Tag Archives: dynamic SSM

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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The Future of Prediction

I have read the positions stating that calls for emergency services are completely random (justifying the reason they are often called “accidents”) and therefore not able to be predicted.  But both academic literature and practical experience show that demand prediction can be an effective tool in helping to balance scarce resources (ambulances and their trained crews) with public demand (requests for emergency responses even without taking into account the abuses to the system as discussed in a previous posting on the problem of “frequent flyers”) while still improving response times and controlling costs.

For anyone who thinks all of this sounds too good to be true, there are examples of where expensive technology is not having the desired affect.  One such location is Lee County EMS in Florida where not only have response times not been improved, but ambulances are burning more fuel than ever and the critics include the very paramedics it is supposed to help.  While predicting where the next 911 call will come from may be similiar to “picking the winning card at a casino” as the Florida investigative news reporter suggests, that isn’t really the objective.  We don’t need to know which phone will make the next call, it is enough just knowing the probability of a call coming from any given location within the service area.  This may be a subtle distinction, but one that makes a huge difference at MedStar in Fort Worth or Life EMS in Grand Rapids where response times were dramatically improved by taking the next step beyond simple demand prediction and placing ambulances at positions where they can be the most effective.

Academic studies show that demand pattern analysis can be used without hourly, daily, or seasonal calibration to achieve potentially acceptable tolerances of demand prediction, but when adjusted with these appropriate corrections, software applications like MARVLIS (the Mobile Area Routing and Vehicle Location Information System) can effectively predict demand in practical situations.  According to Tony Bradshaw of BCS, the makers of MARVLIS, it routinely calculates where about 80% of demand will occur and when paired with realistic drive-time response zones it demonstrates valuable support for a dynamic System Status Management plan to pre-position, or “post” ambulances closer to their next call saving valuable time and increasingly expensive fuel costs.

What matters most, though, is what agencies experience in the field.  At SunStar they say ” the most significant result was improving our emergency response time from 90.2% to now over 93% in lieu of an increase in patient call volumes.  This equates to ambulances arriving on scene more than 1 minute quicker.  We additionally saw a savings of $400,000 in penalties by exceeding our contractual goal of 92% and performing above 93% compliance.”  Similarly, Steven Cotter, Director of Sedgewick EMS added that “the technology has opened our eyes to be able to understand how we are performing, where we are deficient in our performance and how we can make changes quickly and adapt to a changing environment.”  And beyond simple response times, “it’s what technology should do,” says Joe Penner, Executive Director at the Mecklenburg EMS Agency, ” take the complex and present useful, straightforward information.  It has helped us improve response times, resource utilization AND simultaneously reduce unnecessary post moves — your patients and employees will appreciate it!”

My conclusion is that proper demand prediction paired with realistic response creates significant opportunity to improve performance and cut costs even in growing communities.  When used properly, the future looks bright for High Performance EMS!

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