March 8, 2024 · 12:27 pm

Decision Engines to Improve Performance

There is plenty of data in the world, the key to improving performance is to discover the right information and make an appropriate decision, based on facts, in time to change outcomes for the better. Computers are quite useful in aggregating and processing data to distill information in support of the decision-making process, but artificial intelligence and machine-learning has increased our expectations of cyber assistance. Although it is not time to completely hand over critical decision-making to an automaton, there is a lot we can learn from leveraging algorithms to offer appropriate decisions in real-time.

Public safety professionals make many critical decisions every day. Honing an individual’s decision-making prowess through experience can be a slow and sometimes expensive means toward improvement. While building personal skills is valuable, there is no guarantee that the increasingly knowledgeable individual will remain in the particular decision-maker role. There must also be consideration of the decisions made during the early slope of the learning curve. The fact that we can do better should always be an encouragement to seek operational improvement.

A well-established class of software known as “decision support systems” have long sought to advance the quality of the decision-making practice. Since any decision requires the gathering of data and comparison of possible alternatives, these computer applications aggregate loads of information to simplify access for the professional. Some more advanced examples of this software may even integrate models, such as plume analysis or fire behavior, to estimate the impact of potential changes to the input data based on specific actions or conditions. The functional core of all systems in this cohort is that they only present options and leave the actual decision to the discretion of the user.

Shifting the focus from the simple presentation of facts to performing complex tasks without explicit instruction from the data consumer in an evolving category of applications described as “decision engines“. The point of these automated functions is to present preferred solutions to immediate concerns that utilize a larger corporate logic. By integrating the organizational experience of lessons learned with strategies approved by administrators, a higher level of operational consistency of outcomes can be achieved. This follows the same logic as developing protocols to guide medical treatments in the field. Most importantly, these preferred decisions can be available within the demanding real-time operational timeframe.

The term “decision engine” is more than a new marketing phrase to differentiate products, but provides three key distinctions beyond the basic “decision support system” applications. These characteristics include:

  • Standardized decision criteria
  • Real-time actionable recommendations
  • Quantitative functionality for decision review

The decision criteria is codified in some form of a standardized rule base to allow the institutional knowledge gained through experience to be immortalized and managed for accessibility to immediate situations. The way these criteria are stored can vary, but must be available as algorithmic expressions for the data to be automatically manipulated to produce a specific decision option. Within the MARVLIS suite of applications are several decision engines. One engine utilizes stored query sets that categorize units, statuses, and requests-for-service by acuity to be combined with recommendation expressions to form the criteria that automates unit assignment recommendations. It is also important that the decision criteria be flexible to adjust quickly to any changes in the business environment. External factors such as rapid fluctuation in fuel prices or staffing issues can impact the command-level methodology of making specific decisions. The ability to alter criteria quickly provides immediate consistency at the staff level with these changing objectives.

A second defining factor is the real-time support of improved outcomes achieved by providing logical and defensible actions at the time a decision must be made. Another engine within MARVLIS utilizes level-specific, time-of-day posting plans developed from weighted demand analysis automatically combined with move penalties to suggest detailed location reassignments by unit in preparation for future service requests. Not only should decisions be standardized to meet current agency directives, but they must be provided within the moment that a decision is being considered. The recommended decision option, however, should not constrain the decision-maker as their “ceiling of competence“, but establish a “floor of support” between individual decision-makers across shifts and experience levels to minimize rouge decisions based on “ritualized responses” instead of more “appropriate responses” that are situationally-specific. The decision made at any given time should not be solely dependent on who is in the role at the moment.

Finally, there must be a review methodology to report outcome measures and allow for lessons to be learned from discrepancies in the decisions made during unique situations. After all, the purpose of an automation logic is not just about making better decisions, but ultimately developing better decision-makers. A reporting database such as the one maintained by MARVLIS to detail what was known and when is critical to understanding the decisions enacted at the moment. Hindsight may be twenty-twenty, but simply using outcomes can be an unfair bar for measuring appropriate decisions in an evolving situation. It may be better to account for the coverage of recommended demand maintained based on a measured forecast for evaluation of performance, for instance, than simply reviewing specific results at the end of an event. While both must be considered, the weight of an encouragement for consistency should be applied to mitigate any lone exception. Similarly, a less optimal automated decision recommendation can benefit from the experience of an identified exception to improve the rule base to cover such a repeated event in the future.

The decision engine stands as a next-level strategy beyond the traditional decision support system to provide additional consistency and improved efficacy in public safety operations. Due to many of the current challenges in providing emergency services, the time spent developing the decision model along with the decision maker can be a prudent investment.

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December 27, 2023 · 3:01 pm

Optimizing Demand Forecasts

Improvement of your deployment operations requires that you understand where your services will be needed and how to get the available units into the most suitable positions. Then, once you are prepared to respond, it is also critical that only the most appropriate assignments are made for each request to preserve your ability to respond to the next call as well. Traditionally call assignment was a simple “closest unit” consideration with all your resources being equal. That task has now become increasingly complex with a recognition of a growing diversity in call acuity and the increasingly common tiered capabilities of your immediately available resources. This second step of appropriate dispatching toward operational efficiency will be the subject of a future blog post to focus this article solely on demand forecasting.

A common practice for emergency services that have grown beyond a single central depot has been a simple distribution of their resources geographically in the hopes of being able to serve anyone at any time. Without an abundance of crews, this is not typically a successful strategy since neither population nor risk are ever uniform. To make matters even worse, most agencies are experiencing an increase in their volume of demand while also facing some of the most serious challenges in decades to simply maintain staffing levels. Emergency medical services across the country are reporting employee turn-over rates of around a quarter of their staff annually. This trend suggests that there are not only fewer providers per call but less experience on each transport as well. Dispatch centers across the nation also face challenges with an average of 20% of their staff positions routinely left unfilled during the past few years. These difficulties underscore the importance of making good decisions quickly.

Figure 1: Disproportionate access is difficult to resolve with fixed stations.
Figure 1

Disproportionate access to services is difficult to resolve with fixed stations and often results in increased service available outside of the intended district. To adequately populate this geographic coverage model requires an excessive workforce.

So, are you accurately forecasting demand to help improve your operations?

Is your current demand forecasting process recognizing trends throughout the day and week to allow for effective decision making in response to any predicted demand patterns? Without some certainty in your predictive capabilities, it is impossible to effectively trust the recommendations of any decision automation. A potential lack of credible information makes the choices of unit movement more difficult at the same time they are becoming even more critical to the agency. And a lack of credibility also encourages the freelancing of decisions outside of the control of your administration.

Seasonal variation

When reviewing your own annual call history, you should notice the seasonal variation that distinguishes not only the volume of calls within or between school calendars, but the very nature of the calls themselves tend to follow a pattern. During the summer months, personal schedules tend to be increasingly variable with more adventurous outside activities repeatedly lead to more traumatic events. Once school is in session, most families have less-flexible schedules and the shorter, cooler days often make individuals more vulnerable to acute medical conditions.

Even shorter temporal variation

On a shorter scale of time, differences are also recognizable by day of the week or even hour of the day. Higher call volumes typically occur toward the start and end of the traditional work week. The early morning hours of these business days also exhibit patterns found with early waking habits and the increased vehicle traffic and population movement. The pattern repeats itself later in the afternoon, but the locations of people are quite different than in the morning. The unique business hours and personal behaviors on the weekend also make these days unique from the rest of the week. An unequal distribution of people throughout space and time leaves discernable patterns in the location of requests as well.

Figure 2

Sample data demonstrating ALS (green) and BLS (blue) call volume comparisons by hour-of-the-day and day-of-the-week. Notice the similarity in daily patterns although total volume (represented by 90th percentile) is unique.

To create a useful model that honors all these variations, the operational period to be described in a forecast must generally be shortened while simultaneously extending the pool of similar examples to achieve the required statistical precision. The more similar the forecast of demand is to the current moment in time, the more useful it will be in guiding effective decisions. If the intention is to describe demand during the next hour or two, the historic records queried should reflect a comparable timeframe.

Fortunately, your call history is proven to contain many useful clues about the future. It is not merely a matter of extrapolating a progression of time or an assumption that the same request will come from the same caller again. The real-world is complex, yet we all tend to live, work, and associate with individuals that are more like us than the overall population. As a result, each previous request is an indicator of the types of requests likely from our unique population cohorts. The successful technique is in the allocation of the right populations within the right timeframes to sufficiently forecast the future demand. This is accomplished through the way incident records are selected in a dynamic query to represent a time-based forecast and even more importantly, how those results will be spatially aggregated.

Through Demand Monitor, MARVLIS users can not only update forecast parameters based on their local knowledge, but they can monitor both the accuracy and precision of each dynamic forecast. Using a default configuration, most services should find that approximately 80% of the actual calls received are in an identified hotspot recognized by a current demand forecast. With some effort, that average can often be raised to over 90% of future requests are correctly forecast by the hotspot zones. Simply raising accuracy, however, could be easily accomplished if precision is not considered. By including the whole jurisdiction within a hotspot, an accuracy of 100% would be the result. While technically valid, this type of forecast would provide absolutely no assistance in pre-positioning responders to improve outcomes. The forecast area must be maintained as small as possible while increasing the predictive capabilities of a demand query. Currently, this is recognized by comparing incoming requests over time to the effective forecast when each call was received.

Demand Monitor allows analysts to define multiple query strategies for simultaneous execution and evaluation. If each of these queries is validated against reality, the distinct forecasts can be quantitatively compared and improved over time. The result is a continuous quality improvement that requires some regular review to maintain.

Best practices in Demand Monitor

A recommended best practice for modeling demand is reviewing and modifying the demand queries at least twice a year to coincide roughly with the school calendar. It is not necessary to be precise in modelling academic dates, it is the mindset of schedule regularity that is driving the demand pattern. Jack Stout, the father of the System Status Management concept, suggested using a floating 20-week period based on the size of the spreadsheets he used, but this often crosses the known seasonal variations discussed earlier. To minimize the impact of influence from outside the current season, the number of weeks can be shortened. Reviewing only 5 weeks before and after the current forecast date cuts that total number of weeks in half. It is possible to maintain the number of records of the longer period by including the same weeks from a previous year to mitigate the reduction of number of samples while maintaining seasonality. However, the addition of too many years may have a detrimental affect by increasing the influence of older neighborhoods since newer subdivisions would have less representation across the years. Experience suggests looking back no further than 2 previous years in most circumstances. For most agencies, that keeps the records reviewed within the post-pandemic experience as well.

Another successful strategy to control for the temporal pattern can be to query a fixed seasonal timeframe rather than a floating period. If you want to model the school year, setting fixed dates of mid to late August through mid-May will clearly eliminate the effect of any summer dates. A downside to this method would be the necessity of changing the query period once school begins or ends for the year. Automating the model of both strategies simultaneously can allow for each query option to be graded separately to discover the best alternative for your jurisdiction.

It is difficult to argue against modeling each day of the week individually, but when it comes to the finer segmentations of the day, there is legitimate debate. Again, Jack Stout recommended modeling each hour of each day for a total of 168 unique timeframes of the week. Part of his justification is the average busy time of a unit being about an hour and to simplify the calculation of a Unit Hour Utilization (UHU). Demand Monitor is typically automated to execute every 5 to 10 minutes to minimize the amount of change between each forecast while allowing the predictions to subtly adjust more frequently. It is also common for ambulances to be busy longer than an hour in our post-pandemic world.

Once a query definition is set, it can be tested in Demand Monitor to see how many records it will return. Ideally, the number of records for any sample query should be measured in the hundreds, but less than a thousand. If you need to adjust your parameters, altering the number of years will have the greatest impact followed by the number of weeks and finally the number of minutes which will have the smallest influence.

The experts at BCS have decades of experience bringing real-time analytics to the real-world. If you require any assistance in customizing your Demand Monitor queries, please contact your support representative.

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June 12, 2023 · 2:30 pm

Passing ‘Fast’ for ‘Appropriate’ Responses

During the height of the COVID pandemic, shortages led to many operational challenges that required creative solutions. One of the more challenging issues that has become as endemic as the disease itself is the recruitment and retention of EMS professionals. This shortage has disproportionally impacted paramedics, as evidenced in the NAEMT survey results published in May of 2022. The ripple effect of the workforce reductions that has changed the certification balance favoring basic credentials has led to some logistical changes in priorities. The most recent NAEMT survey results, published last month, show services are taking longer to respond to requests, considering alternatives to serve low acuity calls, and changing the provider mix that services patients.

As more agencies move from exclusive ALS capabilities to tiered responses, there must also be a growing concern with ensuring the most appropriate resources are responding to each call. The idea of thoughtful intentionality in the assignment of units helps to improve the chances that the right resource will be available to the next future request for service. I like to describe the logical shift in thinking as moving from “the right response times on every call” to “the right call for responses every time.” This may be a subtle but highly significant change in attitude regarding the best, or most “appropriate,” response assignments to each request rather than routinely sending the closest unit. Managing these resources well may additionally involve adjustments to the expectations of your community.


Depending on the priority of a call, it may be that the closest resource is logically passed over for a more appropriately matched capability responding to that call from a greater distance. While the 90th percentile response times may increase for certain lower acuity calls, this selective assignment process allows advanced capabilities to be preserved for potentially higher acuity needs. But it is seldom really as simple as it sounds. How much further can that preferred response be before its preference is overtaken by the need to simply respond promptly?

The reality of these critical decisions means the process becomes far more complex and dangerously slower. The more conditions that must be understood and compared extend the time for each dispatch without automated assistance. By planning and codifying dynamic selection criteria, the extra delay can be eliminated which means making far better decisions in no more time than traditional fastest responses. These guided decisions can also be made uniform across positions and shifts to achieve corporate objectives that prioritize clinical outcomes based on acuity in addition to broad operational objectives that consider the condition of crews.

A typical Charlie priority call, for instance, might prefer to have a paramedic respond timely. That ideal response might be within, say, 15 minutes. With expected delays beyond that time limit, it may be acceptable to dispatch a BLS unit to begin care while still allowing the ALS resource to join the response from a longer distance. However, the practicality of that rigid rule may send a basic unit on a 14-minute response when the nearest advanced unit is only 16 minutes away. That implementation of a simple preference for immediate care has practical limitations because it committed two units with little time for the first to even complete an assessment before requiring a hand-off of care. Is the additional drop in service level worth the brief time savings in this example?

Response rules should be focused on improving both the speed and quality of outcome without artificially taxing the system. In this case, the lower-level capability may only be desired if it will be more than at least 3 minutes faster than the closest ALS unit regardless of its distance to travel. Without more time for basic intervention on scene, the multiple assignments are only tying up more response units without actually improving care.

A Delta priority request may also need a speedy paramedic response and a basic unit alone for too long may not be an adequate alternative. However, matching a paramedic QRV, or another supervisor, with that nearest BLS resource provided that it can be completed in 5 minutes less time than the closest ALS ambulance could be an acceptable solution.

While the time-sensitive examples above show better potential for care, there are also system benefits with appropriate responses on the lower acuity side of the scale. A Bravo request could be most efficiently served in a BLS capacity with longer response times before it would be deemed late. Yet if the preferred units are just not available by the time a limit approaches, an ALS resource could be dispatched to keep your response statistics within acceptable limits.

The impact of posting schemes on response capabilities cannot be overstated. There simply is no substitute to having the right resources located closer to their next most likely call. But the post priority can also be useful in assigning appropriate responses at the low-end of the acuity scale. A hospital discharge, or typical interfacility transfer, will not benefit from sending a fastest unit. The posts nearer the hospitals tend, in general, to be busier locations. Using one of those units not only increases the chances of keeping a crew within the vortex of handling patients, but it also exposes a potential lapse in coverage until another unit can be moved from a less active post. If the assignment is given initially to the unit filling the least critical post, there is no immediate coverage loss and no additional post moves required. Assigning an appropriate unit for this call can actually reduce the effective activity, or UHU, of other resources.

Appropriate dispatch is becoming a necessity to balance workloads and provide the best care possible to our patients given current trends. It comes, however, at a cost to the complexity of decisions demanded of telecommunicators unless they are given tools to help manage the art and science of dispatching. But to be effective, we must use appropriate automation tools for the best results.

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May 11, 2023 · 9:45 am

SSM Strategies for Success

I am pleased to announce the availability of my latest paperback book. As a consultant working with agencies across the nation that are representative of many distinct models of service, as well as being an EMS provider and a chief fire officer myself, I feel highly invested in the process to improve strategic dispatch and deployment decisions. It is my unique collective experience gained over more than 13 years of consultation and experimentation that is shared through these pages. Even in these years spanning the pandemic crisis followed by an ongoing paramedic shortage, I have witnessed multiple significant changes of focus within the industry providing emergency medical care.

System Status Management Strategies for Success with MARVLIS

Still, the prime objective “to provide each critical patient the best possible chance of survival without disability or medical complication, given the current state-of-the-art of prehospital care technology,” professed so eloquently by Jack Stout in his articles published by JEMS in the 1980’s, remains a guiding light. However, the details necessitate some update to truly achieve this directive while juggling a growing list of constraints and corporate objectives in an environment of highly advanced computing capabilities.

The foundational concept that drove the development of this book is that any agency who responds to emergency requests from the public needs to thoughtfully consider where their resources are located when they are not actively engaged on a call. It is not just about response times for outcomes, safety, or even efficiency that drives operations. The consistent idea across the many diverse strategies discussed in this book is that the place these units wait to respond clearly communicates the values that a service prioritizes. This work challenges the traditional thoughts of simply automating the tasks of deployment and dispatch by looking at ways to leverage them instead as tactical opportunities specifically to coordinate successful organizational strategies beyond just immediate responses.

As a result of the increasing constraints on high-demand emergency resources, our crews must be used wisely and with special attention to their distribution over both space and time. And, further, we must acknowledge that each deployment or dispatch decision, whether we follow a formal plan or not, has an impact on the crews and the patient as well as any performance metrics of the agency. These three constraints are inextricably intertwined and often mutually contradictory.

I am particularly proud to have Jack’s son, Todd Stout, the Founder and President of FirstWatch, say this about my efforts, “This book is a thoughtful and detailed examination of my father’s work and shows ways it can be applied and updated for modern concerns and technology. Dale has done a masterful job of combining EMS history, SSM theory, practical information and explanations of real-world implementations using the MARVLIS system. Definitely worth a read!“

As much of the implementation of these ideas involves the telecommunicators within the communications center, I also received valuable advice from Jerry Overton, who serves as President of the IAED who also addressed my work saying, “With limited resources, it is appropriate to reflect on the way we deploy and dispatch ambulances. In these pages you will find valuable discussions to help improve patient care as well as the efficiency of your operations. In my own experience, MARVLIS just works.“

I hope that the time and effort I poured into these pages causes each reader to think deeper and reflect upon the conscious or unconscious choices being made each minute that impact our patients and our providers. The fact that we can do better ought to drive us to make improvements, not for the mere sake of change, but to improve whatever goals are important in the moment to provide better outcomes regardless of how they are measuered.

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September 13, 2022 · 6:33 am

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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May 19, 2022 · 9:29 am

How is Your EMSWeek?

Elsewhere on social media this week I have seen a call to “protest EMS Week 2023”. The logic suggests that the free meals and cheap trinkets are far less than the long-suffering and under-paid providers deserve. While I whole-heartedly agree, “we” have made EMS Week what it has become, not what it was intended.

The EMS Week 2022 proclamation reads “I call upon public officials, doctors, nurses, paramedics, EMS providers, and all the people of the United States to observe this week with appropriate programs, ceremonies, and activities to honor our brave EMS workers and to pay tribute to the EMS providers who lost their lives in the line of duty.” I read nothing in there about free drink coosies and pizza. While there is nothing wrong with a “company BBQ” to get together outside the ambulance, we are wasting the opportunity we were given as a national spotlight. It feels good to bask in that glow for the moment, but we could be grabbing the microphone while we’re there.

EMS Week was designed with a daily focus on service, not just for accolades to passive providers, but as a chance at the microphone to tell our individual stories. Unlike our brothers and sisters in law enforcement and fire protection, we are generally not considered “essential” because the public has not yet seen our value. We can’t point the finger at them and say they need to wake up and look harder, it is up to us to proclaim and demonstrate that value. That doesn’t happen when our mouths are full of free food, only when we provide the free “food” of empowerment to others. So, why aren’t we teaching free classes that build community awareness?

Face it, we suck at coordinated political action. We can’t agree on educational requirements, collective bargaining, titles, or even whether you can provide good care from a red truck. Most of us still honor personal anecdote over research and blame volunteers or federal reimbursement for low pay. We all have different ideas to solve the mess. However, we do share a common power and it is in the personal interactions that we are best at demonstrating our medical knowledge and concern for the welfare of individuals. The same individuals that we are asking to fund us with their taxes.

We could be using this week in service above and beyond instead of using it as a chance to rest. Empowering the public is the best way to gain trust. Show what we know and ask for help in improving outcomes. We need their help to keep a cardiac arrest patient viable until we can arrive and resuscitate them. Make them a part of the team, not outsiders. Learn to install a car seat and help new parents prevent an accident. Build the relationships that use our strength when people are in distress and they will see us as a necessary component of the community, not an add-on.

Many of the pieces of the puzzle to fix EMS are beyond our immediate personal control, but if we actively make the community a partner, they will support us and even demand change on our behalf. Don’t protest the monster we created, tame it for our better purposes.

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March 1, 2022 · 7:47 am

Advice From an FTO

As we begin to wind down on the pandemic-level of constant 911 calls and the endless hours waiting on a room in the ED, we find ourselves in a time to reflect a little before our next call. Like so many services, we have a new influx of eager young professionals. Recently, a new student asked me, “how do you guys keep doing this day after day?” Not an unusual thing to ask lately, and my reply was this:

“determination and our perseverance to make sure our patient gets the definitive care and treatment they need.”

COVID-19 has really stretched us thin, not just with staffing, but with supplies, training, willpower, and people who actually WANT to learn. Training new hires and students can be tedious and frustrating if you don’t have the opportunity to learn and adapt along with them in addition to teaching them what they need to know. You must become extremely patient and place yourself into their boots. If you don’t empathize, you risk placing yourself in the position of doing harm. Not only to your student, but to the patient, and most likely our profession as well. We need to be resilient and steadfast, showing them the ropes and thoughtfully placing them into the patient care position supported with good proctoring and mentorship.

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January 20, 2022 · 1:30 pm

Minority Report or Moneyball

I have often heard comparisons on the automation of System Status Management to the 2002 Spielberg movie starring Tom Cruise called “Minority Report” loosely based on the 1956 short story by Philip K. Dick. This science fiction action thriller is set in the year 2054 when police utilize a psychic technology to arrest and convict murderers before they commit their crime. The obvious comparison there is to the forecast of future call demand and the eerie accuracy of the reports that allow the right resources to get there in time to make a difference in the outcome. Sometimes in the movie, as in real life, there is a considerable cost to achieve that goal as well. It is easy to get wrapped up in the technology, particularly the virtual reality user interface that Detective Anderton (Cruise) uses to make sense of the premonitions and quickly locate the scene. I like to end the analogy there before we learn the darker side of the way the technology works and can even be manipulated to put a stop to the whole project. Perhaps some EMS providers think they see a similar inherent darkness and hope for an eventual collapse of the whole dynamic deployment paradigm as well. This may be where the art of a story and our reality diverge, especially considering the current economic dynamics even given the admittedly sporadic successes. This may also be why we need a different analogy.

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March 18, 2021 · 7:25 pm

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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January 26, 2021 · 8:31 pm

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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