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Modeling Patient Survivability for Emergency Medical Service Systems

Modeling Patient Survivability for Emergency Medical Service Systems. Laura A. McLay Virginia Commonwealth University Statistical Sciences & Operations Research lamclay@vcu.edu 4 October 2007 In conjunction with the Hanover Fire and EMS Department in Hanover County, Virginia. Motivation.

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Modeling Patient Survivability for Emergency Medical Service Systems

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  1. Modeling Patient Survivability for Emergency Medical Service Systems Laura A. McLay Virginia Commonwealth University Statistical Sciences & Operations Research lamclay@vcu.edu 4 October 2007 In conjunction with the Hanover Fire and EMS Department in Hanover County, Virginia

  2. Motivation • Goal: design next-generation emergency medical service (EMS) systems that • deliver advanced medical care quickly • save lives • Optimization models for EMS systems straightforward if goal is to deliver medical care quickly • Optimization models for saving lives not so clear • Agenda: introduce a new approach to modeling patient survivability Emergency Medical Service Systems Laura A. McLay

  3. Emergency Medical Service (EMS) Systems • EMS systems measured according to how they respond to cardiac arrest (CA calls) • CA victims have 1-8% chance of survival • CAs cause 400,000 – 460,000 deaths per year • Ambulances employ either • Paramedics (ALS) • Emergency medical technicians (BLS) • Most EMS systems in the US have moved from BLS to ALS since the 1960s • CAs motivated change • Development of CPR and defibrillators Emergency Medical Service Systems Laura A. McLay

  4. EMS Models • Why not redesign EMS systems to optimize patient survivability? • Focus on CA 911 calls • What does the medical community know? • What helps CA patients? • Early bystander intervention • Early CPR • Defibrillators at scene in 4 minutes • What doesn’t? • Paramedics at scene in 8 minutes (as opposed to basic medical care) Emergency Medical Service Systems Laura A. McLay

  5. 80% CDF of calls for service covered 9 Time in minutes Why don’t paramedics save lives? • System designed to cover 80% of calls for service in 9 minutes • Rule of thumb for survivability: • 90% survival rate if defibrillation within one minute • Survival reduces about 10% every minute thereafter Emergency Medical Service Systems Laura A. McLay

  6. Maximizing Patient Survivability • Traditional models for EMS systems maximize a proxy for patient survivability • cover the most area possible in a given amount of time • cover the largest population in a given amount of time • cover the most calls for service in a given amount of time • Objective: Directly tie ambulance service to patient outcomes • Why hasn’t this been done before??? • EMS systems designed prior to the information age Emergency Medical Service Systems Laura A. McLay

  7. Maximizing Survivability • Objective: Directly tie ambulance service to patient outcomes • Traditional operations research optimization models for EMS systems maximize a proxy for patient survivability • Examples: • cover the most area possible in a given amount of time • cover the largest population in a given amount of time • cover the most calls for service in a given amount of time • Does this “just in time” modeling approach save lives? Emergency Medical Service Systems Laura A. McLay

  8. Response time (EMS) Cardiac arrest Ambulance dispatched Ambulance arrives at scene Defibrillation or care provided 911 call Anatomy of a 911 call • Defibrillation should occur within six minutes from CA • Response time measures time from ambulance dispatch, not time from CA Response time (patient) Emergency Medical Service Systems Laura A. McLay

  9. 80% CDF of calls for service covered 9 Time in minutes Maximize CA patient survivability • Not all ways of reaching 80% coverage are equal • System that responds robustly to CA calls will respond well to all calls Emergency Medical Service Systems Laura A. McLay http://images.jupiterimages.com/common/detail/89/11/22251189.jpg http://www.clipartheaven.com/clipart/holidays/halloween/tombstone-clipart.gif

  10. Existing OR models for EMS • Goal of operations research models to determine • what type of resources (ambulances) to purchase • where to place ambulances • how to staff ambulances • how to dispatch ambulances • how to accurately measure time traveling and other parameters • Operations research methods • Simulation, optimization, queuing • Issues considered • Busy vehicles, back-up coverage, vehicle types, dynamic issues Emergency Medical Service Systems Laura A. McLay

  11. Existing OR models for EMS, cont’d • Much research in 1970s and 1980s • No CAD systems, dispatch centers pencil and paper • Data difficult to obtain so reasonable assumptions made • Information age in 1990s and beyond • CAD systems in dispatch centers collect lots of data • Patient billing data links EMS to patient outcomes • The proxies for patient survivability don’t do what we want them to do Emergency Medical Service Systems Laura A. McLay

  12. Case study: Hanover County, Virginia Anecdotes from an ambassador to the EMS community Emergency Medical Service Systems Laura A. McLay

  13. Hanover County map • The basics: • Population = 100,000 • Area = 474 mi2 • 70% rural with small pockets of suburbs • EMS a branch of the Fire Department • EMS all volunteer-run (BLS) until recently • Staff (ALS) work on weekdays Emergency Medical Service Systems Laura A. McLay

  14. Hanover County Goals • Their goals: • Cover 80% of calls within 9 minutes (currently covering ~50% of calls) • Understand if not meeting goal due to geography or insufficient resources • Decide which resources to purchase • My goals: • Is covering 80% of calls within 9 minutes really the goal? • Is 80% coverage realistic in a semi-rural county? • Are they measuring what they really need to measure to reach their goals? Emergency Medical Service Systems Laura A. McLay

  15. Response Time: Stopping the Clock • Priority 1 (life threatening) calls require ALS response (60% of calls) • 24% of calls could potentially be CAs • 11% of calls are “Chest Pain/Heart Problems” • 13% of calls are “Breathing Difficulty” • Double coverage by BLS ambulance or fire truck if ALS not immediately available (12% of calls) • Response time defined when ALS arrives • Issue: models depends on response time • Can we stop the clock when the first responder arrives? Emergency Medical Service Systems Laura A. McLay

  16. The Problem is Complex • Vehicles that can respond to calls • ALS ambulance [2 people] • BLS ambulance [2 people] • ALS QRV (non-transport unit) [1 person] • Fire truck (BLS) [3 people] • Police car (AED) [1 person] • Two types of vehicles = hard problem • Five types of vehicle = great problem • Impact out-of-service times, response times, service times, turnaround times. Emergency Medical Service Systems Laura A. McLay

  17. Final messages • The dispatch center and CAD system are backbone of entire EMS system • Software constrains how systems works • Constant customer interaction and feedback • Need input from (real) doctors • It’s truly a systems problem • Police cars have defibrillators • Other counties rely on Hanover County EMS • Be a good first responder—learn CPR Emergency Medical Service Systems Laura A. McLay

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