Delivering emergency medical services:

research, application, and outreach

Laura A. McLay

Industrial & Systems Engineering

University of Wisconsin-Madison

lmclay@wisc.edu

punkrockOR.wordpress.com

@lauramclay

1This work was in part supported by the U.S. Department of the Army under Grant Award Number W911NF-10-1-0176 and

by the National Science Foundation under Award No. CMMI -1054148.

The road map

• How do emergency medical service (EMS) systems work?

• How do we know when EMS systems work well?

• How can we improve how well EMS systems work?

• Where is EMS OR research going?

• Where does EMS OR research need to go?

2

Emergency medical service (EMS) systems in a nutshell

• Originally designed to transport patients to hospital

• Medical advances allowed for more treatment of patients at the scene• E.g., Cardiopulmonary resuscitation and automated external

defibrillation for cardiac arrest patients

• OR application areas:

3

Healthcare

Transportation

Public sector

Anatomy of a 911 call

Response time

Service provider:

Emergency 911 callUnit

dispatchedUnit is en

routeUnit arrives

at sceneService/care

provided

Unit leaves scene

Unit arrives at hospital

Patient transferred

Unit returns to service

4

Response time from the patient’s point of view

EMS design at the local level

• Design varies by community• Fire and EMS vs. EMS

• Paid staff vs. volunteers

• Publicly run vs. privately run

• Emergency medical technician (EMT) vs. Paramedic (EMTp)

• Mix of vehicles

• Operations vary by community• Ambulance location, relocation, and relocation on-the-fly

• Operational guidelines (send closest unit)

• Jurisdictional issues regarding mutual aid

…one size doesn’t fit all

5McLay, L.A., 2011. Emergency Medical Service Systems that Improve Patient Survivability. Encyclopedia of Operations Research in the area of “Applications with Societal Impact,” John Wiley & Sons, Inc., Hoboken, NJ (published online: DOI: 10.1002/9780470400531.eorms0296)

Operationalizing recommendations

Additional recommendations from different national agencies regarding:

• Time to answer 911 call

• Time to send (dispatch) a unit to a call

• Response time / travel time

• The types of vehicles to send

Priority dispatch: Does not indicate which specific units to send

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Type Capability Response Time

Priority 1Advanced Life Support (ALS) Emergency

Send ALS and a fire engine/BLSE.g., 9 minutes

(first unit)

Priority 2Basic Life Support (BLS) Emergency

Send BLS and a fire engine if availableE.g., 13 minutes

Priority 3Not an emergency

Send BLSE.g., 16 minutes

Performance standards

• National Fire Protection Agency (NFPA) 1710 guidelines for departments with paid staff• 5 minute response time for first responding vehicle

• 9 minute response time for first advanced life support vehicle

• Must achieve these goals 90% of the time for all calls

• Similar guidelines for volunteer agencies in NFPA 1720 allow for 9-14 minute response times

• Guidelines based on medical research for cardiac arrest patients and time for structural fires to spread• Short response times only critical for some patient types:

cardiac arrest, shock, myocardial infarction

• Most calls are lower-acuity

• Many communities use different response time goals

7

Objective functions

• NFPA standard yields a coverage objective function for response time threshold (RTT)• Most common RTT: nine minutes for 80% of calls

• A call with response time of 8:59 is covered

• A call with response time of 9:00 is not covered

Why RTTs?

• Easy to measure

• Intuitive

• Unambiguous

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Response times vs. cardiac arrest survival

9

0

1.1

0 2 4 6 8 10 12 14

Response time (minutes)

Pro

ba

bilit

y o

f s

urv

iva

l

Larsen et al. 1993

Valanzuela et al. 1997

Waaelwijn et al. 2001

De Maio et al. 2003

9 Minute Standard

CDF of calls for service covered

Response time (minutes) 9

80%

What is the best response time threshold?

• Guidelines suggest 9 minutes

• Medical research suggests ~5 minutes• But this would disincentive 5-9 minute responses

• Which RTT is best for design of the system?

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What is the best response time threshold?

Research Goal: find the best RTT based on corresponding patient survival rates

• RTTs drive resource utilization decisions

• Optimize 4, 5, …, 12 minute RTT for high-priority patients

Decision context is locating and dispatching ALS ambulances

• Discrete optimization model to locate ambulances

• Markov decision process model to dispatch ambulances11

Ambulance Locations, N=7Best for patient survival / 8 Minute RTT

= one ambulance

= two ambulances

McLay, L.A. and M.E. Mayorga, 2010. Evaluating Emergency Medical Service Performance Measures. Health Care Management Science 13(2), 124 - 136

Suburban area –>(vs. rural areas)

<– Interstates

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Ambulance Locations, N=710 Minute RTT

= one ambulance

= two ambulances

McLay, L.A. and M.E. Mayorga, 2010. Evaluating Emergency Medical Service Performance Measures. Health Care Management Science 13(2), 124 - 136

13

Ambulance Locations, N=74-5 Minute RTT

= one ambulance

= two ambulances

McLay, L.A. and M.E. Mayorga, 2010. Evaluating Emergency Medical Service Performance Measures. Health Care Management Science 13(2), 124 - 136 14

Survival and dispatch decisions

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Across different ambulance configurations

Across different call volumes

McLay, L.A., Mayorga, M.E., 2011. Evaluating the Impact of Performance Goals on Dispatching Decisions in Emergency Medical Service. IIE Transactions on Healthcare Service Engineering 1, 185 – 196

Minimize un-survivability when altering dispatch decisions

Insights

• Response time thresholds are a good proxy for patient survival

• …but some response time thresholds (e.g., 7-9 minutes) are better than others

• Short response time thresholds based on what is best for individual patient survival are do not improve survival of the system

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Location and dispatching models

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Optimal dispatching policiesusing Markov decision process models

911 callUnit

dispatchedUnit is en

routeUnit arrives

at sceneService/care

provided

Unit leaves scene

Unit arrives at hospital

Patient transferred

Unit returns to service

Determine which ambulance to send based

on classified priority

Classified priority(H or L)

True priorityHT or LT

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Information changes over the course of a callDecisions made based on classified priority.Performance metrics based on true priority.

Classified customer riskMap Priority 1, 2, 3 call types to high-priority (𝐻) or low-priority (𝐿)Calls of the same type treated the same

True customer riskMap all call types to high-priority (𝐻𝑇) or low-priority (𝐿𝑇)

Under- or over-prioritize

• Assumption: No priority 3 calls are truly high-priority

Case 1: Under-prioritize with different classification accuracy

Case 2: Over-prioritize

Pr1 Pr2 Pr3

Pr1 Pr2 Pr3HT

HT

Pr1 Pr2 Pr3HT

Pr1 Pr2 Pr3HT

Informational accuracy captured by:

𝛼 =𝑃 𝐻𝑇 𝐻

𝑃(𝐻𝑇|𝐿)

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Classified high-priorityClassified low-priority

Improved accuracy

Coverage

0 10 20 30 40 500.405

0.41

0.415

0.42

0.425

0.43

0.435

0.44

0.445

Ex

pe

cte

d c

ov

era

ge

Optimal Policy, Case 1

Optimal Policy, Case 2

Closest Ambulance

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Low and high priority callsConditional probability that the closest unit is dispatched given initial classification

High-priority calls Low-priority calls0 10 20 30 40 500.98

0.985

0.99

0.995

1

1.005

Pro

po

rtio

n c

losest

am

bu

lan

ce is d

isp

atc

hed

Closest Ambulance

Optimal Policy, Case 1

Optimal Policy, Case 2

0 10 20 30 40 500.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Pro

po

rtio

n c

losest

am

bu

lan

ce is d

isp

atc

hed

Closest Ambulance

Optimal Policy, Case 1

Optimal Policy, Case 2

Classified high-priority Classified low-priority

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Case 1 (𝛼 = ∞), Case 2 policiesHigh-priority calls

Case 2: First to send to high-priority calls

Station1

2

3

4

Case 2: Second to send to high-priority calls

Station1

2

3

4

Service can be improved via optimization of backup service and response to low-priority patients

Rationed for high-priority calls

Rationed for low-priority calls

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Server busy probabilities

1 2 3 40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Serv

er

busy p

robabili

ty

Server

Closest Server Policy

Optimal Policy

1 2 3 4

District

𝛼 = ∞

23

Equity in OR models

• EMS systems are public processes where there is an expectation of equity

• We want to balance equity with efficiency/effectiveness

• Giving no one pie is equitable but it is not very efficient

• Twenty equity measures used in models for locating public assets*• Not all are “good” equity measures• Equity measures often selected for computational tractability • All focus equity from customer point of view

• Need equity measures for• (Spatial) queueing systems• Service providers and stakeholders other than customers

* Marsh, M. T., & Schilling, D. A. (1994). Equity measurement in facility location analysis: A review and framework. European Journal of Operational Research, 74(1), 1-17. 24

Equity and Markov decision processes

Goal: Balance coverage (efficiency) and an equity model

• Constrained MDP that optimizes coverage subject to equity constraints

• Solve MDP via linear programming

Equity constraints from the customer point of view

1. Ex ante equity: are resourced allocated fairly up front?Fraction of patients serviced by ambulance at “home” station.

2. Ex post equity: was equity achieved? Minimum utility achieved at each node (e.g., survival).

Equity constraints from the service provider point of view

3. Min/max ambulance busy probabilities

4. Rate at which each ambulance is dispatched to high-priority patients.

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Implications of choosing equitable policies

Observation (not surprising):

• Not possible to satisfy all notions of equity

Observation:

• Not always possible to equalize a single notion of equity

• E.g., patient survival

Observation:

• Sometimes we can achieve equity only at an enormous cost

• E.g., Rate at which each ambulance dispatched to high-priority patients

Observation:

• Sometimes it is easily to equalize a notion of equity

• E.g., ambulance busy probabilities

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Coordinating multiple types of units

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Coordinating multiple types of vehicles

• Not intuitive how to use multiple types of vehicles• ALS ambulances / BLS ambulances (2 EMTp/EMT)• ALS quick response vehicles (QRVs) (1 EMTp)

• Double response = both ALS and BLS units dispatched

• Downgrades / upgrades for Priority 1 / 2 calls• Who transports the patient to the hospital?

• Research goal: operationalize guidelines for sending vehicle types to prioritized patients• (Linear) integer programming model for a two vehicle-type

system: ALS Non-transport QRVs and BLS ambulances

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Results quantify impact of using QRVs

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Application in a real setting

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Achievement Award Winner for Next-Generation Emergency Medical Response Through Data Analysis & Planning (Best in Category winner), National Association of Counties, 2010.

McLay, L.A., Moore, H. 2012. Hanover County Improves Its Response to Emergency Medical 911 Calls. Interfaces 42(4), 380-394.

Severe weather and disasters

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Emergency response during severe weather events

• Resource allocation decisions—such as staffing levels—is important for system performance and patient outcomes.

• First, we have to understand what is different during severe weather:

• the volume and nature of calls for service may be different,

• critical infrastructure is impaired or destroyed, and

• there are cascading failures in the system.

• …these issues are not as predictable as they would be on a “normal” day

• In a blizzard scenario:

• System flooded with low-priority calls

• Amount of work (offered load) between fire and EMS increases by 41%

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Staffing during blizzards

• Study the number of calls that arrive when no units are available (NUA scenario).

• How many ambulances are needed such that NUA scenario occurs less than 1% of the time?

• How does this change based on response policies and system-wide adaptation?• Model parameters vary according to the traffic in the system:1. Probability that a patient needs to go to the hospital.2. Service times conditioned on whether a patient needs

hospital transport.

• Simulation goal:

• >99% of patients receive an immediate response

• Four queuing disciplines considered for priority queueing

Kunkel, A., McLay, L.A. 2013. Determining minimum staffing levels during snowstorms using an integrated simulation, regression, and reliability model. Health Care Management Science 16(1), 14 – 26.

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How many ambulances are needed to immediately respond to 99% of calls?

Taking system adaptation into account is often like having one additional ambulance in the system, particularly when the system is busy.

Queueing Discipline System Adaptation

Normal weather

Snow flurries

Leftover snow

Blizzard conditions

Queue excess No 6 7 7 8

Yes 6 6 7 7

Priority-specific excess No 6 7 7 8

Yes 6 6 7 7

Drop excess No 6 6 6 8

Yes 5 6 6 7

Drop low priority No 5 5 5 7

Yes 5 5 5 6

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Where do EMS systems need to go?

35

EMS response during/after extreme events

• Two main research streams exist:• Normal operations• Disaster operations

• More guidance needed for “typical” emergencies and mass casualty events

• Health risks during/after hurricanes:• Increased mortality • Traumatic injuries• Low-priority calls• Carbon monoxide poisoning * Caused by power failures

• Electronic health devices * Caused by power failures

• Decisions may be very different during disasters• Ask patients to wait for service• Evacuate patients from hospitals• Massive coordination with other agencies (mutual aid)

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EMS = Prehospital care

Operations Research

• Efficiency

• Optimality

• Utilization

• System-wide performance

Healthcare

• Efficacy

• Access

• Resources/costs

• “Patient centered outcomes”

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Healthcare

Transportation

Public sector

Common ground?

More thoughts on patient centered outcomes

Operational measures used to evaluate emergency departments

• Length of stay

• Throughput

Increasing push for more health metrics

• Disease progression

• Recidivism

Many challenges for EMS modeling

• Health metrics needed

• Information collected at scene

• Equity models a good vehicle for examining health measures (access, cost, efficacy)

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Healthcare

Transportation

Public sector

Thank you!

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1. McLay, L.A., Mayorga, M.E., 2013. A model for optimally dispatching ambulances to emergency calls with classification errors in patient priorities. IIE Transactions 45(1), 1—24.

2. McLay, L.A., Mayorga, M.E., 2011. Evaluating the Impact of Performance Goals on Dispatching Decisions in Emergency Medical Service. IIE Transactions on Healthcare Service Engineering 1, 185 – 196

3. McLay, L.A., Mayorga, M.E., 2014. A dispatching model for server-to-customer systems that balances efficiency and equity. To appear in Manufacturing & Service Operations Management, doi:10.1287/msom.1120.0411

4. Ansari, S., McLay, L.A., Mayorga, M.E., 2014. A maximum expected covering problem for locating and dispatching servers. Technical Report, Virginia Commonwealth University, Richmond, VA.

5. Kunkel, A., McLay, L.A. 2013. Determining minimum staffing levels during snowstorms using an integrated simulation, regression, and reliability model. Health Care Management Science 16(1), 14 – 26.

6. McLay, L.A., Moore, H. 2012. Hanover County Improves Its Response to Emergency Medical 911 Calls. Interfaces 42(4), 380-394.7. Leclerc, P.D., L.A. McLay, M.E. Mayorga, 2011. Modeling equity for allocating public resources. Community-Based Operations Research: Decision

Modeling for Local Impact and Diverse Populations, Springer, p. 97 – 118.8. McLay, L.A., Brooks, J.P., Boone, E.L., 2012. Analyzing the Volume and Nature of Emergency Medical Calls during Severe Weather Events using

Regression Methodologies. Socio-Economic Planning Sciences 46, 55 – 66.9. McLay, L.A., 2011. Emergency Medical Service Systems that Improve Patient Survivability. Encyclopedia of Operations Research in the area of

“Applications with Societal Impact,” John Wiley & Sons, Inc., Hoboken, NJ (published online: DOI: 10.1002/9780470400531.eorms0296)10. McLay, L.A. and M.E. Mayorga, 2010. Evaluating Emergency Medical Service Performance Measures. Health Care Management Science 13(2),

124 - 136

lmclay@wisc.edupunkrockOR.wordpress.com

@lauramclay