Modeling Patient Survivability for Emergency Medical Service Systems

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Modeling Patient Survivability for Emergency
Medical Service Systems

• Laura A. McLay
• Virginia Commonwealth University
• Statistical Sciences Operations Research
• lamclay_at_vcu.edu
• 4 October 2007
• In conjunction with the Hanover Fire and EMS
  Department in Hanover County, Virginia

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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

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

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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 doesnt?
• Paramedics at scene in 8 minutes (as opposed to
  basic medical care)

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Why dont 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

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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 hasnt this been done before???
• EMS systems designed prior to the information age

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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?

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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)
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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

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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

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Existing OR models for EMS, contd

• 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 dont do
  what we want them to do

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Case study Hanover County, Virginia
Anecdotes from an ambassador to the EMS community
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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

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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?

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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?

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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.

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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
• Its truly a systems problem
• Police cars have defibrillators
• Other counties rely on Hanover County EMS
• Be a good first responderlearn CPR