Flashpoint: September 2013
In my August column, I reviewed a consultant’s report on the potential combination of Toronto’s fire and EMS services, which had just been publicly released.
September 9, 2013
By Peter Sells
In my August column, I reviewed a consultant’s report on the potential combination of Toronto’s fire and EMS services, which had just been publicly released. The study recommended that Toronto Fire Services “implement dynamic staging and use predictive modeling and pre-emptive traffic controls to better predict demand and more effectively deploy fire resources and apparatus.”
That recommendation calls for two distinct and unrelated strategies: deployment based on prediction of demand and pre-emptive traffic controls. Pre-emptive traffic controls is a complex topic on its own, so let’s leave that for a future discussion.
The study states that “Toronto’s current fire resources and apparatus could be used more effectively through the use of dynamic staging and an enhanced technology to better predict demand and deploy resources.” My problem with this statement is that it has put the cart before the horse. One cannot conclude that dynamic staging, or any other deployment strategy, would be effective until a predictive modelling analysis has been done. The recommendation appears to suggest a solution to a problem that has not been specifically identified, and certainly has not been quantified.
Let’s understand what is being suggested here. Predictive modelling involves the analysis of past trends and current conditions to determine the most probable distribution of service demands in the future. I first became aware of such modelling as a manager trainee at McDonald’s, as I languished on the wait list to be hired by Toronto Fire. We had binders of data with sales numbers for this day last week, this day last month and this day last year. These were used to predict sales totals in 15-minute increments for today. Staffing of the front counter, drive-through and kitchen would then be scheduled according to expected sales volumes throughout the day. It worked pretty well, for a paper-driven 1980s system. I can only imagine the tools McDonald’s has now.
In a fire-service context, predictive modelling is used to anticipate call volumes by type and geographic area within defined time periods. Models cannot predict individual fire calls by address or vehicle collisions by intersection, just probabilities of events within perhaps a three-hour window in a specific first-response district. The idea is then to deploy personnel and apparatuses to the best advantage based on those probabilities. Hang onto that thought for a minute.
Dynamic staging involves the placement of emergency response units to pre-defined locations to best respond to event trends predicted by modelling. Staging locations can be existing emergency service stations, other civic properties or even marked locations on the side of a city street. The fire people reading this are getting ready to hang and ignite my effigy, but please hear me out. Dynamic staging has been in use by EMS organizations for a long time, and it makes sense – in an EMS context. Ambulances, especially advanced-care paramedic units, can have average response times in excess of eight, 10 or 12 minutes even within major cities. Their home bases are spread much thinner than fire halls. Dynamic staging of fire resources as suggested (but not analyzed or justified in any way) by the Toronto study amounts to applying an EMS paradigm to a fire-protection infrastructure. Parking a fully staffed quint on a street corner, barking out diesel particulate for hours is a complete waste of time. No training or maintenance is taking place and the tactical advantages are likely minimal. There are better ways to use predictive modelling to optimize fire-response resources.
The Toronto study addresses this point: Although the 24-hour shift model ensures maximal fire response capacity at all times of the day, it does not facilitate peak hour staffing changes, with reductions in planned staffing over the predictable slow time periods of the day.
Step 1: Determine your minimum safe level of staffing and ensure that level is met at all times. Step 2: Use predictive modelling to anticipate peak demands by time and space. Step 3: Schedule additional staffing to meet those demands, in whatever time increments are appropriate.
Does this sound radical? It certainly runs contrary to normal operations for most of us, but in the United Kingdom, where budgets have been slashed drastically in recent years, these strategies are in place.
Two things are clear: we can’t continue to do business as usual and expect to see any service improvements or cost efficiencies without putting all issues on the table; and, any future changes must be analyzed and implemented from a fire-protection perspective putting public and firefighter safety above all other considerations.
OK, light me up.
Retired District Chief Peter Sells writes, speaks and consults on fire service management and professional development across North America and internationally. He holds a B.Sc. from the University of Toronto and an MBA from the University of Windsor. He sits on the advisory council of the Institution of Fire Engineers, Canada branch. Peter is president of NivoNuvo Consulting, Inc, specializing in fire-service management. Contact him at email@example.com and follow him on Twitter at @NivoNuvo
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