An EMS Medley August 8, 2008

Agenda Background Case Studies Key Findings

EMS Projects Edmonton Single Start Station – Simulation Station Location – St Albert Performance Improvement – Calgary Flexing/SSM/Redeployment – Edmonton/Calgary Regionalization – Calgary & Edmonton Regions Call Forecasting – Seattle

Sources of Reduced Performance Structural –Road congestion –Call growth –Suburban sprawl –Hospital Locations StrategicOperational –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

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…

459,501

Calgary Project – Performance Improvement Calgary EMS – How Can we Improve? 2005 Population – ~950K (2.4% growth) 24 current stations 721 km 2 geographic area Up to 42 ambulances at peak time ~15,000 P1 calls - ~95,000 total calls in last year

Overall Performance % Response < 8min Performance

Call growth Weekly Call Volumes Calls Calls have been growing at a 9.8% compound annual rate since Population (000s) – 1.6% CAGR EMS Calls – 9.8% CAGR

Call patterns – Geographic Call growth has been concentrated in the periphery Downtown Periphery Priority 1 calls from all years. Growth 8.5% 5.8% EMS Call Growth Call Forecasting Response Times The Ambulator

Call growth Monthly Calls by Priority PriorityCAGR % 3% 10% 6% Growth is continuing at around 10% per year. Calls

Priority one calls CAGR 5% 0% 1% 6% 20% The largest categories within priority one are growing more slowly

Call forecasting Average Hourly Calls An average of ~180 calls arrive in one day.

Call forecasting But daily totals vary due to other effects (weather, etc.) Distribution of Daily Calls Number of Calls Frequency

Call Forecasting Calls arrive in distinct patterns SunMonTuesWedThursFriSat Weekly Call Pattern JanAprJulOct Yearly Call Pattern (Detrended)

Geo-coding Call LocationsDispatch Locations

Where Are Calls Overgoal Station Zones 2004

Legend: Late Calls Year

Legend: Late Calls Year

Legend: Late Calls Year

Legend: Late Calls Year

Legend: Late Calls Year

Legend: Late Calls Year

Why Calls are Overgoal Evaluate each call to see why it was overgoal: 1.Chute problem 2.Dispatch problem 3.Call Evaluation problem 4.Hospital problem 5.Travel distance (from nearest two stations) 6.Stations busy (nearest two) 7.Driving problem (other travel factors) 8.Multiple problems (combinations of the above)

Why Calls are Overgoal Total Late Calls by Year (Responses > 8 Minutes) Late Calls

Hospital time Time spent waiting at the hospital nearly tripled from Hours/Month Data from – all calls.

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 MIXED ???

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

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: Secondar y Primary Residentia l 50 km/h40 km/h32 km/h 1.5 km 2 km 0.5 km 1.5 km

Stochastic model – travel times Although covered on average – this node is not reached 2% of the time in 9 minutes Actual Travel Times for 34 Mission Ave (361 Incidents)

Stochastic model – travel time distributions Modeled Travel Time (mean = 3 min.) Actual Travel Times for 34 Mission Ave (361 Incidents)

Coverage analysis – probabilistic coverage Total Time (Dispatch Time + Activation Time + Travel Time) Nine minute target response 98% of the instances are to the left of 9 min

Coverage analysis – probabilistic coverage Total Time (Dispatch Time + Activation Time + Travel Time) Nine minute target response 69% of the instances are left of 9 minutes

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%

Current (2002) Primary Secondary Residential

2005 Primary Secondary Residential

2010 Primary Secondary Residential

2015 Primary Secondary Residential

Station Recommendations (2015) Calls92.9%91.2%91.8% EMS93.1%91.5%92.0% FIRE92.0%90.3%90.8% VALUE91.8%89.9%90.4%

Expected Performance % Calls Covered in 9 Minutes Year Coverage Through Time in St. Albert (projected calls) North station added West station added Calgary

Results Mayor: “This is all well and good – percent improvements here and there – but what does this mean in terms of lives saved?

Response Times vs. Heart Attack Mortality

Adding Stations – Heart Attacks Number of Heart Attack Fatalities Year Expected Heart Attack Fatalities West station added East station added On average, we save an additional life every year.

Models Covering (1974) (1983) (1987) Uncertain ambulance availability Uncertain response time Budge, Ingolfsson, Erkut (2005)

Month of Year by Region (GC) Hour of Day Index ASSIG PROB

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 SolverSchedulator 15 minutes to Calculate 4,000 hours to calculate 2 minutes to calculate

Hospital Time Improvement Total Improvement of 2.3% Performance (Average total daily performance)

(July – 2005) ~1% drop per 1000 Calls

Schedule & Fleet Size Note – schedule and performance based on Old vs. Optimal Schedule Performance 76.8% 78.2% Total Total Improvement of 1.4%

Shift Flexibility How does performance change when we adjust shift flexibility? Performance Unlimited fleet size

Fleet Size How does performance change when we adjust fleet size? Fleet Size Performance 1hr shifts

Unit Hours An additional 24hr unit is 0.8% improvement at 780 unit hours Unit Hours Performance

Station Expansion Total Improvement of ~1%

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

Where are we going? Performance Improvement Cost Improved Dispatch Additional Crews Additional Stations Expanded Stations

Things learned Pretty counts Quick is as important as accurate Data analysis is often more beneficial than modeling Rules of thumb