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An EMS Medley

An EMS Medley. August 8, 2008. Agenda. Background Case Studies Key Findings. EMS Projects 2001-2008. Edmonton Single Start Station – Simulation Station Location – St Albert Performance Improvement – Calgary Flexing/SSM/Redeployment – Edmonton/Calgary

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An EMS Medley

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  1. An EMS Medley August 8, 2008

  2. Agenda • Background • Case Studies • Key Findings

  3. 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

  4. Sources of Reduced Performance • Road congestion • Call growth • Suburban sprawl • Hospital Locations Structural 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

  5. 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…

  6. 459,501

  7. 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

  8. Overall Performance % Response < 8min Performance

  9. Call growth Calls have been growing at a 9.8% compound annual rate since 2000. Weekly Call Volumes EMS Calls – 9.8% CAGR Calls Population (000s) – 1.6% CAGR 2000 2001 2002 2003

  10. Call patterns – Geographic EMS Call Growth Call Forecasting Response Times The Ambulator Call growth has been concentrated in the periphery. Growth 8.5% 5.8% Periphery Downtown 2000 2001 2002 2003 2004 Priority 1 calls from all years.

  11. Call growth Growth is continuing at around 10% per year. Monthly Calls by Priority Priority CAGR 1 10% Calls 2 10% 3 3% 4 10% 5-7 6% 2000 2001 2002 2003 2004 2005

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

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

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

  15. Call Forecasting Calls arrive in distinct patterns Yearly Call Pattern (Detrended) Jan Apr Jul Oct Weekly Call Pattern Sun Mon Tues Wed Thurs Fri Sat

  16. Geo-coding Call Locations Dispatch Locations

  17. Where Are Calls Overgoal Station Zones 2004

  18. Legend: Late Calls 250 180 90 30 2000 2001 2002 2003 2004 2005 Year 2000

  19. Legend: Late Calls 250 180 90 30 2000 2001 2002 2003 2004 2005 Year 2001

  20. Legend: Late Calls 250 180 90 30 2000 2001 2002 2003 2004 2005 Year 2002

  21. Legend: Late Calls 250 180 90 30 2000 2001 2002 2003 2004 2005 Year 2003

  22. Legend: Late Calls 250 180 90 30 2000 2001 2002 2003 2004 2005 Year 2004

  23. Legend: Late Calls 250 180 90 30 2000 2001 2002 2003 2004 2005 Year 2005

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

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

  26. Hospital time Time spent waiting at the hospital nearly tripled from 2000-2005 Hours/Month Data from 2000-2005 – all calls.

  27. So what did they do? UNSUCCESSFUL • 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 ???

  28. 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

  29. Speed Calculations Secondary Residential Primary 50 km/h 40 km/h 32 km/h Stochastic Model – Travel Times 2 km • 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: 0.5 km 1.5 km 1.5 km 1.5 km

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

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

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

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

  34. X 120 calls = 114 calls 130 calls X = 104 calls X 40 calls = 24 calls Total Calls: 290 Total Covered: 242 Illustrative Stochastic model Covered in 9 min 95% of the time Station Covered in 9 min 80% of the time Covered in 9 min 60% of the time Coverage Rate = 83%

  35. Primary Secondary Residential Current (2002)

  36. Primary Secondary Residential 2005

  37. Primary Secondary Residential 2010

  38. Primary Secondary Residential 2015

  39. 2005 2010 (2015) 2005 2010 2015 Calls 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% Station Recommendations 2002

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

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

  42. Response Times vs. Heart Attack Mortality

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

  44. Models Uncertain ambulance availability Uncertain response time Budge,Ingolfsson,Erkut(2005)

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

  46. The Ambulator Model Forecast Engine Calculation Engine Deployment Engine Scheduling Engine Forecastor Tabu Solver Schedulator Calculator 2 minutes to calculate 15 minutes to Calculate 4,000 hours to calculate • Input • Date/Time • Output • Call Demand • Busy Time • Response Time • Input • Available units • Output • “Optimal” deployment • Input • Optimization output • Available shifts • Output • Optimal schedule • Input • Forecast outputs • Unit Deployment • Output • Coverage

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

  48. ~1% drop per 1000 Calls (July – 2005)

  49. Schedule & Fleet Size Old vs. Optimal Schedule - 2005 Performance Total Improvement of 1.4% Note – schedule and performance based on 2004.

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