The Impact of Triage Classification Errors on Military Medical Evacuation System Performance

In a deployed environment, evacuation requests of injured personnel are serviced by multiple forms of evacuation including medical evacuation (MEDEVAC) and casualty evacuation (CASEVAC). This thesis focuses on the optimal dispatching policy for MEDEVAC units when triage classification errors and blood transfusion kits are considered. A discounted, infinite-horizon Markov decision process (MDP) model is formulated to analyze the MEDEVAC dispatching problem and determine the optimal policy based on the status of the MEDEVAC units in the system, the priority level of incoming requests, and the locations from which requests originate. A notional, representational scenario based in Azerbaijan is utilized to compare the optimal policy against the currently practiced policy of always dispatching the nearest available MEDEVAC unit. Multiple excursions are analyzed to understand the impact of altering problem parameters, including the misclassification rate, number of aircraft equipped with blood transfusion kits, arrival rate of incoming service requests, aircraft speed, and types of triage classification errors. Results reveal that with the application of the optimal policy found by the MDP model the performance of the MEDEVAC dispatching system improves, wherein performance is measured in terms of casualty survivability. Additionally, the inclusion of blood transfusion kits on board aircraft increase MEDEVAC system performance. This analysis is of interest to the military medical planning community and may inform the development of tactics, techniques, and procedures of future dispatching policies for MEDEVAC systems

Dispatching Fire Trucks under Stochastic Driving Times

In this paper we discuss optimal dispatching of fire trucks, based on a particular dispatching problem that arises at the Amsterdam Fire Department, where two fire trucks are send to the same incident location for a quick response. We formulate the dispatching problem as a Markov Decision Process, and numerically obtain the optimal dispatching decisions using policy iteration. We show that the fraction of late arrivals can be significantly reduced by deviating from current practice of dispatching the closest available trucks, with a relative improvement of on average about $20\%$, and over $50\%$ for certain instances. We also show that driving-time correlation has a non-negligible impact on decision making, and if ignored may lead to performance decrease of over $20\%$ in certain cases. As the optimal policy cannot be computed for problems of realistic size due to the computational complexity of the policy iteration algorithm, we propose a dispatching heuristic based on a queueing approximation for the state of the network. We show that the performance of this heuristic is close to the optimal policy, and requires significantly less computational effort.Comment: Submitted to Computers and Operations Research (December 08, 2018

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

Using Markov Decision Processes with Heterogeneous Queueing Systems to Examine Military MEDEVAC Dispatching Policies

major focus of the Military Health System is to provide efficient and timely medical evacuation (MEDEVAC) to battlefield casualties. Medical planners are responsible for developing dispatching policies that dictate how aerial military MEDEVAC units are utilized during major combat operations. The objective of this research is to determine how to optimally dispatch MEDEVAC units in response to 9-line MEDEVAC requests to maximize MEDEVAC system performance. A discounted, infinite horizon Markov decision process (MDP) model is developed to examine the MEDEVAC dispatching problem. The MDP model allows the dispatching authority to accept, reject, or queue incoming requests based on the request\u27s classification (i.e., zone and precedence level) and the state of the MEDEVAC system. Rejected requests are rerouted to be serviced by other, non-medical military organizations in theater. Performance is measured in terms of casualty survivability rather than a response time threshold since survival probability more accurately represents casualty outcomes. A representative planning scenario based on contingency operations in southern Afghanistan is utilized to investigate the differences between the optimal dispatching policy and three practitioner-friendly myopic baseline policies. Two computational experiments, a two-level, five-factor screening design and a subsequent three-level, three-factor full factorial design, are conducted to examine the impact of selected MEDEVAC problem features on the optimal policy and the system level performance measure. Results indicate that dispatching the closest available MEDEVAC unit is not always optimal and that dispatching MEDEVAC units considering the precedence level of requests and the locations of busy MEDEVAC units increases the performance of the MEDEVAC system. These results inform the development and implementation of MEDEVAC tactics, techniques, and procedures by military medical planners. Moreover, an open question exists concerning the best exact solution approach for solving Markov decision problems due to recent advances in performance by commercial linear programming (LP) solvers. An analysis of solution approaches for the MEDEVAC dispatching problem reveals that the policy iteration algorithm substantially outperforms the LP algorithms executed by CPLEX 12.6 in regards to computational effort. This result supports the claim that policy iteration remains the superlative solution algorithm for exactly solving computationally tractable Markov decision problems

Models and algorithms for trauma network design.

Trauma continues to be the leading cause of death and disability in the US for people aged 44 and under, making it a major public health problem. The geographical maldistribution of Trauma Centers (TCs), and the resulting higher access time to the nearest TC, has been shown to impact trauma patient safety and increase disability or mortality. State governments often design a trauma network to provide prompt and definitive care to their citizens. However, this process is mainly manual and experience-based and often leads to a suboptimal network in terms of patient safety and resource utilization. This dissertation fills important voids in this domain and adds much-needed realism to develop insights that trauma decision-makers can use to design their trauma network. In this dissertation, we develop multiple optimization-based trauma network design approaches focusing minimizing mistriages and, in some cases, ensuring equity in care among regions. To mimic trauma care in practice, several realistic features are considered in our approach, which include the consideration of: (i) both severely and non-severely injured trauma patients and associated mistriages, (ii) intermediate trauma centers (ITCs) along with major trauma centers (MTCs), (iii) three dominant criteria for destination determination, and (iv) mistriages in on-scene clinical assessment of injuries. Our first contribution (Chapter 2) proposes the Trauma Center Location Problem (TCLP) that determines the optimal number and location of major trauma centers (MTCs) to improve patient safety. The bi-objective optimization model for TCLP explicitly considers both types of patients (severe and non-severe) and associated mistriages (specifically, system-related under- and over-triages) as a surrogate for patient safety. These mistriages are estimated using our proposed notional tasking algorithm that attempts to mimic the EMS on-scene decision of destination hospital and transportation mode. We develop a heuristic based on Particle Swarm Optimization framework to efficiently solve realistic problem sizes. We illustrate our approach using 2012 data from the state of OH and show that an optimized network for the state could achieve 31.5% improvement in patient safety compared to the 2012 network with the addition of just one MTC; redistribution of the 21 MTCs in the 2012 network led to a 30.4% improvement. Our second contribution (Chapter 3) introduces a Nested Trauma Network Design Problem (NTNDP), which is a nested multi-level, multi-customer, multi-transportation, multi-criteria, capacitated model. The NTNDP model has a bi-objective of maximizing the weighted sum of equity and effectiveness in patient safety. The proposed model includes intermediate trauma centers (TCs) that have been established in many US states to serve as feeder centers to major TCs. The model also incorporates three criteria used by EMS for destination determination; i.e., patient/family choice, closest facility, and protocol. Our proposed ‘3-phase’ approach efficiently solves the resulting MIP model by first solving a relaxed version of the model, then a Constraint Satisfaction Problem, and a modified version of the original optimization problem (if needed). A comprehensive experimental study is conducted to determine the sensitivity of the solutions to various system parameters. A case study is presented using 2019 data from the state of OH that shows more than 30% improvement in the patient safety objective. In our third contribution (Chapter 4), we introduce Trauma Network Design Problem considering Assessment-related Mistriages (TNDP-AM), where we explicitly consider mistriages in on-scene assessment of patient injuries by the EMS. The TNDP-AM model determines the number and location of major trauma centers to maximize patient safety. We model assessment-related mistriages using the Bernoulli random variable and propose a Simheuristic approach that integrates Monte Carlo Simulation with a genetic algorithm (GA) to solve the problem efficiently. Our findings indicate that the trauma network is susceptible to assessment-related mistriages; specifically, higher mistriages in assessing severe patients may lead to a 799% decrease in patient safety and potential clustering of MTCs near high trauma incidence rates. There are several implications of our findings to practice. State trauma decision-makers can use our approaches to not only better manage limited financial resources, but also understand the impact of changes in operational parameters on network performance. The design of training programs for EMS providers to build standardization in decision-making is another advantage

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

STRATEGIES TO IMPROVE THE EFFICIENCY OF EMERGENCY MEDICAL SERVICE (EMS) SYSTEMS UNDER MORE REALISTIC CONDITIONS

Emergency medical service (EMS) systems provide medical care to pre-hospital patients who need rapid response and transportation. This dissertation proposes a new realistic approach for EMS systems in two major focuses: multiple unit dispatching and relocation strategies. This work makes recommendations for multiple-unit dispatch to multiple call priorities based on simulation optimization and heuristics. The objective is to maximize the expected survival rate. Simulation models are proposed to determine the optimization. A heuristic algorithm is developed for large-scale problems. Numerical results show that dispatching while considering call priorities, rather than always dispatching the closest medical units, could improve the effectiveness of EMS systems. Additionally, we extend the model of multiple-unit dispatch to examine fairness between call priorities. We consider the potentially-life-threatening calls which could be upgraded to life-threatening. We formulate the fairness problem as an integer programming model solved using simulation optimization. Taking into account fairness between priorities improves the performance of EMS systems while still operating at high efficiency. As another focus, we consider dynamic relocation strategy using a nested-compliance table policy. For each state of the EMS systems, a decision must be made regarding exactly which ambulances will be allocated to which stations. We determine the optimal nested-compliance table in order to maximize the expected coverage, in the binary sense, as will be later discussed. We formulate the nested-compliance table model as an integer program, for which we approximate the steady-state probabilities of EMS system to use as parameters to our model. Simulation is used to investigate the performance of the model and to compare the results to a static policy based on the adjusted maximum expected covering location problem (AMEXCLP). Additionally, we extend the nested-compliance table model to consider an upper bound on relocation time. We analyze the decision regarding how to partition the service area into smaller sub-areas (districts) in which each sub-area operates independently under separate relocation strategies. We embed the nested-compliance table model into a tabu search heuristic algorithm. Iteration is used to search for a near-optimal solution. The performance of the tabu search heuristic and AMEXCLP are compared in terms of the realized expected coverage of EMS systems

PRIORITIZATION AND DISTRIBUTION OF CASUALTIES IN DISASTER RESPONSE MANAGEMENT

This dissertation focused on two different problems that typically arise in the aftermath of disasters. In the first part of the dissertation, we study the problem of how casualties should be prioritized and distributed to different medical facilities in the aftermath of mass casualty incidents (MCIs) with the objective of maximizing the expected total number of survivors. Assuming that casualties have been triaged into two classes differentiated by their severity levels and medical needs, the decision-maker needs to prioritize and distribute casualties using a limited number of ambulances to multiple medical facilities with different capacities. By explicitly taking into consideration the capacity and service time at each medical facility, we formulate this sequential decision-making problem as a Markov decision process (MDP). Based on this MDP formulation, we propose heuristic policies that prescribe decisions on prioritization and distribution of casualties. We then employ discrete-event simulations to demonstrate the benefits of using the proposed heuristics against some benchmark policies under several realistic mass casualty incident scenarios such as terrorist attacks, major traffic accidents, and earthquakes. In the second part of the dissertation, we study the resource allocation problem in urban search and rescue operations that follow natural disasters. Specifically, we consider a scenario in which some individuals are trapped at various locations within a geographical area and there is a limited time window during which these individuals can be rescued. We model the problem as an MDP. Then, we characterize the optimal policy under the assumption that individuals belong to only one of two locations. We propose heuristics for the general version of the problem. Finally, the proposed heuristics are examined with a simulation.Doctor of Philosoph

A STUDY ON LOCATION SET COVERING MODELS WITH TWO APPLICATIONS

Ph.DDOCTOR OF PHILOSOPH