Harnessing Technology to Improve EMS Deployment and Operations

 Some 30 years ago, Raj Nagaraj, PhD, was one of the early pioneers in using data, analytics and modeling to improve EMS operations and deployment practices in Washington, D.C. Nagaraj has continued to find new ways to improve fire and EMS operations through technology. Nagaraj is co-founder and Chief Technology Officer of Deccan International, which serves some of the nation’s largest fire and EMS services. In the following interview, Nagaraj shares his insights on where EMS operations research technology has been–and where it’s going.

Q: You were among the first to use data, analytics and modeling to improve EMS operations in Washington D.C. some 30 years ago. How has EMS evolved in the use of analytics since then?

A: There have been remarkable improvements since I first became involved in EMS at the District of Columbia. At that time, Jack Stout was introducing the EMS world to a variety of performance measures, including unit hour utilization and concepts like system status management. Since then, analytics and modeling in EMS has improved by leaps and bounds in a variety of directions. First, GIS, critical for visual insights and routing time estimations, has become common place–so much so that EMS is now highly demanding of GIS. Second, optimization algorithms and metaheuristics have advanced to a point that optimal post locations across all possible street segments can be identified in a matter of minutes. Third, using powerful forecasting techniques to project future call volumes has become quite common. Finally, real time tracking and alerts of key indicators are used routinely so that EMS managers are much better positioned to keep a pulse on operations.

Q: Looking forward, how do you see EMS using technology to improve deployment and operations?

A: Modeling tools for EMS are now used for improving operations in a number of ways–both for planning as well as for real-time management. For example, projecting the potential impact on EMS response times as a result of adding hospitals or closing them, or even just closing some of their critical care capabilities. As one would expect, EMS units would now have shorter or longer response times to hospitals, as well as potentially incur longer wait times at the further hospitals that now must take in more transport workloads. We now have models that project the impact on EMS response times because of additional hospitals or closings. In addition, with hospital closings, the model can also calculate the number of extra resources needed and when and where to make up for the worsening response time.

Another example is in managing crew breaks in a manner to ensure that crews get their vital breaks, while simultaneously minimizing coverage impact. Models can track crew fatigue as a result of a variety of factors, including: types of calls they were on and hours since their last break; and tracking and projecting real-time coverage holes based on current unit locations and near future projected call volumes. This information can be combined to recommend when specific crews could take their breaks with minimal impact on coverage.

Q: Community Risk Reduction (CRR) is getting a lot of attention among progressive fire and EMS services. What’s the role of data and analytics in this area?

A: CRR is indeed a wave of the future, considering the benefits of prevention activities both from the cost and patient health perspectives. With a wealth of consumer data along with demographic data, it’s now possible to connect the dots between community profiles and risky behaviors and use this information to develop and implement targeted prevention programs.  For example, at one Fire/EMS service, we discovered a significant correlation between the “wired and connected” demographic and kitchen fires, the narrative being that this group of mostly young people are setting off on independent living for the first time and getting distracted on smart phones while using the kitchen. In such cases, developing a targeted program for preventing kitchen fires in such households becomes a relatively straight forward task.

Q: How are fire and EMS leaders using data and analytics to improve transparency with government officials and other stakeholders?

A: Expectations of transparency with government officials, the public and labor is becoming the norm. At one Fire/EMS service we work with, leadership can pull up in an instant the entire history of a crew’s activities over any given period. As a result, giving commendations and recommendations for improvement are addressed through data rather than being personality driven. It’s important to be able to pull data from a variety of sources, use analytics to connect the dots and identity outliers to help identify improvement opportunities.

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