Ambulance location for maximum survival

Abstract

This paper proposes new location models for emergency medical service stations. The models are generated by incorporating a survival function into existing covering models. A survival function is a monotonically decreasing function of the response time of an EMS vehicle to a patient that returns the probability of survival for the patient. The survival function allows for the calculation of tangible outcome measures-the expected number of survivors in case of cardiac arrests. The survival-maximizing location models are better suited for EMS location than the covering models which do not adequately differentiate between consequences of different response times. We demonstrate empirically the superiority of the survival-maximizing models using data from the Edmonton EMS system.

Hypercube Queuing Models in Emergency Service Systems: A State-of-the-Art Review

“…Erkut et al [55] extended ten existing covering models for emergency systems by taking the survival function, which maximizes the expected number of patients who survived cardiac arrest. They used the Jarvis' algorithm to evaluate this function.…”

Integrated decision making in healthcare : an operations research and management science perspective

“…The decision where to locate ambulances is determined such that a specified region can be reached within a target response time by one or more ambulances, or that the average or maximum response time to a potential emergency is minimized [45,79,158,165,276]. The response time of an ambulance concerns the time elapsed from notification of an emergency until an ambulance arrives at the emergency location [180].…”

“…Other factors to take into account in planning the locations of ambulances are the likelihood of timing and location of an emergency, staff availability, location constraints (e.g., a place where staff can rest), and the emergency care center where patients are potentially transported to [36,62,79,158,180,205,267,276,399,412,526]. Methods: computer simulation [62,165,180,186,204,237,267,412,429,459,526], heuristics [22,36,38,164,197,266], Markov processes [21,257], mathematical programming [18,36,38,39,40,45,79,118,158,165,186,205,207,237,257,276,412,…”

“…Ambulances exist in different transport modalities (helicopters, vans, cars) carrying different types of equipment and staff [38,39,40,165,186,267,412,429,446,468,526]. Ambulances collect emergency patients, but also perform less urgent transfers of patients between care facilities [468].…”

“…For example, staff members may be 'on call' while working elsewhere or being off-duty, and they are called upon when additional staff is required in the ED to cope with unexpected demand peaks [376]. Methods: computer simulation [16,40,60,91,165,181,186,267,311,315,375,412,429,526,532], heuristics [38], mathematical programming [38,39,165,375,376,412], queueing theory [106,220,446,468], literature review [62,282,393].…”