Category Archives: Fire Dispatch

BCS Releases MARVLIS Version 4.5

Dale Loberger                                                FOR IMMEDIATE RELEASE: 9/13/22

BCS, Inc.

(803) 641-0960

dloberger@bcs-gis.com

BCS Releases MARVLIS Version 4.5

MARVLIS 4.5 Available Featuring Significant New Features and Updates

Aiken, SC: BCS today announced the release of MARVLIS version 4.5. This major release provides new and updated features focused on our rapidly changing world. Incident recommendation has been expanded in scope and complexity, adding tiered recommendations to get the right resources to the right place even when resource counts are running low. Incident recommendation now also supports response packages for those incidents where a single resource is insufficient. This release also contains tools to simplify MARVLIS database deployment and adds support for multiple MARVLIS systems running on a single database instance. Finally, this release contains improvements in the Dashboard report and help system, NETCall functionality, and the MARVLIS technology stack.

“The evolution of MARVLIS to version 4.5 is yet another example of our dedication to innovation in the Public Safety sector. This new release expands on MARVLIS’s position as the complete solution to control, route, and manage resources across the entire agency. Communication centers will save time and reduce manual steps with new features like tiered responses and response packages”, says Tony Bradshaw, President at BCS. “The latest version of MARVLIS NETCall is a game changer for the efficient management of non-emergency resources and provides technology to optimize trip assignments to maximize profitability.”

Features and benefits of MARVLIS 4.5 include:

  • Added Incident Recommendation module support for tiered recommendations and response packages
  • New Query Sets to create vehicle and incident queries for incident recommendations
  • Dashboard pages now include context-specific help links
  • MARVLIS Database now supports multiple MARVLIS systems running on a single database instance
  • Updates to Playback, Post Coverage, and Incident Recommendation Reviewer Dashboard Reports
  • Added support for password complexity 
  • Updated technology stack includes:
    • MARVLIS Client updated to support ArcGIS® Runtime 100.13
    • MARVLIS Dashboard updated to support jQuery® 3.6.0 from 3.3.1
    • MARVLIS Dashboard updated to support the ArcGIS® API for JavaScriptTM 4.23
  • Added support for routing with live traffic in Canada using the TomTom® Real Traffic Feed
  • NETCall updates to support revenue information in processing and numerous user interface enhancements

MARVLIS 4.5 is now available and is included as part of annual maintenance for existing MARVLIS customers. If you’d like more information or think that MARVLIS might be the right solution for your organization, please email sales@bcs-gis.com or visit https://www.bcs-gis.com/marvlis.html.

About BCS, Inc.: Founded in 1998 in Aiken, SC, BCS develops solutions to help organizations leverage technology and strategies to improve operational performance and delivery of time-critical resources, services, and management of non-emergency transportation. Visit us at bcs-gis.com

About Esri: Esri, the global market leader in geographic information system (GIS) software, location intelligence, and mapping, helps customers unlock the full potential of data to improve operational and business results. Founded in 1969 in Redlands, California, USA, Esri software is deployed in more than 350,000 organizations globally and in over 200,000 institutions in the Americas, Asia and the Pacific, Europe, Africa, and the Middle East, including Fortune 500 companies, government agencies, nonprofits, and universities. Esri has regional offices, international distributors, and partners providing local support in over 100 countries on six continents. With its pioneering commitment to geospatial information technology, Esri engineers the most innovative solutions for digital transformation, the Internet of Things (IoT), and advanced analytics. Visit us at esri.com.

About TomTom: At TomTom we’re mapmakers, providing geolocation technology for drivers, carmakers, enterprises and developers.

Our highly accurate maps, navigation software, real-time traffic information and APIs enable smart mobility on a global scale, making the roads safer, the drive easier and the air cleaner.

Headquartered in Amsterdam with offices worldwide, TomTom’s technologies are trusted by hundreds of millions of drivers, businesses and governments every day. Visit us at tomtom.com

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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 andTuretsky stated in 2006 that “(people) are the dominant source of ignitions except in sparsely populated regions.” Our troubled standard for measuring population is thedecennial 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 onpopulation density modelling in support of disaster risk assessment, the authors conclude that “block groups arenot 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. Evenlayers 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 youve made and why they were poor.” We can do better and that is exactly why we should.

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Analyzing Routes and Response Times

This is a second preview chapter of a new book in the Primer series from Bradshaw Consulting Services to be titled “Closest Vehicle Dispatch: A Primer for Fire? to be released in time for the FDIC 2017 at the end of April.

Whether you are held to the standards of NFPA 1710, which addresses predominately career fire department responses in the US, or NFPA 1720, which deals specifically with volunteer departments, the challenge of meeting these response time standards is increasingly difficult for many reasons. Higher demands on limited resources and increasing performance expectations from the public are just a couple of those forces opposing response efficiency. Another elementary factor that critically impacts our response times is the route we choose in order to arrive at an incident. In most cases, there is not always a single route that is consistently the best choice at all times of the day or week. These differences can also include seasonal variations or be complicated by special events which may be planned or unplanned (Demiryurek, 2010). The subjectivity of route selection is further complicated by dynamic characteristics such as traffic or weather in addition to the extent of the mental map we develop of a service area or what that map may be lacking in adjoining or mutual aid areas (Spencer, 2011).

Most of the considerations that we process as we consider a potential path of travel in an emergency vehicle are often made subconsciously through personal experience and knowledge. While there is no legitimate argument against knowing your service territory well, the question becomes do we have sufficient awareness to consistently make the best route choices?

According to U.S. Fire Administration statistics for 2005, responding to alarms accounted for 17 percent of firefighter on-duty fatalities (Response, 2007). Deaths in road vehicle crashes are often the second most frequent cause of on-duty firefighter fatalities. In 2014, this percentage dropped to only 10 percent with a total of just 7 fatalities. Although the change is positive, it is too early to consider this to be a trend since it is only the second lowest number of crash deaths over the past 30 years (Fahy, 2015). While these accidents are not all due to their route choice, it can be argued that there are times where crews were clearly in the wrong place at the wrong time. Furthermore, the shortest path is not always the quickest route, and the fastest one may not have the simplest directions either (Duckham, 2003). Given the technology and data available today, there is little doubt that we can make strong decisions provided that we understand how we make these choices and what information may improve them.

In selecting a route for any particular apparatus, we may consider the physical or geographic characteristics of the roadway that determine the maximum speed of travel based on the maneuverability and size of our apparatus. Similarly, we must consider the likelihood of traffic congestion and also the safety of our crews as well as the public. As we increasingly rely on algorithms for making driving decisions, it is important to appreciate the mechanics of how the technology components function together. The Global Positioning System (GPS) is often credited with providing guidance to vehicle operators, but this is not exactly true. The satellite constellation that makes up the American-operated GPS (and similarly the European GLONASS) simply sends accurate time signals by radio waves to our portable receivers who detect the length of time each signal has traveled through space and then triangulates a position based on the calculated distance from those man-made stars (Hurn, 1989). The accuracy of the position that your GPS unit determines is based on the quality of those signals received and the precision of the local clock used to compare the time encoded in the signals. These satellites have no concept of transportation networks or traffic congestion on earth. It is Geographic Information Systems (GIS) that model the street networks and also track the vehicles using them. Unlike the limited number of GPS-like constellations in space that help us derive our position, there are a multitude of GIS-based computer services that offer routing recommendations. Some of these services, like the consumer-based routing applications available on your smartphone, are located on “cloud servers? (although they are quite terrestrial) while others may be hosted privately on local government networks and available only to “trusted client? applications on your Mobile Data Terminal (MDT).MARVLISiOSinFD

Each of these GIS services has unique embedded algorithms for recommending directions or to estimate arrival times (Keenan, 1998). As users of these systems, we become subject to the specific assumptions inherent within their design leaving them far from being equivalent to one another (Psaraftis, 1995). For instance, network models must account for the elevation differences of overpasses in relation to the roadway below in order to prevent suggesting that a vehicle take a turn off of the side of a bridge. The cost of that ill-fated maneuver would be insurmountable, but other legitimate turns have minor costs associated with them because the apparatus must slow down to navigate the curve safely. A traffic light, or oncoming vehicles, can add further to that turn delay. Accounting for these delays requires logic in the GIS routing algorithm as well as valid time estimates coded into the street network data at each intersection.

The most basic feature of any transportation network model, however, is the cost of movement along a road segment in either direction which is known as its “impedance.? Many systems will assume the speed limit over the distance (impedance_time=speed/distance) between intersections to derive a similar “drive time” in both directions. Real world conditions (including traffic, terrain, and weather) will prove that speed limit-based assumption to be overly simplified and can lead to poor routing decisions because of unrealistic impedance values in the model (Elalouf, 2012). Crews will quickly recognize these failures and the lack of trust that these errors engender can compromise the entire routing program. Realistic impedances should be variable based on the time of day or day of the week in addition to the direction of travel.

More complex online routing services now offer near real-time traffic updates. While this traffic feedback can be invaluable to most drivers, its practicality to emergency vehicles appears limited in general. If our task was to deliver pizzas, we would be constrained by normal traffic regulations. Knowing where traffic congestion is at any given moment would allow us an opportunity to seek an alternative to bypass a congested intersection. This is a common type of need for drivers and therefore many consumer routing apps seek to address that specific function (Ruilin, 2016). But when our duty is to respond to the accident at that same intersection that is causing the delay for others, these typical consumer routing applications may fail our unique requirement. This objection is especially valid where emergency vehicles are not strictly constrained by the driving patterns of other vehicles on the roadway. In certain situations, it may be allowable for an apparatus to use the road shoulder for travel or even cross a median to use an on-coming traffic lane or to traverse a one-way street in the wrong direction (Harmes, 2007). The only reasonable exceptions to this generality are those dense urban areas where congestion is excessive and these “open” lanes or roadway shoulders simply do not exist to allow apparatus to circumvent that traffic. In a recent trip to New York City, I visited a fire station in downtown Manhattan. They received a call and exited the station with red lights and sirens blaring, but even the air horn was unable to move traffic. The engine sat at the traffic light behind the rest of the cars until the intersection cleared enough to allow drivers to create a path up to the next intersection.

In general, when we look to leverage technology for our unique demands in public safety, a system would ideally be able to learn our peculiar patterns of travel and record typical impedances based on how our own fleet resources travel. Additionally, these impedances will likely be different during certain hours of the day or on specific days of the week and vary even further seasonally based on whether school is in or out of session. These cyclical patterns will have a huge impact on actual drive times and any route recommendations must account for them accordingly. Current consumer routing applications are continually improving their ability to recognize and address the needs of passenger cars or ordinary delivery trucks, but this still does not necessarily translate to better routing of emergent public safety vehicles in most cases.

Finally, the last critical piece of route selection is a review after the call. Comparing the actual route traveled with the recommended path is an important feedback mechanism to both ensure that the system is operating as intended and to build confidence within your crews that encourage them to trust the system. This is not to suggest a blind obedience to technology, but constructing a learning process for everyone in developing tools that function to improve overall performance. No technology is perfect in the real world, just as no person has ultimate knowledge at all times. But cooperatively, we can learn to make improvements in either the computer or human systems as needed to enhance awareness in the other. The most successful implementations of routing assistance create cooperative relationships between responders and the GIS staff responsible for maintaining the data. Failures discovered in any system should not be used to condemn an otherwise useful technology, but seen as opportunities for improvements in either the algorithms behind it or the data that fuels it.

One of the critical outcomes of route selection, aside from arriving safely, is the total time of travel. No matter when the clock starts for measuring your response time, it is the minutes and seconds that the wheels are rolling that often consume the majority of it. The longer that time or distance, the higher the cost. A cost that can be measured both in actual vehicle operating expenses as well as the risks associated with its operation; not to mention the losses adding up on scene prior to your arrival. In general, the shorter the time (and distance) between dispatch and your safe arrival on scene, the better it is for everybody.

 

References:

Demiryurek, U., Banaei-Kashani, F., Shahabi, C. “A case for time-dependent shortest path computation in spatial networks.” GIS ’10 Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, November, 2010; 474-477.

Duckworth, M., Kulik, L. “’Simplest’ Paths: Automated Route Selection for Navigation in Spatial Information Theory.” Foundations of Geographic Information Science. (2003) 169-185. Berlin: Springer-Verlag.

Elalouf, Amir. “Efficient Routing of Emergency Vehicles under Uncertain Urban Traffic Conditions.” Journal of Service Science and Management, (2012) 5, 241-248

Fahy, R. F., LeBlanc, P., Molis, J. Firefighter Fatalities in the United States-2014. NFPA No. FFD10, 2015. National Fire Protection Association, Quincy, MA.

Harmes, J. Guide to IAFC Model Policies and procedures for Emergency Vehicle Safety. 2007. IAFC: Fairfax, VA.

Hurn, Jeff. GPS: A Guide to the Next Utility. (1989) Sunnyvale: Trimble Navigation.

Keenan, Peter B. “Spatial Decision Support Systems for Vehicle Routing?. Decision Support Systems. (1998);22(1):65-71. Elsevier, Salt Lake City.

Psaraftis, H.N. “Dynamic vehicle routing: Status and prospects.” Annals of Operations Research (1995) 61: 143.

Response-Time Considerations.? Fire Chiefs Online. ISO Properties, 2007. Web. 20 May 2016.

Ruilin, L., Hongzhang, L., Daehan, K. “Balanced traffic routing: Design, implementation, and evaluation.” Ad Hoc Networks. (2016);37(1):14-28. Elsevier, Salt Lake City.

Spencer, Laura. “Why the Shortest Route Isn’t Always the Best One.? Freelance Folder, November 2011. Web. 7 December 2016.

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The Fallacy of the "First Due" Area

The following is a preview of a book coming soon from Bradshaw Consulting Services to be titled "Closest Vehicle Dispatch: A Primer for Fire" which is a follow-up to "Dynamic Deployment: A Primer for EMS".
Watch for the new release in time for the FDIC 2017 conference at the end of April.

The modern legal definition of response zones can be found in the Code of Federal Regulations, which states that the “first-due response area is a geographical area in proximity to a fire or rescue facility and normally served by the personnel and apparatus from that facility in the event of a fire or other emergency.? (44 CFR 152.2) This banal definition glosses over some very interesting history in the development of modern professional fire departments. In the mid-nineteenth century, there were frequent, and often bitter, disagreements over territories that sometimes resulted in physical confrontations. In fact, the politically powerful New York City volunteer fire companies of that era were known to send out runners ahead of the engine in order to claim the right to fight a particular fire and thereby receive the insurance money that would be paid to the company who fought it. While the monetary incentives are not nearly so direct today, there is still a great deal of pride invested in being the “first responders” to an incident. It would not be a difficult argument to make that we haven’t changed as much as we would like to think in regards to response.
A retired fire chief recently relayed a story to me about an engine crew that raced through a residential neighborhood in order to beat another engine that had been dispatched for mutual aid since the “first due? engine was out of quarters returning from another call. The need was so great to be the first responding company in “their own area? that they willingly disregarded the safety of the public that they had sworn to serve simply to avoid the embarrassment of being second to a call that was “rightfully theirs.?
The concept of the “first due” area is a strategy to automate a century-old manual concept of pre-assigning the closest resources to specific structure addresses within a fixed response area. The thought that a central station will have the closest apparatus to any potential fire in their district is simple, but with the increasing complexity of urban transportation networks, it is also an increasingly simplistic idea. The reality is that traffic patterns, and increasing traffic congestion, can dramatically change response times, particularly in high density population areas.
Public safety vehicles, even those running emergency traffic, can sometimes struggle to reach the posted speed limits at certain times during a shift. Alternatively, a lack of traffic at other times will permit the discretion of rates above the normal traffic speed. These periods of diverse congestion levels exist not only for intermittent periods of time but can vary dramatically by the direction of travel as well. Additionally, these temporal and directional impacts are confounded by the fact that station locations are often inherited positions that were designated many years earlier when housing, demographic and development patterns were very different from today. In most areas, fire station placements have grown through ‘incrementalism’, often tainted with political influence. In some jurisdictions this inheritance may go back over a century or more. Not all current station locations are the result of some forward-thinking intelligent design. The result of fixing address assignments to these past growth patterns may, or may not, represent who will be able to arrive first on the scene with the right resources. Furthermore, the common overlap of nearly a third between each of multiple urban engine companies means that when they are each dispatched from quarters, the next few arriving fire units, under normal conditions, will likely have a similar response time to that of the “first due? apparatus.

 

The “effective service area? of any station will vary during different times of the day based on traffic congestion. On a typical morning, as most traffic is heading toward a downtown business district, an urban station located at the city center will be able to travel outward toward the suburbs with relative ease. At the end of a normal business day, that same station will find that it can no longer travel as far in the same direction in the same length of time. Any sort of break in the normal business routine will further alter that pattern. These exceptions can include weekends, holidays, or special events. Most areas will also experience seasonal changes to traffic as a result of adding school buses or tourists to the roadways. The result of traffic is the evolution a unique “first due? area for different hours of the day and days of the week during different months of the year. A “fixed?, or “average?, first due area must either ignore, or at the very least, generalize the pressures of these growing realities.

 

Generalizations of Effective Service Areas as Impacted by Primary Traffic Patterns
Morning                                                                     Afternoon

Rzones1      Rzones2

During a typical morning “rush hour? period, the heaviest traffic may be to the north and west as in the left example making response in that direction relatively more difficult than moving to the south and east. Consequently, the effective response zone represented in gray around two example stations will compress moving with the traffic and elongate against that traffic. In the afternoon, this pattern will reverse since the heaviest traffic would now be moving away from the downtown area making response to the south and east slower as compared to the morning pattern and therefore reforming the effective service area in the opposite direction.

The dispatch of a theoretical “persistently closest resource? is made even more difficult when we consider that an increase in call volume makes it increasingly common for an apparatus to be dispatched when it is already out of its assigned station, either on or returning from another alarm. With an increase in call volume, the chances of another call leading to a dispatch before a unit has returned to its station are only increasing. These moving vehicles will have a significantly different effective service area and a different proximity to an incoming alarm when compared to an apparatus that is currently parked in a given “first due? station. Additionally, the “chute time? in preparing the crew to respond is completely eliminated when the dispatched vehicle is already moving. In this case, the effective response area is larger when considering response time than an apparatus that is parked at its station. However, this dynamic nature of the responding vehicles can also work against the efficiency of a traditional “first due? response. Consider that an apparatus may be available after clearing an alarm at some extreme point within its district when a call is received from an opposite extreme location. The mere fact that the responding vehicle is moving may still not overcome the greater distance that places it significantly further from that next alarm than an apparatus that is parked elsewhere. In this case, the closest unit may well be one outside of the assigned primary response area.

Impact of Increasing Call Volume on Effective Service Areas

Rzones3

When an apparatus clears a call, it becomes available in a different location than the station and although it is capable of responding with a “zero chute time”, its distance from the station will impact its effective service area possibly putting it further away from the “next call” than a neighboring station “in quarters”. As call volume increases, the likelihood of being dispatched while returning from another call only increases.

These changing logistical dynamics significantly alter the performance realities for modern fire stations from simple planned service delivery to a complex system of matching dynamic resources to increasing demand. Meeting the expectations of your community requires more than the historical paradigm of “first due? scenarios assisted by mutual aid to that of a cooperative system approach designating primary and secondary response functions on-demand and independent of an arbitrary enforcement of outdated patterns of convenience. Fire departments must literally become dynamic fire services requiring an intelligent coordination of these mobile resources.

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Static v. Dynamic: A Continuum of Cost

In our recently published book, “Dynamic Deployment: A Primer for EMS“, John Brophy and I established a dichotomy between the standards of static deployment and dynamic deployment in the very first chapter.  Fortunately, that strong polar perspective has spurred some interesting discussions for me. While the check-out lane analogy was effective in distinguishing some of the differences of static and dynamic deployments, its simplicity only recognized the extreme ends of the spectrum and failed to acknowledge what I would describe as a “Continuum of Cost” between them.

Few systems (at least those with more than just a few ambulances) probably function exclusively at either extreme. The static model will necessitate some flexibility to provide “move-ups” to fill holes, just as dynamic systems will have reasons to keep specific posts filled as long as enough ambulances are available in the system. The reasons for moving, or even fixing locations, may have something to do with demand necessity or even the political expedience of meeting community perceptions.

While there are many differences between static and dynamic deployments that we could discuss, there are also some elementary misconceptions. For instance, dynamic deployment does not mean vehicles are constantly in motion. The term dynamic refers to the nature of their post assignments which can vary between, and even within, shifts. As alluded to in the book, proper post assignments also reduce, not increase, operational expenses. In at least one example we stated, the dynamic deployment strategy was shown to significantly reduce the number of unloaded miles actually driven, which in turn increases the percentage of overall miles that can be billed. This situation not only increases revenue while simultaneously reducing expenses, it also reduces fuel costs and wear on the vehicles (and crews) too which potentially extends their useful life. All this is still in addition to reducing response time and improving crew safety by positioning ambulances closer to their next call so that fewer miles need to be driven under lights and sirens.  The inherent efficiency of this management strategy allows a system to achieve response compliance at the 90th percentile with the smallest possible fleet.  To achieve the same compliance level with a static deployment of crews and posts, the fleet must grow significantly larger. Another recent sample calculation showed that both staff and fleet size would need to grow by well over double in order to reach the same goal. The resulting cost continuum, therefore, clearly shows that a static fleet has operational and capital expenses multiple times the costs of the dynamic deployment model without burning crews out with excessive and unhealthy UHU figures.

For the sake of validating my argument, it is unfortunate that these examples are from private ambulances companies who do not wish to openly share details of their calculations at this time for competitive reasons. It would be safe, however, to assume from these competitive reservations that these results are not automatic, but dependent on proper management and the use of good tools. There are certainly numerous examples of poorly managed systems or ineffective operational tools. To achieve similar positive results in your own system requires certain knowledge, an underlying reason for having written the book in the first place, and an assurance that the deployment tools are proven to be effective.  Just as managers should have references checked during the hiring process, vendors of operational deployment tools should be able to provide ample references for successful implementations of their technology in comparable systems to your own. It is also important that any solution be able to address a continuum that includes your specific objectives to find a balance between geographic coverage with anticipated demand coverage at an acceptable workload and schedule for your staff.

There is no “magic bullet” to achieving operational nirvana, but the combination of effective management with operationally proven tools has shown that cutting costs while improving performance is an achievable goal in most any size system. It is also fair to say that performance can be enhanced with less skill through the application of significant sums of money; but honestly, who can afford that sort of strategy in the competitive arena of modern mobile integrated healthcare.

It is our desire to produce yet another, even more extensive, volume on the topic of dynamic deployment to make the achievement of efficient and effective high performance EMS a reality for more systems. Stay tuned for future details!

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Is 'SSM' Still a 'Bad Idea'?

Ideas often take time to saturate a market. Even if the idea is generally recognized as a good one, complete with compelling evidence, change can still take time.  As a current example, how many agencies still have a protocol for complete spinal immobilization on a long spine board for “any fall” or “significant impact”?  On that very point, Dr. Ryan Jacobsen puts forth a lengthy argument in this recording of a  presentation at a NAEMSP conference.  The process of acceptance can be even worse yet if the idea has been controversial – as in the case of “System Status Management” introduced by Jack Stout in 1983. This distinction means it takes longer still in order for it to receive a “fair hearing” even if the evidence now shows a positive impact. In an ideal world, the best ideas would always be automatically and universally adopted, but that simply isn’t how the world works.  And for any professional industry it is a good thing that ideas are properly “vetted”over time to determine what is truly “best” before wholesale adoption or, in the case of “bad ideas”, that they are discarded only when a fair reading of the evidence discredits them.

CycleDynamicsGartner, Inc. of Stamford, Connecticut, has built both a reputation as an information technology research and advisory firm and a booming business of annually publishing their signature “hype cycle? graphs by industry segment.  For those unfamiliar with these charts, the basic structure starts with a technology trigger near the origin of time and is visibility followed by a quick rise to the “peak of inflated expectations” that is often driven by a combination of unrealistic claims by proponents and the hopes of users desperate to believe those claims.  The exaggerated peak of hype is inevitably followed by a crash of popularity into the so-called “trough of disillusionment.”  Many ideas just die here and drop off the curve, but for others, a more realistic set of expectations develop as ‘believers’ (the “early adopters” according to Everett Rogers’ “Diffusion of innovations”) begin to experience measurable benefits and serves to push the idea (sometimes with changes) up the “slope of enlightenment.” This gradual advance passes an important point of inflection on the performance “S” curve known as the “attitude confirmation” identified by Joon Shin.  The next landmark is crossing a social “chasm” identified by Geoffrey Moore at another critical inflection point called the “attitude plateau.”  Once an idea successfully crosses the chasm, it plateaus as a generally recognized productivity concept for that industry. Some ideas fly quickly along these curves passing other older ideas that seem to just plod along at a much slower pace.

So, is “SSM” still on the curve? And if so, where is it?  We must first realize that ideas evolve and sometimes morph into other names (just as “Emergency Medical Services” is known by some as “Mobile Integrated Healthcare” now.)  One apparent synonym for “SSM” is a broader idea of “dynamic deployment.”  If we look at the literature and practices of emergency ambulatory services, we find that the underlying concept is still quite popular despite attempts of detractors to further discredit or simply ignore it.  One such potentially damning article was written by Bryan Bledsoe back in 2003 after a crash of industry expectations for the idea.  This could easily be explained as the time that SSM passed its own pivot point where its value was questioned in the trough of disillusionment. (Some may also claim that hypothermia treatments for cardiac patients was also recently in this trough.)

Computing performance has increased dramatically since the 1980’s (or even the early 2000’s) and algorithms are discovering patterns in many human activities.  Demographic data show socioeconomic clustering that leads to similar health issues and traffic patterns with road designs that see more accidents than they should. These patterns are proving to be key in forecasting demand for EMS services. Automated Vehicle Location systems allow far better tracking than ever before and traffic patterns are being used to calculate more realistic routes. These are some of the advances that help explain the numerous agencies that are significantly improving response performance and making use of resources. Where field providers take an active part is developing strategies, there are also reductions in post moves, unloaded miles driven, and better disbursement of work loads.  The efficiency gained by its use in mainstream agencies beyond the initial public utility model organizations seem to vindicate Stout’s early vision and research as the concept moves up the slope of enlightenment toward the plateau of general acceptance.

Ideas are not static entities, so our understanding must continue to evolve and incorporate new thoughts.  As the iconic American social commentator, Will Rogers once said, “even if you’re on the right track, you’ll get run over if you just sit there.”  So, to honestly argue an idea, proponents of either side must continue to evolve their understanding and witness the current thought and evidence of an idea.  There is little point in continuing to attack past grievances which have been addressed while ignoring the mounting evidence out of sheer disbelief.  If “SSM” is not a “good idea’ yet, it is certainly moving in that direction all the while being shaped by those who are concerned over the future of EMS (or MIH.)

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What is "Performance" in EMS? Part 1

It is that time of year for resolutions and reflection. As I ponder this thought, the topic that sticks out to me is about what really constitutes a “High Performance EMS.” As we look back over the past year of the High Performance EMS social network (including our Twitter and Facebook feeds as well as this blog) one of the recurring comments that disturbs me is that “response time doesn’t matter.” This causes me concern in two ways – first, that the primary measure of performance is overwhelmingly always “response time? and the other is that this simple measure is deemed to not really be important. So, for the next few posts, I will discuss various characteristics that I feel do matter in becoming a truly high performing EMS system.

Part 1: Response Time

This past February, Elsevier published an excellent newsletter (EMS Insider, Volume 39, Number 2) focused on EMS response times and included articles such as “The Great Ambulance Response Time Debate Continues? in which the author, Teresa McCallion, laid out many of the facts. For instance, the article recites the “MedStar example” from Super Bowl XLV suggesting that very few EMS calls” in that prospective two week study actually “required an immediate response. It is important to note that this statement did not go so far as to say that response time is meaningless in all cases – just that it is far less limited in most. Then as counterpoint to dismissing response times altogether, the public conflict at EMSA in Oklahoma City was brought up where at least one politician complained of the number of excluded calls required in order to reach a 90% response time compliance rate. This is only a single instance, but we all understand that it is certainly indicative of how the public measures the value we provide. In the conclusion, Matt Zavadsky, MedStar EMS Associate Director for Operations, offered several good recommendations to improve patient outcomes and public understanding of the EMS system. While I agree with nearly everything he said, I would really only argue with his statement that began, “There is no such thing as an inappropriate request for 9-1-1, (which is a whole other topic) but then he added there is such a thing as an inappropriate response to that request.” I can only assume he was referring to the fact that accidents sometimes happen en route to calls. While these incidents point out failures in judgement somewhere, it is not the “response? itself that is at fault.

Zavadsky also authored another article in that newsletter entitled “Response Time Realities: The Scientific Evidence.? Interestingly, several of the studies he cites actually help to make the case for effectively reducing response times under 4 or 5 minutes in certain cases rather than eliminating the standards in general. Furthermore, the quotes he uses from the 2008 “Gathering of Eagles” consortium position paper entitled “Prehospital Emergency Care? do not discount the time of a response, but instead point out the unsupportability of “over-emphasis on response-time interval metrics? compared to the “unintended, but harmful, consequences (e.g. emergency vehicle crashes) and an undeserved confidence in quality and performance.” While I also cannot justify the 7:59 standard used in many urban areas, I also cannot condone apathy toward responding timely. Maybe I am overly sensitive to the literal meaning of “response time doesn’t matter? when justified with the statement that the “golden hour? is just a myth. For most of us, at least 10-20% of calls include a cardiac, respiratory, stroke or other event where time really is critical and we must be at the top of our game to prevent a death or minimize as much loss in quality of life as possible.

My concern in these arguments is an unstated bias that “response” means only the arrival of an ALS-experienced paramedic traveling with red lights and sirens from a fixed fire station. Technically, “response” must be understood as simply the time between a call for emergency assistance and the initiation of appropriate necessary treatment. For many calls, that care could be BLS-led in most circumstances assuming that the calls are appropriately triaged at dispatch. Emergency Medical Dispatch itself even provides some level of immediate guidance in care with a response time of zero. Additionally, the greater availability of defibrillators as well as more common knowledge of compression-only CPR means that initial emergency life-saving care can be initiated well before any ambulance arrives. The existence of advanced telemedicine devices (such as the LifeBot-5) are also changing the rules by providing advanced medical consultation even more quickly in remote rural areas typically with far longer average ALS arrival times.

My point is not necessarily trying to get medical responsders moving faster, but to redefine response time not just as the metric for the ambulance arrival to justify budgets but as a factor that affects patient outcome. There are many ways to achieve this goal and it begins as education within the system as well as with the public because technology is changing the dynamics. Zavadsky’s points are valid. Making defibrillators more available and teaching the public how to respond when a medical event is witnessed is critical. Also while adding ambulances and staff to more locations would be another way to address reducing response time, it is not financially practical. An effective alternative to achieve that same goal would be to position the responders closer to the call thereby minimizing distance and the associated need for risky driving. Modern “dynamic system status management? practice has proven that response time can be shortened to most calls (at least 80-85%) without the need for excessive driving risk that places crews or the public in danger. Improving performance means responding appropriately in less time – not necessarily just responding “faster.” Technology can be evaluated as being “outcome-based? just the same as patient treatments.

Watch for future posts which will highlight other components of performance-based EMS beyond just measuring and improving response time.

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"No Available EMS"

No one really wants to read a bad story about some other EMS agency, but it is even worse to read a similar story about your own. The purpose of this post is not to make Detroit into some “failed EMS poster child? but intended to shed some light on similar problems that may be all too common at other locations as well. For instance, I am sure that the Detroit EMS is not alone in being accused of having their service underfunded and their resources understaffed. I also know there are many other agencies out there paying penalties for “exceptions? (calls outside the expected response time), but holding a call for an hour before dispatch as claimed by EMS workers in a Detroit Examier story is certainly not a common trait of a High Performance EMS. Actions like this are certainly worthy of examination, however, the thing that has really set Detroit apart right now is the realignment of its fleet effective January 3rd in response to the death of Gordon Mickey shortly before Christmas of 2010.

According to Detroit Free Press reporters in an article published in EMS World just today, the plan is to reallocate eight ALS (Advanced Life Support) ambulances to Basic Life Support (BLS) units by reorganizing the Paramedic/EMT teams. Jerald James, Detroit EMS Chief, said that the model “will help better address non-emergency runs? which can make up about 65% of the roughly 130,000 dispatches each year. But in the same article, a “city paramedic? was said to have expressed concern that the wrong ambulances will end up at the wrong calls identifying dispatch’s difficulty with properly prioritizing EMS response. Interestingly, Detroit had already reorganized its EMS service back in 2004 by adding Echo units (paramedic equipped vehicles without transport capabilities) to its formerly all-ALS ambulance fleet but concern was expressed even back then that “tiering? the system to add Echo units and converting certain ALS units to BLS years ago was not an answer to increasing service.

While the specific case in Detroit may have many unique conditions or particular circumstances leading to their current status, the idea I want to spotlight is the not-so-unique idea of reallocation of staff and resources just to improve the emergency response statistics rather than looking more broadly to improve overall EMS response. As David Konig (The Social Medic) describes the situation in his recent blog, downgrading certain 911 calls from ALS emergencies to BLS status is just “shuffling the deck? to improve response time stats in one category over another. I believe he correctly deduces a major part of the answer by saying that “systems improve service and response through intelligent deployment.?

It is exactly that type of “intelligent deployment? that is the driving motivation behind the Mobile Area Routing and Vehicle Location Information System (MARVLIS) suite of products. Using an agency’s own historical data, MARVLIS forecasts future demand by geographically highlighting the “most likely demand areas” with a confidence of approximately 80%. It is also the only system to dynamically predict vehicle response zones to calulate up-to-the-minute demand and geographic coverage based on vehicle status and location – even when units are moving!  This proven system has reduced response times, held growth in future spending, and improved the clinical outcome while working with EMS staff to improve operating conditions not squeezing productivity.

We would love to hear your comments or experiences on this topic, so please add a comment below and check back often for future discussions.

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