There are a broad range of motivations for moving beyond what you are currently doing (or not doing) in order to understand how your agency is currently performing and how your agency might perform in the months and years ahead. Funding levels continue to be constrained and don’t grow as quickly as the demand for services, communities often expect increasing capabilities and performance without increasing investment, external influences change the operational environment and the accuracy and comprehensiveness of your ability to answer complex questions is continuously expected to improve.
These challenges aren’t going to decrease in the future, and you must therefore be able to analyze, optimize and model the various operational aspects of your organization as these are becoming critical capabilities for the modern emergency services leader. The technologies and capabilities exist, but it is prudent to understand a bit more about your organization before you determine the right investment to make.
Here are five questions that will help you decide what solutions and capabilities will provide the best value for your tactical and strategic operational planning investment.
- How Good Is Your Data?
One of the pitfalls involves your data. Your data is probably of adequate quality for your typical daily operational needs, but employing toolsets such as modelling tools based upon discrete event simulation (the most capable and accurate solutions) imposes harsher requirements and will expose any data quality deficiencies. If you have never truly looked at your data in this context before, be careful not to assume that your data is as accurate and sound as you would like it to be.
- How Well Do You Understand Your Operation?
Your operational rules can pose the second key challenge or trap. A mathematical model prefers clear and well-defined rules. Even if you do have well-prescribed rules, they may not be followed, the actual process may change day by day or person by person and they may not be implemented correctly or as expected. Related is the concept of soft data and rules, which is the idea that the full set of information resides within your key people and is either not captured anywhere or is not necessarily known by anyone.
- Do You Have The Talent You Need?
Thirdly, it is not trivial to formulate, construct and solve the mathematical model. Either a toolset specialist is needed to do some bespoke work using generic tools or a more specific application (such as Optima Predict) must be available to do this work, tailored to your needs. The latter is rare, particularly if the application needs to have sufficient usability to be operated by a non-specialist. Related is understanding the trade-off between model complexity and capability, both of which you would want aligned to your needs.
- Are You Comfortable Defining Your Problem?
A fourth challenge is defining an objective and interpreting the solution results into something that can be applied to the real world. In terms of your objective, there are decisions to be made around what business question you want an answer to and how to measure the quality of that answer—for example, by minimizing some approximation of real dollar costs, maximizing some performance aspect or a combination of both. Further, it is important to remember that you are not operating in isolation and there are various things outside your control such as hospital capacity and the weather. In terms of interpreting the solution, an additional challenge here is that you will not necessarily get a clear black and white answer. There are probabilistic and variability aspects impacting most of your business questions. In short: predicting the future is never easy.
- Are You Ready To Change?
Lastly, the fifth potential challenge is managing the reality that change is often difficult within any organization. Analysis, optimization and modelling are excellent tools to help an organization improve, but if there is no will to implement the new approaches or methods discovered during the exploration, it might as well have not taken place. Agencies often assume that implementing advanced planning tools is purely the domain of the data analysis team, but it is critical that this strategy have the endorsement and commitment of the highest levels within the organization so that the value of your investment is realized and a culture based upon data is given the needed foundation within the leadership team.
So What Can You Do?
The ultimate goal is to use a technology toolset to improve the performance (in whatever manner you define it) of your EMS operation based on specific criteria and objectives. Depending on exactly what your criteria are, you might just get lucky or discover that your specific business question falls into a well-proven and understood subcategory with straightforward requirements. If that is the case, use of these toolsets may not be as hard as described above. If not, however, there are measures you can take to mitigate the challenges and traps.
First, it is important to acknowledge and recognise the real world challenges and therefore the wider effort and investment required to get a true and full understanding of your operation’s data and rules. This may involve questioning or at least reviewing the status quo and looking at data and rules from multiple and possibly new perspectives (including using modern data approaches).
Second, within the context of your operation and plans, it is critical to use the appropriate toolset and resources. Ideally, the toolset should include an application that also provides input mechanisms, configuration, visualisation and analysis. There can be advantages if this application is also specific to EMS operations for in-house use. The resources should know how to operate and interpret the toolset, whether this comes through a strong and skilled in-house planning/operations culture or bringing in skilled and appropriate resources as required. Regardless, the key is to know and understand both the EMS operation and toolset challenges.
Finally, to really have a means of overcoming challenges, consider experimenting with your toolset models and know what, when and how to apply solution results to the real world. This may require evaluating a range of repeated scenarios, identifying trade-offs and sensitivity analysis, collecting justification and evidence from multiple models or perspectives and using the tool to show the cost of things outside your control. No matter what is required, it is important to involve and convince key people throughout the process and consider change management for major operational changes.
For those actions you do implement in the real world, measure and feed that data back into the toolset models to continually increase accuracy and confidence.