Tag Archives: emergencies

emergency response during mass casualty incidents

Today’s blog post about my research on mass casualty events and emergency response given that COVID-19 has been declared a pandemic by the World Health Organization (WHO). I have four papers in the area that are relevant in the area of emergency medical services (EMS) during mass casualty incidents.

A mass casualty incident (MCI) is an event in which the demand for service overwhelms local resources. Since fire and EMS departments operate at the local level, they can be overwhelmed quite easily. Anything from a multiple vehicle accident to a weather disaster to a hospital evacuation can be considered an MCI. Fire and EMS departments have “mutual aid” agreements with neighboring departments to address the more routine of these incidents and have “Standard Operating Procedures” for a range of more severe incidents. However, switching between such policies in practice is not simple. Moreover, not all mass casualty incidents are the same. Responding to calls for service during a hurricane is different than during a pandemic. In the latter, paramedics and emergency medical technicians can become sick and should stop treating patients, leading to fewer resources for responding to patients that require service. Additionally, we would expect less road congestion and wind during pandemics than in a hurricane evacuation. However, both cases may see a surge of low-acuity patients who request service.

My research focuses on emergency response during MCIs lifts limiting assumptions made by papers in the literature, which often assume that there are enough resources available all the time (which is not a reasonable assumption during MCIs). Here is a summary of four of my papers that have addressed MCIs.

Dubois, E. Albert, L.A. 2020. Dispatching Policies During Prolonged Mass Casualty Incidents. Technical report, University of Wisconsin-Madison.

The newest paper is available as a technical report and is most relevant to COVID-19. It focuses on a large surge of patients that overwhelmed EMS resources. Here, we lift the assumption that a patient’s priority is a fixed input. Instead, we consider patients whose conditions deteriorate over time as they wait for service.  We consider how to assign two types of ambulances to patients, advanced and basic life support. We study how to dispatch ambulances during MCIs while allowing ambulances to idle while less emergent patients are queued. This is similar to keeping a reserve stock of advanced life support ambulances (see the last paper listed in this post). The inherent trade-off is that when low-priority patients are asked to wait for service, they can become high-priority patients. When high-priority patients are asked to wait for service, they can become critical or die. Our solution method is to find dynamic response policies to match two types of ambulances with these three types of patients.  We observe that, under the optimal policies, advanced life support ambulances often remain idle when less emergent patients are queued to provide quicker service to future more emergent patients. It is counter-intuitive to not use all resources all the time during an MCI. However, keeping some resources in reserve ensures that there are resources available at the time the most critical patients need them.

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.

The second paper seeks to characterize the volume and characteristics of EMS and fire calls for service. It was motivated by the need to deliver routine emergency service during weather emergencies and disasters. What typically happens during emergencies is that there are more calls for service, most of which are low priority calls. Triage becomes more important in these situations, because the most severe calls for service can be drowned out by so many low-priority requests. However, call surges are not the only stress on fire and EMS departments. Road congestion and slow travel times mean that each call takes more time to serve, which can further stress limited resources. As a result, it becomes important to triage calls and assign appropriate resources.

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.

The third paper studies staffing levels during a blizzard, where a surge of calls can temporarily overwhelm resources that are available. Additional staff are usually scheduled during emergencies when call volumes increase. We specifically focus on snow events, and the results have insight into other situations. To determine staffing levels that depend on weather, we propose a data-driven model that uses a discrete event simulation of a reliability model to identify minimum staffing levels that provide timely patient care,with regression used to provide the input parameters. We consider different response options, including asking low priority patients to wait for service, and we take into account that service providers often work faster when systems are congested. The latter issue of allowing adaptive service rates is important, since it makes the model more realistic by limiting the assumption that service rates are constant. A key observation is that when it is snowing, intrinsic system adaptation with respect to service rates has similar effects on system reliability as having one additional ambulance.

Yoon, S., Albert, L. 2018. An Expected Coverage Model with a Cutoff Priority Queue. Health Care Management Science 21(4), 517 – 533.

The final paper examines how to locate and dispatch ambulances when resources can be temporarily overwhelmed. In this paper, there are prioritized calls for service in a congested system, but the system is not completely overwhelmed by an MCI such as a hospital evacuation. Typically, models in the literature implicitly assume that there are always enough resources to respond immediately to all calls for service that are received. This is not a good assumption when there is an MCI. As a result, we need new models and analyses to provide insights into how to allocate resources when there is congestion and many service providers are busy treating patients.

We formulate new models to characterize policies when ambulances are held in reserve for high priority calls. When the system is so congested that it hits the “reserve” stock of ambulances, low priority patients are either diverted to neighboring EMS systems through mutual aid or added to a queue and responded to when the congestion has reduced. Interestingly, we find that by adopting such an approach for sending (and not sending) ambulances to patients, this affects where we might want to locate ambulances at stations.

 

 

 


It’s National Emergency Medical Services Week #EMSweek2019. Check out my papers and presentations about EMS systems.

This week is National Emergency Medical Services Week. I’ve published and spoken extensively about my research on emergency medical services.

Some blog posts about EMS include:

Mike Trick wrote a post about my semi-plenary talk at the 2014 German OR Society conference entitled “Using analytics for emergency response

 

 

Papers include:

  1. McLay, L.A., A Maximum Expected Covering Location Model with Two Types of ServersIIE Transactions 41(8), 730 – 741.
  2. McLay, L.A. and M.E. Mayorga, 2010. Evaluating Emergency Medical Service Performance MeasuresHealth Care Management Science 13(2), 124 – 136.
  3. McLay, L.A., Mayorga, M.E., 2011. Evaluating the Impact of Performance Goals on Dispatching Decisions in Emergency Medical ServiceIIE Transactions on Healthcare Service Engineering 1, 185 – 196.
  4. Chanta, S., Mayorga, M.E., Kurz, M.E., McLay, L.A., 2011. The minimum p-envy location problem: A new model for equitable distribution of emergency resourcesIIE Transactions on Healthcare Systems Engineering 1(2), 101 – 115.
  5. McLay, L.A., Moore, H. 2012. Hanover County Improves Its Response to Emergency Medical 911 CallsInterfaces 42(4), 380-394.
  6. Bandar, D., Mayorga, M.E., McLay, L.A., 2012. Optimal Dispatching Strategies for Emergency Vehicles to Increase Patient SurvivabilityInternational Journal of Operational Research.
  7. 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 MethodologiesSocio-Economic Planning Sciences 46, 55 – 66.
  8. Kunkel, A., McLay, L.A. 2013. Determining minimum staffing levels during snowstorms using an integrated simulation, regression, and reliability modelHealth Care Management Science 16(1), 14 – 26.
  9. McLay, L.A., Mayorga, M.E., 2013. A model for optimally dispatching ambulances to emergency calls with classification errors in patient prioritiesIIE Transactions 45(1), 1—24. This paper was selected as a Best Paper Award for IIE Transactions Focused Issue on Scheduling and Logistics.
  10. Mayorga, M.E., Bandara, D., McLay, L.A., 2013. Districting and dispatching policies for emergency medical service systems to improve patient survivalIIE Transactions on Healthcare Systems Engineering 3(1), 39 – 56.
  11. Toro-Diaz, H., Mayorga, M.E., Chanta, S., McLay, L.A., 2013. Joint location and dispatching decisions for Emergency Medical ServicesComputers & Industrial Engineering 64(4), 917 – 928.
  12. Dreiding, R.A., McLay, L.A., An Integrated Screening Model for Screening Cargo Containers for Nuclear WeaponsEuropean Journal of Operational Research 230, 181 – 189.
  13. Chanta, S., Mayorga, M. E., McLay, L. A., 2014. Improving Rural Emergency Services without Sacrificing Coverage: A Bi-Objective Covering Location Model for EMS SystemsAnnals of Operations Research 221(1), 133 – 159.
  14. Sudtachat, K., Mayorga, M.E., McLay, L.A. 2014. Recommendations for Dispatching Emergency Vehicles under Multi-tiered Response via SimulationInternational Transactions in Operational Research 21(4), 581-617.
  15. Chanta, S., Mayorga, M.E., McLay, L.A., 2014. The minimum p-envy problem with requirement on minimum survival rateComputers & Industrial Engineering 74, 228 – 239.
  16. Bandara, D., Mayorga, M.E., McLay, L.A., 2014. Priority Dispatching Strategies for EMS SystemsThe Journal of the Operational Research Society 65, 572 – 587.
  17. Grannan, B.C., Bastian, N., McLay, L.A. A Maximum Expected Covering Problem for Locating and Dispatching Two Classes of Military Medical Evacuation Air AssetsOperations Research Letters 9, 1511-1531.
  18. Toro-Diaz, H., Mayorga, M.E., McLay, L.A., Rajagopalan, H., Saydem, C., Reducing disparities in large scale emergency medical service systemsJournal of the Operational Research Society 66, 1169-1181. doi:10.1057/jors.2014.83
  19. McLay, L.A., Mayorga, M.E., 2013. A dispatching model for server-to-customer systems that balances efficiency and equityManufacturing & Service Operations Management 15(2), 205 – 200.
  20. Ansari, S., McLay, L.A., Mayorga, M.E., 2015. A Maximum Expected Covering Problem for District DesignTransportation Science 51(1), 376 – 390.
  21. Sudtachat, K., Mayorga, M.E., McLay, L.A., 2016. A Nested-Compliance Table Policy for Emergency Medical Service Systems under RelocationOMEGA 58, 154 – 168.
  22. Ansari, S., Yoon, S., Albert, L. A., 2017. An approximate Hypercube model for public service systems with co-located servers and multiple responseTransportation Research Part E: Logistics and Transportation Review. 103, 143 – 157. DOI: 1016/j.tre.2017.04.013.
  23. Yoon, S., Albert, L. An Expected Coverage Model with a Cutoff Priority QueueHealth Care Management Science 21(4), 517 – 533. DOI: https://doi.org/10.1007/s10729-017-9409-3.
  24. Enayati, S., Mayorga, M., Toro-Diaz, H., Albert, L. 2018. Identifying trade-offs in equity and efficiency for simultaneously optimizing location and multi-priority dispatch of ambulancesInternational Transactions in Operational Research 26, 415 – 438. DOI:1111/itor.12590
  25. Yoon, S. and Albert, L.A., 2018. Dynamic Resource Assignment for Emergency Response with Multiple Types of Vehicles, Submitted to Operations Research, October 2018.
  26. Yoon, S., and Albert, L.A. A dynamic ambulance routing model with multiple response. Submitted to Transportation Research Part E: Logistics and Transportation Review, January 2019.

Translating engineering and operations analyses into effective policy

I am presenting at the AAAS Annual meeting in a session entitled “Translating Engineering and Operations Analyses into Effective Homeland Security Policy” with Sheldon Jacobson and Gerald Brown:

In my talk, I will discuss three research questions I have advanced:

  1. How can we more effectively perform risk based security?
  2. What is the optimal way to allocate vehicles to emergency calls for service?
  3. What is the optimal way to protect critical information technology infrastructure?

My slides are below.

Related posts and further reading:

If you have any questions, please contact me!


My keynote at the 4th International Workshop on Planning of Emergency Services in Delft

I gave the opening keynote at the 4th International Workshop on Planning of Emergency Services on June 19-20 in the Netherlands at TU Delft. The workshop was organized by Karen Aardal, Theresia van Essen, Pieter van den Berg, and Rob van der Mei. The workshop was a nice way for researchers and practitioners from several countries in Europe to discuss ideas in emergency service planning. Talks were about emergency medical services, defibrillators, and disaster response. The slides from my keynote are posted below. I enjoyed the other keynote given by Jo Røislien, who talked about optimizing air ambulance base locations in Norway and the politics of addressing the policy issues in Norway.

My hosts ensured I enjoyed my time in Delft. Delft is a wonderful place to visit. I took a few pictures from my trip and posted them below.

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#Delft #Netherlands

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A savory #cheese dish in #Delft #nederland

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Dessert #cheese in the #netherlands #yum

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the logistics of the post-Sandy New York Marathon

I’m pleased to hear that NYC marathon will be held on Sunday as planned.

The logistics will be challenging. The race organizers were expecting 50,000 runners before Hurricane Sandy hit. While many runners may sit out, I expect that most will try to run. After all, the hurricane hit well into the tapering phase of training, meaning that runners should be ready to run,even if they’ve been dealing with hurricane-related challenges. And most of the out of town runners will be relatively unaffected by the hurricane and should similarly be ready to run.

The main challenges as I see it will be to:

  1. Get runners into the city and have a hotel room
  2. Get runners and volunteers to the race.
  3. Distribute race supplies such as water and Powerade and to locate portable bathrooms.

#1 Get runners into the city

In huge marathons like this one, many of the runners will not be nearby. Last year, 20,000 of the runners came from overseas. The main ways to get to NYC are by plane and train. As of now, Amtrak still has not resumed NYC travel. They plan to partially restore travel on Friday. There have been a large number of flight cancellations, but flights are being restored and it appears that runners are making it to New York.

Runners from out of town also need hotels. Surprisingly, the lack of hotel rooms seems to be a larger problem for runners than transportation to NYC. The hotels are packed:

The city’s hotels are coping with a list of issues. Among them: Unprecedented cancellations and requests to extend stays; a high number of walk-in room requests from powerless local residents; unpredictable staffing levels; non-working land lines, and in some cases no steam heat.

The Pittsburgh Steelers likewise had problems finding a hotel to accomodate the team on Saturday night before their road game against the New York Giants. The Steelers are flying to Newark for their game Sunday morning.

#2 Get runners and volunteers to the race

Once in/near the city, all 50,000 runners and a few thousand volunteers need to get to the beginning of the race more or less at the same time. Driving to the beginning of a big race like this is generally not the best way to get there. The NYC marathon normally starts on Staten Island, which harder to get to than most races. In the past, half of the runners take the subway in combination the Staten Island ferry to the beginning of the race. Not so this year. The Staten Island ferry has been cancelled and buses will instead transport the runners from a meeting point to the race in four waves at 4:30am, 5:30am, 6:30am, and 7:30am. There shouldn’t be a lot of traffic at 4:30am on Sunday morning, so I would anticipate that the runners should be OK as long as they can take other forms of public transportation to get to the meeting point for the race buses.

Distribute race supplies such as water, Powerade, and portable bathrooms

Normally, setting up portable bathrooms and water/Powerade stations is not a complicated matter. With the number of road closures, etc., it will be more difficult to obtain the necessary marathon resources and get them where they need to be. Races need a huge number of bathrooms because all runners need to go to the bathroom at the same time (right before the race). I wasn’t sure that many portable bathrooms would be available, and it sounds like 1750 bathrooms are at the start of the race. I wrote about bathrooms before [Link]. That sounds like a lot of bathrooms per runner, but I can assure you, there will still be long lines.

In sum, I am amazed that the marathon will continue more or less as planned. I am surprised, however, that hotels may be the biggest challenge. I am also concerned about snafus with public transportation, since runners will rely on public transportation in new ways this time. I hope everything goes smoothly.

What are other issues, bottlenecks, and shortages do you foresee?


the forecasting models behind the power outages forecasts for Hurricane Sandy

I’m thrilled to have interviewed Seth Guikema about his forecasting models for hurricane power outages between his gigs on Good Morning America and Bloomberg. Seth is a professor at Johns Hopkins University, and he is the rock star of hurricane power outage forecasts. I wrote about a Baltimore Sun article about his research not too long ago. On the podcast, he and I chat about the methodologies he uses in his models as well as how news sources like to turn scientific research into digestible sound bites.

Listen here: (or go directly to the mp3 here)

You can listen to the episode below or you can go to the podcast web page (where you can download to iTunes, etc.) and feed. I recommend subscribing to the feed or going directly to the Punk Rock OR Podcast iTunes page, but you can also find the podcast episodes on this blog by clicking on “Podcast” under “Categories” in the left column.

Seth’s models have gotten a lot of coverage. Here are a few places where you can see Seth’s work translated for a general lay audience:

Seth’s forecasts as of 6am on 10/29:

Total prediction: 11 million without power
MD: 2 million
DC: 300,000
NJ: 3.4 million
DE: 425,000
PA: nearly 4 million
Here is an image of where the power outages will occur:

Power outage forecasts for Hurricane Sandy (courtesy of Seth Guikema)