From Data to Decisions in Ambulance Redeployment
by Shane G Henderson
Abstract: Joint work with Professors David Matteson, Huseyin Topaloglu, Dawn Woodard, and PhD students Matt Maxwell, Matt McLean, Matt Schneider, Brad Westgate
Emergency medical service (EMS) providers attempt to provide quick ambulance response to calls for medical attention, along with transport to hospital if necessary. Budgets are invariably tight, so there is great pressure to make the most of EMS resources. Many EMS providers now use some form of ambulance redeployment, which is also known as system-status management or move-up. Redeployment involves moving vehicles in real time in response to real-time system information, in an attempt to better match available ambulances to future calls. Many existing redeployment implementations are designed in somewhat ad-hoc ways, leading to limited performance gains and crew frustration at what are perceived as pointless moves.
I will describe our efforts in using approximate dynamic programming (ADP) to make these redeployment decisions more carefully. I'll describe how ADP works in our context, why traditional methods for tuning ADP coefficients are not completely satisfactory in our context and perhaps more broadly, our revised tuning methods, and numerical results for a couple of major centres. I'll also briefly explain the statistical methods we use to obtain parameters for our models from Computer-Aided Dispatch databases, emphasizing how we can obtain travel times on road networks from Global Positioning System (GPS) breadcrumb data through a Bayesian formulation. If time allows, I'll also describe our efforts to obtain an upper bound on achievable performance improvements using redeployment, which is important in knowing when we researchers can stop looking for improvements.
For More Information: contact: Peter G Taylor. email: firstname.lastname@example.org