Efficiency and fairness in ambulance planning

Mei, R.D. van der [Promotor]Bhulai, S. [Promotor

Approximate Dynamic Programming: Health Care Applications

This dissertation considers different approximate solutions to Markov decision problems formulated within the dynamic programming framework in two health care applications. Dynamic formulations are appropriate for problems which require optimization over time and a variety of settings for different scenarios and policies. This is similar to the situation in a lot of health care applications for which because of the curses of dimensionality, exact solutions do not always exist. Thus, approximate analysis to find near optimal solutions are motivated. To check the quality of approximation, additional evidence such as boundaries, consistency analysis, or asymptotic behavior evaluation are required. Emergency vehicle management and dose-finding clinical trials are the two heath care applications considered here in order to investigate dynamic formulations, approximate solutions, and solution quality assessments. The dynamic programming formulation for real-time ambulance dispatching and relocation policies, response-adaptive dose-finding clinical trial, and optimal stopping of adaptive clinical trials is presented. Approximate solutions are derived by multiple methods such as basis function regression, one-step look-ahead policy, simulation-based gridding algorithm, and diffusion approximation. Finally, some boundaries to assess the optimality gap and a proof of consistency for approximate solutions are presented to ensure the quality of approximation

Benchmarking online dispatch algorithms for Emergency Medical Services

Providers of Emergency Medical Services (EMS) face the online ambulance dispatch problem, in which they decide which ambulance to send to an incoming incident. Their objective is to minimize the fraction of arrivals later than a target time. Today, the gap between existing solutions and the optimum is unknown, and we provide a bound for this gap.Motivated by this, we propose a benchmark model (referred to as the offline model) to calculate the optimal dispatch decisions assuming that all incidents are known in advance. For this model, we introduce and implement three different methods to compute the optimal offline dispatch policy for problems with a finite number of incidents. The performance of the offline optimal solution serves as a bound for the performance of an - unknown - optimal online dispatching policy.We show that the competitive ratio (i.e., the worst case performance ratio between the optimal online and the optimal offline solution) of the dispatch problem is infinitely large; that is, even an optimal online dispatch algorithm can perform arbitrarily bad compared to the offline solution. Then, we performed benchmark experiments for a large ambulance provider in the Netherlands. The results show that for this realistic EMS system, when dispatching the closest idle vehicle to every incident, one obtains a fraction of late arrivals that is approximately 2.7 times that of the optimal offline policy. We also analyze another online dispatch heuristic, that manages to reduce this gap to approximately 1.9. This constitutes the first quantification of the gap between online and offline dispatch policies

Real-time ambulance relocation: Assessing real-time redeployment strategies for ambulance relocation

Providers of Emergency Medical Services (EMS) are typically concerned with keeping response times short. A powerful means to ensure this, is to dynamically redistribute the ambulances over the region, depending on the current state of the system. In this paper, we provide new insight into how to optimally (re)distribute ambulances. We study the impact of (1) the frequency of redeployment decision moments, (2) the inclusion of busy ambulances in the state description of the system, and (3) the performance criterion on the quality of the distribution strategy. In addition, we consider the influence of the EMS crew workload, such as (4) chain relocations and (5) time bounds, on the execution of an ambulance relocation. To this end, we use trace-driven simulations based on a real dataset from ambulance providers in the Netherlands. In doing so, we differentiate between rural and urban regions, which typically face different challenges when it comes to EMS. Our results show that: (1) taking the classical 0-1 performance criterion for assessing the fraction of late arrivals only differs slightly from related response time criteria for evaluating the performance as a function of the response time, (2) adding more relocation decision moments is highly beneficial, particularly for rural areas, (3) considering ambulances involved in dropping off patients available for newly coming incidents reduces relocation times only slightly, and (4) simulation experiments for assessing move-up policies are highly preferable to simple mathematical models

Strategic Location and Dispatch Management of Assets in a Military Medical Evacuation Enterprise

This dissertation considers the importance of optimizing deployed military medical evacuation (MEDEVAC) systems and utilizes operations research techniques to develop models that allow military medical planners to analyze different strategies regarding the management of MEDEVAC assets in a deployed environment. For optimization models relating to selected subproblems of the MEDEVAC enterprise, the work herein leverages integer programming, multi-objective optimization, Markov decision processes, approximate dynamic programming, and machine learning, as appropriate, to identify relevant insights for aerial MEDEVAC operations

The Minimum Expected Penalty Relocation Problem for the computation of compliance tables for ambulance vehicles

We study the ambulance relocation problem in which one tries to respond to possible future incidents quickly. For this purpose, we consider compliance table policies: a relocation strategy commonly used in practice. Each compliance table level indicates the desired waiting site locations for the available ambulances. To compute efficient compliance tables, we introduce the minimum expected penalty relocation problem (MEXPREP), which we formulate as an integer linear program. In this problem, one has the ability to control the number of waiting site relocations. Moreover, different performance measures related to response times, such as survival probabilities, can be incorporated. We show by simulation that the MEXPREP compliance tables outperform both the static policy and compliance tables obtained by the maximal expected coverage relocation problem (MECRP), which both serve as benchmarks. Besides, we perform a study on different relocation thresholds and on two different methods to assign available ambulances to desired waiting sites

Ambulance Emergency Response Optimization in Developing Countries

The lack of emergency medical transportation is viewed as the main barrier to the access of emergency medical care in low and middle-income countries (LMICs). In this paper, we present a robust optimization approach to optimize both the location and routing of emergency response vehicles, accounting for uncertainty in travel times and spatial demand characteristic of LMICs. We traveled to Dhaka, Bangladesh, the sixth largest and third most densely populated city in the world, to conduct field research resulting in the collection of two unique datasets that inform our approach. This data is leveraged to develop machine learning methodologies to estimate demand for emergency medical services in a LMIC setting and to predict the travel time between any two locations in the road network for different times of day and days of the week. We combine our robust optimization and machine learning frameworks with real data to provide an in-depth investigation into three policy-related questions. First, we demonstrate that outpost locations optimized for weekday rush hour lead to good performance for all times of day and days of the week. Second, we find that significant improvements in emergency response times can be achieved by re-locating a small number of outposts and that the performance of the current system could be replicated using only 30% of the resources. Lastly, we show that a fleet of small motorcycle-based ambulances has the potential to significantly outperform traditional ambulance vans. In particular, they are able to capture three times more demand while reducing the median response time by 42% due to increased routing flexibility offered by nimble vehicles on a larger road network. Our results provide practical insights for emergency response optimization that can be leveraged by hospital-based and private ambulance providers in Dhaka and other urban centers in LMICs

The Impact of Threat Levels at the Casualty Collection Point on Military Medical Evacuation System Performance

One of the primary duties of the Military Health System is to provide effective and efficient medical evacuation (MEDEVAC) to injured battlefield personnel. To accomplish this, military medical planners seek to develop high-quality dispatching policies that dictate how deployed MEDEVAC assets are utilized throughout combat operations. This thesis seeks to determine dispatching policies that improve the performance of the MEDEVAC system. A discounted, infinite-horizon continuous-time Markov decision process (MDP) model is developed to examine the MEDEVAC dispatching problem. The model incorporates problem features that are not considered under the current dispatching policy (e.g., myopic policy), which tasks the closest-available MEDEVAC unit to service an incoming request. More specifically, the MDP model explicitly accounts for admission control, precedence level of calls, different asset types (e.g., Army versus Air Force helicopters), and threat level at casualty collection points. An approximate dynamic programming (ADP) algorithm is developed within an approximate policy iteration algorithmic framework that leverages kernel regression to approximate the state value function. The ADP algorithm is used to develop high-quality solutions for large scale problems that cannot be solved to optimality due to the curse of dimensionality. We develop a notional scenario based on combat operations in southern Afghanistan to investigate model performance, which is measured in terms of casualty survivability. The results indicate that significant improvement in MEDEVAC system performance can be obtained by utilizing either the MDP or ADP generated policies. These results inform the development and implementation of tactics, techniques and procedures for the military medical planning community