Category Archives: Dispatch & Communications

Improving EMS Deployment Performance

I work regularly with agencies that are looking to improve aspects of their operations. Some casual readers may be surprised to know that the focus of those discussions is not always about cutting response times. While response is a simple and common measure, it clearly does not evaluate EMS well and certainly fails to encapsulate many of its complex needs and values. Still, I feel the necessity to address the time objective briefly before going on to other important aspects.  

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Where Do We Go Next?

To know where our increasingly limited emergency resources will be needed next, we need to understand where future requests for service will originate. If we knew exactly where the next call would come from, we could proactively dispatch a resource there even before it is requested (watch the movie “Minority Report” for an idea of how that might work.) Unfortunately, the nature of emergency response is not nearly that easy, but that is not to say it is impossible to recognize useful patterns across both time and space. While the 2002 Spielberg movie was set 50 years into the future, it correctly predicted the use of several new technologies that have become reality in less than twenty years. And although we don’t use “precogs” in forecasting demand, the ability of data to show future patterns that effectively influence deployment is also now well established within some agencies.

No one can tell you who will be that very next person to dial 9-1-1; however, it is imperative for the effectiveness of deployment that we concede that people and events often follow certain predictable patterns. Let me explain how this works in just a few steps. First, consideration of the repeatable nature of the temporal distribution of calls has been used for years in making shift schedules. The following chart represents the daily call volume from a specific study, but without a scale along the vertical axis, it could easily be representative of almost any agency regarding their relative hourly volumes.

The daily behavioral routine of individuals perpetuates the collective pattern for the larger community. These daily patterns not only replicate over the years, but across various types of political jurisdictions according to a 2019 Scandinavian study on the “Use of pre-hospital emergency medical services in urban and rural municipalities over a 10?year period: an observational study based on routinely collected dispatch data.” The following graphs from that study represent the relative call volumes of rural, small and large towns, as well as medium and large cities over a decade showing the reproducibility of call volume forecasts by hour of the day.

If we segregate the total call data by weekday, we can capture variations by the hour-of-the-day within each day-of-the-week. The chart of call volumes by day over a twenty-week timeframe, shown below, displays the commonly repeated variation throughout each week. It is the reproducibility of these volumes that allows us to schedule adequate crews to cover these anticipated call volumes.

The next step is to adequately distribute those available resources spatially to address the variation over the geographic area by time which requires an even deeper understanding of the call patterns. The fact that we, as social creatures, often live or work in communities that share similar and predictable risk factors allows us to generalize assumptions of individual activities over larger community groups. Corporations have used targeted demographic profiles to understand local populations for many years. Community profiling has even been recognized by the World Health Organization as an essential skill for all health professionals to help understand the specific and detailed needs of focused populations. (See “Community Profiling. A Valuable Tool for Health Professionals” published in Australia during 2014.) Beyond predictable human variables that focus primarily on medical emergencies are the physical characteristics of our built environment that determine the repeatability of traumatic accidents. A 2009 publication by the Association for the Advancement of Automotive Medicine looked specifically at “Identifying Critical Road Geometry Parameters Affecting Crash Rate and Crash Type” to aide road safety engineers with the challenge of addressing safety issues related to the shape of motorways. The existence of identifiable causes explains the ability to properly forecast the vicinity of calls in addition to their timing.

The following animation demonstrates several spatial demand forecasts in quick succession that are normally separated in the real world by hours. Your existing historical CAD records contain the necessary information to build such dynamic views in real-time.

The demonstrated reliability of demand forecasts, both spatially and temporally, is well known to MARVLIS users and proven to provide the critical information necessary to make decisions in prepositioning resources to reduce the time of emergency responses and limit the distances travelled in emergency mode to enhance the protection of crews and citizens. Furthermore, the Demand Monitor has the capability of grading demand hotspot calculations specific to your service by comparing actual call locations – as they are being recorded – with the forecast probability surface to highlight both the accuracy and precision of our demand forecasts over time that is specific to your agency data and query parameters. The following screenshot shows comparisons of various forecast models.

The percentage of calls that correspond with each shaded area over the selected timeframe quantifies the query accuracy while the hotspot size denotes the relative precision. Accuracy could be increased easily by enlarging the hotspots, but this would be at the cost of precision. A well-balanced query should result in a relatively small-sized hotspot that properly captures a significant portion of actual calls.

Still, knowing when and where to anticipate calls is not enough in itself to determine resource deployment. Some number of outlier calls will likely occur outside of the forecast hotspots, so it is critical to also develop a strategy for managing the risk of covering demand versus geography as weighted factors in any deployment decision. Where we need to be next is well beyond the simple strategies we typically employ now and must fully leverage the depth of our data for deeper understanding and action.

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How "New" Will "Normal" Actually Be?

Be careful what you wish for. Just a few months ago, before the words “COVID-19” and “social-distancing” became a regular part of our conversations, I was speaking with the Operations Chief of an EMS service about the difficulty in hiring and retaining paramedics. He said it would take “a downturn in the economy before we could hire enough medics” since candidates typically gravitate toward stable jobs in public service when the market is in a recession. Well, its technically not a recession, but the current pandemic is clearly stressing the world economy and even altering patterns of use for many EMS agencies. In some areas of the country, call volume is now out-stripping capacity while others find themselves in a very different place with far fewer calls than normal. So, as we even consider whether we still need the paramedics we had planned, the immediate questions become “what is ‘normal’,” and “what could be so ‘new’ about it?”

The past can often be a good guide. My primary job in consulting is helping agencies with the optimization of their resources. Doing this successfully requires that I can discover patterns from history to guide forecasts of the immediate future. This is a difficult position when the world is no longer behaving according to the regular fluctuations of the past. Yet, as an undeterred student of history, I continue to search for models that can illuminate the path before us as I did regarding demand in my previous post. There is no shortage of significant anecdotes from history to review, but each has its limitations when applied to today.

My first study was the so-called “Spanish Flu” of 1918-19. It was the deadliest pandemic in history that infected nearly a third of the human population and killed well over 20 million (or by some estimates more than 50 million) victims, including some 675,000 Americans. This historic pandemic had a similar effect to today by shutting down world economies and hiding its population behind face masks. The scariest consideration of a modern parallel to this period would be the idea of an even more devastating second (or even third) wave of infections yet to come. This historic flu, however, was still not able to destroy the world order as some feared. In fact, it preceded one of the greatest economic expansions of industry leading to a period that would be known as the “Roaring Twenties.” The score of our current pandemic is merely a shadow of its predecessor with less than 5 million worldwide infections known and slightly more than 300 thousand total deaths around the globe. So, could we also expect a similar economic boom following our current crisis? That is highly doubtful as the economic conditions preceding this shutdown were entirely different than a century ago. And I’m also not sure we would necessarily want that same exuberance that stemmed from a generation that developed an attitude of “nihilistic hedonism” born from a season of austerity and fear caused by the disease. The age group primarily affected at that time developed a laissez-faire attitude toward life fueled by a rapid rise in prosperity induced by sweeping changes in technology, society, and economy. It was literally the beginning of the modern age – and then came the worst economic depression ever.

Fortunately, the current death toll is still far too low to engender a similar sociological backlash even in a time of modern polarized politics echoing the protests of the last century. With a presidential election less than six months away, many states have entered some form of “Phase 1” of a controlled economic reopening of society. There are probably as many anecdotes as opinions with states like Texas going big on economics over epidemiology compared to the more cautious moves of hard-hit states like New York and New Jersey only ‘cracking open’ slowly. While scientific advances are promising, we still do not have a vaccine, effective treatment, or even reliable tests. Yet we seem reliant on the promise of “contact-tracing” in an environment of community-spread rather than recognized efforts elsewhere at “contact-isolation.” So we can likely plan on seeing more cases of COVID-19 in the coming months and political reactions will likely vary with an increased influence of politics.

What is likely to be lasting from our current experience are new “telemedicine practices” being implemented by physicians and widely accepted by a public that fears even going to the hospital at the moment. If EMS will ever be able to justify the continuation of Community Paramedicine practices or possibly even extending them through their own Mobile-Integrated Healthcare outreach (or as a home-provider within the telemedicine practice of doctors) it will be right now. If the opportunity of the current crisis passes without making political gains to extend the reach of EMS, it will only be more difficult to accomplish in the future. We have also seen traditional conferences gone virtual to eliminate travel and large physical gatherings. Although the experience lacks some of the traditional perks, it has huge cost and time savings. Similarly, professional-referred journals are quickly giving way to a faster social exchange of information and ideas online that bypasses traditional peer-review being replaced by a new social review creating “healthcare influencers” online. To continue this trend, we must figure out how to “qualify” these social icons in the long-term and socially circumscribe their power.

There are also examples we could study of pre-hospital responses to HIV/AIDS, MERS, and SARS. Even though each occurrence caused a significant public panic and subsequent EMS response, their lasting influence quickly waned and the lessons they taught for preparedness were not applied nationally to help us respond to a pandemic. Consequently, the real strategic question we must consider in planning for the future is fortunately not how society will react or estimate how many cases of COVID-19 we will experience, but what effective change will be wrought related to how EMS functions or is financed going forward. As we contemplate moving out from the Department of Transportation  where we are paid only for moving patients, we could consider the terrorist attacks of September 11, 2001, as another example of a precedent model. However, that initiating event concluded within hours and its perpetrators targeted an ideology rather than a lack of immunology. Both passions and fears were inflamed worldwide by these coordinated attacks, but the only lasting results have been legislation expanding government surveillance in the Patriot Act (reauthorized yet again nearly two decades after the event), the creation of a new government bureaucracy over the traveling public in the Transportation Security Administration (which remains focused largely on airline travel which was the target of the terrorists at that time), and the longest on-going war of American history.

Today, the enemy has no flag and the world (or even our industry) also has no unified leader to coalesce a response tactic. Even in the field, the providers of EMS services cannot agree on whether we represent public safety (which justifies an essential funding stream for the public good) or that we provide bona fide healthcare services as a part of an integrated service stream offering appropriate care anywhere from the home to a hospital (that is worth reimbursement independent of driving someone to the hospital.) What history teaches us are several lessons. First, government responds to situations that expand its own interests and that are simultaneously supported by the affections and desires of the public. Even during this EMS Week, it is doctors and nurses who are seen on the front lines of the pandemic war even though the tip of the spear is made up of Emergency Medical Services professionals who go into the homes of the sick and reach through the wrecked vehicles of the injured to risk themselves in the preservation of others. We will continue to be the ‘invisible third service’ as long as we struggle with our identity and lack the statement of a value proposition for a suitable underlying financial mechanism. Second, government consistently responds along an evolutionary path to the last threat rather than a forward-thinking approach. Until we can justify the payment for necessary treatment on scene in addition to any transport to definitive alternative destinations, we will not see revolutionary change. Even wars can be waged indefinitely as long as no one notices they continue. 

We may see some fluctuation in demand for a while, but in the long-run we will return to a familiar normal fare of heart attacks, strokes, and falls once again. It may not be the exact same place we left months ago, but it will not be an entirely new place either. The struggles we fought before will continue to be our struggles again. Hiring and retaining paramedics will again become a topic of discussion as we continue to fight for budgets to maintain our response metrics. That is unless we can learn from one other historical example that comes from back in 1843.  That is the year that Charles Dickens published his famous work known as, A Christmas Carol, where the the Ghost of Christmas Yet to Come prophesies, “If these shadows remain unaltered by the Future, the child will die.”

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Improving Operations in Crisis

Our practice of EMS is facing significant challenges right now. Although many traditional aspects must still continue, we have a few more obstacles to overcome in a crisis. This “pilot podcast” highlights some practical modifications to consider for operational improvements, especially for MARVLIS users.

Notes:

Demand for EMS services is disproportionate across America and outside of normal patterns, but some changes to our practice are helpful across any service right now. If you haven’t begun seeing longer times yet, you can expect it to be coming as we face longer dispatch delays for extended EMD, longer on-scene times for re-triaging patients using a “1-in and 1-out” scouting method, longer decontamination times for ambulances possibly infected with COVID-19, and fewer professional human resources collectively making operational efficiency and crew management even more important than ever. At the same that time we are still dealing with our regular calls, mass quarantines and stay-at-home orders are likely to increase calls for domestic violence, drug abuse, acute mental illnesses, and even suicide as people socially distance.

  1. Consider modifying queries in Demand Monitor to include longer general timeframes when forecasting dynamic demand:
  • Extend the period of weeks, e.g. 56-60 days both Before and After the current date.
  • Extend the period of minutes, e.g. 90-120 minutes both Before and After now.
  • Enable hotspot accuracy reports to quantify the value of different queries.

2. Create new posting plans with Deployment Planner that balance the weight of geography and demand to limit post move recommendations.

3. Implement a Leapfrog in Deployment Monitor value to penalize moving stationary ambulances by preferring to move units already in transit.

4. Call BCS Support for any help you need to configuring MARVLIS to your operational challenges beyond simple mindless efficiency.

 

 

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See What Others Can't

Ever since I was a kid, I wanted a superpower of some kind. Little did I know that one day my wish would actually come true. 

For anyone who is a serious user of Geographic Information Systems (GIS), it is not news that this week is the 2019 Esri User Conference. If you are not one of those people, the “UC” is an annual gathering of around 20,000 people who share an interest in applying geospatial technology to solve real-world problems from optimizing business to saving the environment. I was particularly inspired by the theme this year, “See What Others Can’t.”

At its core, GIS is a spatial database for the analysis and visualization of information. When it is used in EMS, it can take a deep dive through your call history and come up with an estimation of the likelihood of the location of calls for service within the next hour. Because it can be an automated process, this forecast can be repeated every few minutes to give you a constantly updated view of the near future regarding where you are most likely to be needed. Some users of MARVLIS Demand Monitor compare it to a weather map that shows the changing conditions in your service area. But knowing where you need to be is only a part of the problem of optimizing the delivery of emergency medical services.

To really be efficient, you also need to know where you are and where you can be within your response time allocation. To answer this question, you need a model of the street network and an understanding of both the daily patterns of travel as well as the unique driving conditions right now. Many counties across the US have dedicated GIS staff to maintain these navigation and addressing models, but commercial vendors can also provide a good base layer of data. TheAddresser is another product from BCS and it can be used to measure or even improve the quality of your geographic data to improve its ability to turn an address into a proper coordinate where a crew can physically respond. The digital road network that is used to calculate a route can be improved by modeling how fast vehicles in your fleet have traveled along each road segment in the past, divided by direction, and lumped into various traffic time periods. The MARVLIS Impedance Monitor automates the mining of your Automated Vehicle Location (AVL) history to generate these unique travel times to understand exactly what area can be covered even as an ambulance is moving. For the immediate hazards along the way, MARVLIS can leverage the events logged by Waze users in real-time to enhance your own road network data through MARVLIS Central. Together, this gives you the best understanding of the reach your crews have at any given moment.

The real trick is in how you choose to post ambulances to meet your specific objectives. If a fast, safe response is most valued, ambulances can be directed to uncovered hot spots which will minimize the distance they must travel to the next call. If cutting response times across the board, or minimizing post moves is preferred, a weighting can be applied in the MARVLIS Deployment Planner to optimize the geographic coverage area. Regardless of how the criteria are balanced, an hourly, prioritized posting plan can be generated based on your service objectives. That plan can then be automated through the live connection in MARVLIS Deployment Monitor that can not only see where ambulances are located by their status, but also directly viewing where calls are currently active from the Computer Aided Dispatch (CAD) software. It can then even make specific recommendations on reassigning units to automatically optimize your coverage criteria.

Together, these intrinsically GIS-based tools can provide an unparalleled insight into the operational world of EMS with timely automated recommendations on how to improve service according to your community’s values. The suite of MARVLIS applications give any EMS manager a view to “see what others can’t.”  To see clarity in the everyday chaos of EMS operations, GIS can give you genuine superpowers. 

-Dale Loberger

 

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Are You an Ambulance Driver Too?

One of the fastest ways to piss off almost anyone in the emergency medical services community is to call them an “ambulance driver.” It has become a triggered response as reliable as setting off the tones for a call. We bristle at the fact that driving an ambulance is such a small part of what we are trained to do – even though “high-flow diesel” can be an effective, legitimate treatment for certain patients. Retired FD captain and bestselling author of Rescuing Providence, Michael Morse, wrote an article last year on accepting the title of “ambulance driver.” His reasoning was due in large part to the variety of nuanced titles that we stubbornly cling to including Paramedic (which is reserved only for “those who can intubate”), EMT, Basic, EMT-I, or AEMT as well as several permutations of NREMT. Quite frankly, we simply do not accept any generalized term for “EMS workers” that is as easy to understand and say as doctor, nurse, firefighter, or cop. And for those who are offended at being lumped into the cadre of “first responders,” at least the term “ambulance driver” does distinguish one of our unique capabilities.

“Ambulance Driver”

While I agree that Morse has a legitimate argument in his assertion that we have made this predicament ourselves, there is another salient point that comes to my mind from the news multiple times each month when an ambulance is involved in a serious wreck. A local Minnesota news channel investigation discovered that the requirements for operating an ambulance in emergency traffic while carrying a sick patient and an often unrestrained paramedic in a moving emergency room is far less than is mandated for a “truck driver hauling a semitrailer load of beer.”

With a shortage of paramedics, more EMTs are being hired to fill out crews. With low starting wages, it is often people who are still too young to legally rent a car by themselves that are put behind the wheel of a 14,000 pound vehicle costing nearly a quarter of a million dollars and loaded with the most vulnerable of human cargo after just a day or two of experience driving a cone course!  

Recently, NHTSA analyzed 20 years of data and found that the nation averages 29 fatal crashes involving an ambulance each year. Furthermore, these accidents result in an average of 33 fatalities annually. For a group of individuals dedicated to saving lives, this should be an unacceptable statistic. Rather than being indignant that the name describes so little of our training, we need to adequately train for the job of driving an ambulance for proficiency just as we train for our skills as a medical clinician. The lives of our patients (and our partners) depend on that skill every bit as much, if not more, than our medical skills.

In addition to my personal credentials as a professional (both in EMS and GIS), I am a fire vollie, a backpacker, an instructor, and an amateur historian. While none of these monikers describe the entirety of my personality, none of them offend me by limiting the description. Why should I be insulted for being recognized for a critical function in safely operating an emergency vehicle? I do not hear doctors being offended by not being identified by their specialty or even by being lumped in with a PhD outside of the medical community. The sad fact is that we just don’t have an agreed generic term for the collection of people with which we share our profession. Although the term “ambulance driver” does not fully define me as a person, or even as an emergency medical professional, I will proudly accept the title as my personal commitment to safely operate my ambulance for the benefit of the public, my patient, my partner, and myself. To any other “ambulance driver” out there, let me thank you for all you do for the public beyond the safe operation of your rig.  

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More Reasons to Support Dynamic Deployment

The “Leverage Real-Time Data for Improved Ambulance Response Times” article that Zoll posted on their blog site did a good job of explaining response times and even the benefits of System Status Management (SSM) planning to the patient. But there is still more to the story that we have learned over the years since Jack Stout first introduced it.

Of course, it is best for the patient (and the service) when an ambulance arrives to the scene in a short time. The media often picks up on poor response times with stories like the GSW patient dying in Cleveland surrounded only by police and fire personnel. Even the doctors at JAMA can’t resist publishing an article showing response times as an inherent failure of EMS in certain cases. Unfortunately, many will read the solution as medics “speeding” to the scene, yet we have learned that lights and sirens have little impact on times and may even prevent some patients from calling for an ambulance in the first place. However, when the deployment of ambulances is responsive to the dynamic demand patterns throughout the day, ambulances can literally be moved closer to the scene even before they are dispatched. Literally hundreds of High Performance EMS agencies across the US have significantly reduced their 90th percentile response rates through technology. Forecasting the future does not involve magic, at least not for predictable phenomenon like emergency calls for service. Not only can we forecast the quantity and types of calls we will receive (necessary for adequate staffing), we can determine where they are likely to originate from with significant accuracy as well. Shortening the distance that an ambulance must travel is a safer alternative than asking a crew to speed in order to achieve the same result.

Another positive impact of shorter response times is patient satisfaction. There are many reasons that healthcare providers should be using patient satisfaction surveys, both for the benefit of the patient as well as simplifying the accreditation process for your service. Beyond safety, satisfaction and simplification; proper SSM can improve finances. Some services have recognized marked reductions in the number of post moves for crews and ultimately reduce the total number of unloaded (read unreimbursed) miles driven which saves on vehicle wear and tear in addition to fuel costs. Other agencies, particularly those who contract their services, can reduce financial penalties for “exceptions,” or late calls beyond the target response time.

Frankly, the public often expects performance measured in minutes. Whether we approve of the measure or not, we are often graded and compared based on response times. Whether the penalties for missing targets are financial, patient satisfaction, or driven by bad press reports, being late simply hurts. Until EMS is designated and funded as a critical service for government, it will be dependent on political funding allocations and insurance reimbursements. As long as performance is measured by how long it takes to be on scene, response times will be critical to the financial well-being of services. The least we can do is perform to the highest safe standards possible.

 

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Dynamic Risk for Intelligent Fire Move-Ups

Planning for the placement and staffing of fire apparatus, either in a fixed location or for a temporary move-up position, involves the comparative evaluation of community risk for each alternative. Unfortunately, our typical understanding of risk is skewed and outdated. Basing operational decisions on inadequate data leads to choices that can be inefficient, ineffective and legally indefensible.

Of course, there are many factors that combine to influence the danger of a fire response. There must be some estimate of fuel load along with the exposures and barriers to a potential fire spread. For the most part, existing studies get this right – even if only rudimentarily. But it is the most significant single impact on fire frequency that is modeled the poorest. Kasischke and Turetsky stated in 2006 that “(people) are the dominant source of ignitions except in sparsely populated regions.” Our troubled standard for measuring population is the decennial US census. Prior to the twenty-first century, these federal statistics were clearly the most consistent available figures that were widely accessible.

Census population data, which is often the basis of many comprehensive fire plans, have several logical failures for their use in local community risk evaluation. The first problem is the age of the data. The census is taken only every ten years and the values of intervening years are estimated through algorithms. At this present point in time, the 2010 population estimates have been statistically massaged for the past 7 years. Add to that, the fact that the census only counts “night-time” populations by estimating where individuals “live” (or spend the majority of their sleeping time) rather than accounting for their patterns of movement outside of the home. The time away from their census-defined abode can often be the better part of each 24 hour period, yet the nineteenth century agrarian idea of home is the value most studies use to consider the number of humans at risk in an area. Still another major problem is the aggregation level of these population estimates. The census ‘block group‘ is the smallest numerical unit that the US Census Bureau reports to the public. By definition, the block group typically consists of a neighborhood of between 600 and 3,000 individuals where estimates of its values are extrapolated through reports from a representative fraction of the area. Finally, in a 2015 study on population density modelling in support of disaster risk assessment, the authors conclude that “block groups are not fine enough to be suitable for specific hazard analysis.” While many planners attempt to break down these manipulated night-time population estimates by factoring a simple percentage of an area, there is no statistical support for such assumptions. In fact, the foundation of the referenced work by Tenerelli, et. al. describes specific ‘downscaling techniques’ using intensive proxy attributes to give clues for any justifiable disaggregation of coarse population statistics. Most of these techniques are far more involved than percentages and have value only when no other population measure is present.

Today, the near real-time visualization of population surges that quantify the urban influxes at the start of the work day and their subsequent retreat into suburbia for the evening are becoming a reality. Dynamic population movement can now be mapped using anonymized mobile phone data. According to a 2017 Pew Research Center Fact Sheet, it is estimated that “95% of Americans own a cell phone of some kind” (and well over 75% have devices that are classified as “smartphones”.) Since every one of these devices must regularly ‘ping’ a tower in the cellular network, these signals open bold new opportunities for tracking, visualizing and even analyzing population movement forming an important layer in the dynamic risk of any community with a fidelity far greater than the census block group.

Generic population measures are a great start, but not all people are similar when factoring risk. Some populations are more vulnerable than others. Families that live in flood zones, for instance, have a greater exposure for both life and property loss during heavy rain events. Those who live in large housing complexes with limited egress may also be unfairly disadvantaged during a significant event that requires evacuation. Socioeconomic factors can also limit access to current information or an individual’s ability to react to it. Beyond raw numbers of bodies, we must be able to classify groupings of individuals and label their vulnerability.

There are many other sensors in a community that can also be leveraged in modelling the dynamic nature of risk. The risk for flooding is dependent on a source of water input. Rain gauges within your watershed can define the amount of water added over a measure of time. Stream gauges measure the depth of water in a channel and can inform you of the likelihood of imminent flooding. Increasingly, these sensors are becoming part of the Internet of Things (IoT) that allow remote access of real-time data. Even layers of data that are often considered to be static can have variability capable of being modeled. A school, for instance, is usually categorized as a ‘high risk’ asset, but is it always at the same risk level? The actual risk experienced is far lower during summer months or on weekend evenings. Conversely, its risk status may go even higher than normal on certain Friday evenings when the home team is playing a championship game and entire families gather in addition to the normal student population. Similar to pre-plan floor layouts or construction analysis, the use patterns of a building can be noted and input to a dynamic risk model. The increased effort of data collection should be more than repaid by the acute knowledge gained for steering protection decisions.

The reason we do not make more effort to realistically model the threat to our communities is not because it is difficult, but because we simply have never done it that way before. The technology to visualize changing demand and automate recommendations for responding to it has long been proven in the EMS world. The rebuttal is often that the fire service is different. However, simple modifications of existing software provide mobile access to risk as a spatial surface of probability on a user-selected basemap of imagery, topography, or cadastre for incident management or support in apparatus move-up decisions. Modification of the dispatch software to recommend not just the closest ambulance but the most appropriate response package of apparatus based on incident reporting is also being made. The Mobile Area Routing and Vehicle Location Information System™ (MARVLIS) by BCS is leading the movement to change the management of fire apparatus, not just as another point solution, but a significant new platform for visualizing your community and better protecting it.

“Risk” is defined in the Business Dictionary as “the probability or threat of damage, injury, liability, loss, or other negative occurrence.” The threats that face any neighborhood (or fire planning zone) are never constant. We must re-evaluate these time dependent risk factors and re-imagine the information flow used in making decisions that respond to knowing the time-dependent threat. If you only report call history as daily averages, you are ignoring the role that reality plays in your responses. Action as simple as viewing call demand by the 168 hours of each week will provide a clearer image of the routine daily patterns that exist. And these patterns are likely to be different during each season of the year or, at the very least, in comparing the months when school is in session against the months it is not. I recognize commuting changes in my own neighborhood the very day school opens and again on the day after it closes each year. If you can see that too, why are you not making efforts to adjust response potential to these realities?

While public safety is not a traditional ‘business’, it can learn a great deal from business leaders like Warren Buffet who said, “part of making good decisions in business is recognizing the poor decisions you’ve made and why they were poor.” We can do better and that is exactly why we should.

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Split-Second Destination Decisions

This past Sunday night about 2245 hours, a Detroit police officer was shot in the head while responding to a domestic violence call. The Detroit Free Press, in an article identifying the shooter, reported that the incident happened at an apartment complex in the vicinity of the 10000 block of Joy Road near Wyoming Avenue on Detroit’s west side. According to Channel 4 News in Detroit, Rapid Response EMS was dispatched and arrived on scene in less than 60 seconds. The officer was transported, with a police escort, to a level two trauma center,  as reported by another local news source, with the patient being handed over within 22 minutes of the original dispatch. Now, Detroit Police Chief James Craig is asking why the injured officer “wasn’t taken to the closest hospital.”

These are the facts as I have been able to glean them from multiple news reports and summaries. I have no inside knowledge of this particular incident or even any great understanding of Detroit in general, but I believe there are several interesting questions worth a larger discussion here from the perspective of a complete outsider. For those who may have more inside information of this situation, I will point out that I have no interest in any past conflicts that this particular EMS provider has had with the Detroit Fire Department or a memo now resurfacing from an incident last October specifying that injured “Detroit firefighters will be transported by Detroit EMS only.” That is a totally separate matter that relates potentially to medical care, not destination decisions.

A FOX 2 news article made a statement that they are “still looking into why a critically wounded Detroit police officer was taken to a hospital in Dearborn– when there were two hospitals that were closer. At least one of the hospitals that was passed up is better equipped to deal with a gunshot wound to the head [emphasis added].” Another article, updated during my research today, has since made a correction stating, “This story has been updated to clarify that an ambulance driver [sic] did not pass any hospitals while transporting a wounded Detroit police officer.”

The question we in EMS are often forced to answer is what facility is “closest,” however that question does not always have a static answer from every incident. One of the first articles I read on this case printed a thumbnail map (since removed) similar to the image on the left (which you can enlarge by clicking on it.) What immediately struck me was that the shooting occurred at the center of a triangle formed by the three “closest hospitals.”

If we consider distance to be “as the crow flies,” or perhaps more appropriately, “how the medical helicopter flies.” We will get one set of distances and travel times. Here, Henry Ford to the east appears closest in straight-line distance followed by Sinai-Grace to the north and finally Beaumont in Dearborn to the southwest. If we consider road miles of the shortest path, the order changes with Sinai-Grace at 4.8 miles, Henry Ford at 5.6 miles and lastly again, Beaumont at 6.8 miles. However, if distance is measured in drive-time, specific values change (according to my tests using Google Maps) depending on the amount and direction of flow of traffic. In all of my time tests, Sinai-Grace came in dead last due to the number of local street segments traversed and I suspect a large number of traffic signals. These typically narrower streets and signaled intersections are not only slower to travel, but more dangerous when traveled using red lights and sirens (some studies will show this is especially true with a police escort.)

The travel times in my daytime investigation during a typical work week varied with the other two destination hospitals and probably would still be different from a late drive on a Sunday evening. Of some significance in comparing the “best routes” is the number and direction of turns. As a general rule, right turns are safer than left turns which must cross opposing lanes of travel. The other consideration is the speed limit of the roadways. While I assume the ambulance was travelling above the posted rate going code 3, it is the faster roads that are built to a higher level of safety and will more easily accommodate higher speeds with fewer traffic control devices (lights or stop signs.) The route to Beaumont had the highest number of miles on restricted access highways that have the highest speed limits in any city. Consequently, this may have been a very good choice based on actual travel-time as well as safety considerations.

The call was still a judgement one and I will not defend one or the other as the best choice given my lack of knowledge in Detroit, but I will defer to the judgement of crews that travel these streets regularly both as emergent and routine traffic.

The other consideration in this call was the trauma rating of the hospital. As I understand it, both Henry Ford and Sinai-Grace are level 1 while Beaumont is only a level 2 facility. Given the severity of the wound, some deference would likely be given to the better equipped hospitals. However, the real difference between these levels is typically whether there is a teaching and research program available. The surgical capabilities should actually not be significantly different.

With drive times so close to being similar, I can sit comfortably in the safety of my arm chair typing that the choice of Henry Ford would have been quite practical; however, I may well have made a different choice myself as I place myself behind the wheel (as I will be doing tonight.) That immediate “split-second” decision of east versus south west is much more difficult in the moment. And this is exactly the type of situation where I would be grateful for the input from the MARVLIS in-vehicle client that sorts destinations choices by distance and provides an optimal path based on time-of-day with turn-by-turn driving directions.

 

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What 'Level Zero' Really Means in EMS

Rampart, Medic 13 with an incoming patient report.”

Go ahead, 13.”

I have a patient with a pulse of 120. ETA less than 10 minutes. Over.”

Well, this sort of report certainly leaves something to be desired. What is the age of the patient? For an infant, this may be a normal rate, but in a geriatric person it could be a bigger concern. Has the patient been involved in any physical activity? If the subject just completed a marathon it may not be a concern, but if the patient had been sitting on the couch watching TV and the pulse suddenly spiked, it could be a legitimate emergency. In any of these cases, we still need more information. The patient’s blood pressure would be another good measure along with age. Some OPQRST or SAMPLE would be enlightening too. A treatment, let alone a diagnosis, cannot be advised from this single piece of data.

In a very similar vein to our pulse example, there have been several articles written lately bemoaning the dangers of any particular EMS system having hit a ‘Level Zero’ situation some number of times in the last however many months. For instance, there is an article where San Bernardino firefighters attack AMR. Don’t misunderstand my point, not having any ambulances available can definitely be a serious situation, but how long does the situation last in each occurence? In any significant service area, its bound to happen at some point even with proper planning and normally adequate staff. My concern is the media attention over this single measure of an emergency health system. It may be that reporters finally got the message that response time was not a good defining metric by itself. But just like our bodies, an EMS organization is a complex system of interoperating systems. Performance is not defined by any single measure. Although individual metrics, however, can cause us to want to look deeper to understand the likelihood of potential serious problems.

A case in point is a story last year on Paramedics Plus in Sioux Falls, that revolved around two specific cases where an ambulance was not available for patients in distress. While this is not ever a desirable position, the compliance of the ambulance provider in question was 95% and even the investigative news reporter found that EMS arrived before the fire department’s own ”first responders” in 25% of cases. Perfection is simply not easy to maintain. While not making light of any potentially serious situation, my intention is to place this measure within some context, just as a sole pulse reading is only a singular measure of performance and one that is not meant to be interpreted by itself.

The MARVLIS application, in use by almost every member of the AIMHI (Academy of International Mobile Healthcare Integration) organization (formerly known as the Coalition of Advanced Emergency Medical Services or CAEMS) is often viewed as a tool for improving response times. While it has proven to be beneficial in achieving that goal, that is not the only reason these “high value” systems use it. Improving individual response times also improves compliance. Consistently short response compliance can also have clinical value if the times are low enough in the right situations. Jersey City has correlated a response time near 4 minutes to improved ROSC. But other benefits are improved value in post moves. Not moving ambulances for the sake of changing posts, but in positioning units closer to their next call with fewer moves. This also means fewer miles driven with lights and sirens to improve crew safety. Mobile Medical Response (MMR) credits MARVLIS in their annual report with reducing their costs associated with unloaded miles driven. As a collection, these improvements mean more than any single measure.

The reality is that our profession is fundamentally changing. We are coming from an EMS world where measurements of specific vital performance are evolving into a diagnosis of value. Just as good vitals indicate good health, positive measures of performance will be interpreted as higher value. In the same way that a general impression should guide a clinician in measuring vital statistics, the evaluation of an EMS should also be guided by a broader vision of value rather than a microscope trained only on specific measures.

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