calculated average speeds for 3 types of roads:
SecondaryPrimary
Residential
50 km/h 40 km/h 32 km/h
1.5 km
2 km
0.5 km
1.5 km
1.5 km
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Stochastic model – travel timesAlthough covered on average – this node is not reached 2% of the time in 9 minutes
0
10
20
30
40
50
60
70
Actual Travel Times for 34 Mission Ave (361 Incidents)
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Stochastic model – travel time distributions
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
Modeled Travel Time (mean = 3 min.)
0
10
20
30
40
50
60
70
Actual Travel Times for 34 Mission Ave (361 Incidents)
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Coverage analysis – probabilistic coverage
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
Total Time(Dispatch Time + Activation Time + Travel Time)
Nine minute target response
98% of the instances are to the left of 9 min
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Coverage analysis – probabilistic coverage
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
Total Time(Dispatch Time + Activation Time + Travel Time)
Nine minute target response
69% of the instances are left of 9 minutes
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Stochastic model
Station
Illustrative
Covered in 9 min 95% of the
time
Covered in 9 min 80% of the
time
Covered in 9 min 60% of the
time
X
X
X
120 calls
130 calls
40 calls
=
=
Total Calls: 290
=
114 calls
104 calls
24 calls
Total Covered: 242
Coverage Rate = 83%
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Current (2002)
Primary
Secondary
Residential
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2005
Primary
Secondary
Residential
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2010
Primary
Secondary
Residential
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2015
Primary
Secondary
Residential
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Station Recommendations2002 2005 2010 (2015)
2005 2010 2015Calls 92.9% 91.2% 91.8%EMS 93.1% 91.5% 92.0%FIRE 92.0% 90.3% 90.8%
VALUE 91.8% 89.9% 90.4%
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Expected Performance
% Calls Covered in 9 Minutes
Year
Coverage Through Time in St. Albert (projected calls)
80%
82%
84%
86%
88%
90%
92%
94%
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
80%
82%
84%
86%
88%
90%
92%
94%
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
80%
82%
84%
86%
88%
90%
92%
94%
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
North station added
West station added
Calgary
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Results
• Mayor: “This is all well and good – percent improvements here and there – but what does this mean in terms of lives saved?
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Response Times vs. Heart Attack Mortality
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Adding Stations – Heart Attacks
Number of Heart Attack
Fatalities
Year
Expected Heart Attack Fatalities
12
14
16
18
20
22
24
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
12
14
16
18
20
22
24
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
West station added
East station added
On average, we save an additional life every year.
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Models
Covering
(1974) (1983)
(1987)
Uncertain ambulance availability
Uncertain response time
Budge,Ingolfsson,
Erkut(2005)
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Base demand(P1 + all)
Growth FactorDay of Week
indexHour of Week by
Region (GC)DEMAND
Drive times base Growth Factor Rush hour index DRIVE TIMES
Chute average base
Chute average index (hour by stn)
CHUTE AVERAGE
Chute variance base
Chute variance index (hour by stn)
CHUTE VARIANCE
Busy times base Growth factor Busy times indexindex
RESPONSE TIMES
RESPONSE TIMES STDEV
STATION BUSY TIMES
STATION PREFERENCE
RESPONSE PROBABILITY
CALCULATION ENGINE
COVERAGE STATISTICS
Response cut off (n)
Servers (ambulances)
Date/Time Input
Month of Year by Region
(GC)
Hour of Day Index
ASSIG PROB
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The Ambulator Model
Forecast Engine
Input
•Date/Time
Output
•Call Demand
•Busy Time
•Response Time
Calculation Engine
Input
•Forecast outputs
•Unit Deployment
Output
•Coverage
Deployment Engine
Input
•Available units
Output
•“Optimal” deployment
Scheduling Engine
Input
•Optimization output
•Available shifts
Output
•Optimal schedule
Forecastor Calculator Tabu Solver Schedulator
15 minutes to
Calculate
4,000 hours to calculate
2 minutes to calculate
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Hospital Time Improvement
65%
70%
75%
80%
85%
90%
0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00
Performance w/ 30 Minute HT
Performance w/ Current HT
Total Improvement of 2.3%
Performance(Average total daily performance)
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Adding P1 Calls
70%
72%
74%
76%
78%
80%
0% (0 calls) 5% (745 calls) 10% (1489calls)
15% (2234calls)
20% (2979calls)
Additional P1 Calls
Co
veag
e
(July – 2005)
~1% drop per 1000 Calls
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65%
70%
75%
80%
85%
0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00
Baseline 2005
New Schedule
Schedule & Fleet Size
20
25
30
35
40
45 Baseline
New Schedule
Note – schedule and performance based on 2004.
Old vs. Optimal Schedule - 2005
Performance
76.8%78.2%
Total
Total Improvement of 1.4%
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Shift FlexibilityHow does performance change when we adjust shift flexibility?
Performance
Unlimited fleet size
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Fleet SizeHow does performance change when we adjust fleet size?
60.0%
61.0%
62.0%
63.0%
64.0%
65.0%
66.0%
67.0%
68.0%
69.0%
70.0%
44 43 42 41 40 39 38 37 36 35 34 33 32
876 Unit Hours
828 Unit Hours
780 Unit Hours
Fleet Size
Performance
1hr shifts
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Unit HoursAn additional 24hr unit is 0.8% improvement at 780 unit hours
60%
61%
62%
63%
64%
65%
66%
67%
68%
69%
70%
71%
72%
720 744 768 792 816 840 864 888 912 936 960
Unit Hours
Performance
![Page 54: An EMS Medley August 8, 2008. Agenda Background Case Studies Key Findings](https://reader036.vdocuments.us/reader036/viewer/2022062322/56649ea05503460f94ba28f1/html5/thumbnails/54.jpg)
Station Expansion
(X(X
(X
(X
(X
(X
(X
(X
(X
(X
(X(X
(X
(X
(X
(X
(X
(X
(X
(X
(X
(X
(X
2821
15 17
5
18
31
22
12
4
12
68
23
1120
1424
9
19
2630
+1+2
Total Improvement of
~1%
![Page 55: An EMS Medley August 8, 2008. Agenda Background Case Studies Key Findings](https://reader036.vdocuments.us/reader036/viewer/2022062322/56649ea05503460f94ba28f1/html5/thumbnails/55.jpg)
Additional Discoveries
From the Model• Adding the next best station – 1% improvement• Adding additional unit to the best station – 0.8%
improvement• Cutting patient time (scene, transport, or hospital) by 4
minutes – 1%From the Data• Cutting response time (call eval, dispatch, chute, or
travel) by 6 seconds yields 1%• Own-zone performance shows if stations are
necessary…or perhaps call eval/dispatch/chute improvements
![Page 56: An EMS Medley August 8, 2008. Agenda Background Case Studies Key Findings](https://reader036.vdocuments.us/reader036/viewer/2022062322/56649ea05503460f94ba28f1/html5/thumbnails/56.jpg)
Where are we going?
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
Performance Improvement
Cost
Improved Dispatch
Additional Crews
Additional Stations
Expanded Stations
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Things learned
• Pretty counts• Quick is as important as accurate• Data analysis is often more beneficial than modeling• Rules of thumb