In recent years, healthcare management has become fertile ground for the scheduling theory community. In addition to an extensive academic literature on this subject, there has also been a proliferation of healthcare scheduling software companies in the marketplace. Typical scheduling systems use rule-based analytics that give schedulers advisory information from programmable heuristics such as the Bailey-Welch rule cite{B,BW}, which recommends overbooking early in the day to fill-in potential no-shows later on. We propose a dynamic programming problem formulation to the scheduling problem that maximizes revenue. We formulate the problem and discuss the effectiveness of 3 different algorithms that solve the problem. We find that the 3rd algorithm, which has smallest amount of nodes in the decision tree, has an upper bound given by the Bell numbers. We then present an alternative problem formulation that includes stochastic appointment lengths and no shows.

In the last two decades, the western world witnessed a continuous rise in the health expenditure. Meanwhile, complaints from patients on excessive waiting times are also increasing. In the past, many researchers have tried to devise appointment scheduling rules to provide trade-offs between maximizing patients’ satisfaction and minimizing the costs of the health providers. For instance, this challenge appears appointment scheduling problems (ASP). Commonly used methods in ASP include analytical methods, simulation studies, and combination of simulation with heuristic approaches. Analytical methods (e.g., queuing theory and mathematical programming) face challenges of fully capturing the complexities of systems and usually make strong assumptions for tractability of problems. These methods simplify the whole system to a single-stage unit and ignore the actual system factors such as the presence of multiple stages and/or resource constraints. Simulation studies, conversely, are able to model most complexities of the actual system, but they typically lack an optimization strategy to deliver optimal appointment schedules. Also, heuristic approaches normally are based on intuitive rules and do not perform well as standalone methods. In order to reach an optimal schedule while considering complexities in actual health care systems, this thesis proposes efficient and effective methods that yield (near) optimal appointment schedules by integrating mathematical programming, a tabu search optimization algorithm and discrete event simulation. The proposed methodologies address the challenges and complexities of scheduling in real world multistage healthcare units in the presence of stochastic service durations, a mix of patient types, patients with heterogeneous service sequence, and resource constraints. Moreover, the proposed methodology is capable of finding the optimum considering simultaneously multiple performance criteria. A Pareto front (a set of optimal solutions) for the performance criteria can be obtained using the proposed methods. Healthcare management can use the Pareto front to choose the appropriate policy based on different conditions and priorities. In addition, the proposed method has been applied to two case studies of Operating Rooms departments in two major Canadian hospitals. The comparison of actual schedules and the ones yielded by the proposed method indicates that proposed method can improve the appointment scheduling in realistic clinical settings.

In the last two decades, the western world witnessed a continuous rise in the health expenditure. Meanwhile, complaints from patients on excessive waiting times are also increasing. In the past, many researchers have tried to devise appointment scheduling rules to provide trade-offs between maximizing patients’ satisfaction and minimizing the costs of the health providers. For instance, this challenge appears appointment scheduling problems (ASP). Commonly used methods in ASP include analytical methods, simulation studies, and combination of simulation with heuristic approaches. Analytical methods (e.g., queuing theory and mathematical programming) face challenges of fully capturing the complexities of systems and usually make strong assumptions for tractability of problems. These methods simplify the whole system to a single-stage unit and ignore the actual system factors such as the presence of multiple stages and/or resource constraints. Simulation studies, conversely, are able to model most complexities of the actual system, but they typically lack an optimization strategy to deliver optimal appointment schedules. Also, heuristic approaches normally are based on intuitive rules and do not perform well as standalone methods. In order to reach an optimal schedule while considering complexities in actual health care systems, this thesis proposes efficient and effective methods that yield (near) optimal appointment schedules by integrating mathematical programming, a tabu search optimization algorithm and discrete event simulation. The proposed methodologies address the challenges and complexities of scheduling in real world multistage healthcare units in the presence of stochastic service durations, a mix of patient types, patients with heterogeneous service sequence, and resource constraints. Moreover, the proposed methodology is capable of finding the optimum considering simultaneously multiple performance criteria. A Pareto front (a set of optimal solutions) for the performance criteria can be obtained using the proposed methods. Healthcare management can use the Pareto front to choose the appropriate policy based on different conditions and priorities. In addition, the proposed method has been applied to two case studies of Operating Rooms departments in two major Canadian hospitals. The comparison of actual schedules and the ones yielded by the proposed method indicates that proposed method can improve the appointment scheduling in realistic clinical settings.

Expansion of Medicaid and private health insurance coverage through passage of the Affordable Care Act of 2010 was expected to increase primary care access and reduce emergency department (ED) use by reducing financial burden and improving affordability of care. The aim of this study was to examine the differences in utilization patterns that exist among the Medicaid population that participated in an optimal level of care (OLC) intervention inclusive of appointments scheduled to primary care providers. Using the integrated behavior model as a theoretical framework, the key research question focused on determining if there was a difference in ED use among Medicaid individuals who scheduled follow-up appointments compared to those that did not schedule follow-up appointments. The sample population consisted of 176 Medicaid enrollees who presented to the ED for treatment of nonurgent conditions and participated in an OLC intervention from June 2016 to July 2017. The results showed that there were no differences in ED utilization between the population that had scheduled appointments compared to the population that did not have scheduled appointments. A bivariate analysis on demographic variables also showed no differences in ED utilization among the variables. The social change implications of this study are that the practice of scheduling appointments with primary care providers does not reduce or affect ED utilization in the Medicaid population. This study contributes to positive social change through the findings that reducing ED utilization requires more than follow-up appointment scheduling with primary care providers. Further studies are warranted to understand the potential barriers and factors that affect ED utilization.

[pt] Os sistemas de agendamento de consultas tradicionalmente realizam as marcações dos pacientes em intervalos fixos de tempo, sem levar em consideração diversos fatores de complexidade do sistema de saúde. Isso pode causar mão de obra ociosa em alguns períodos do dia e longas filas de espera de pacientes em outros momentos. Nesse contexto, esta dissertação tem como intuito propor uma nova política de agendamento para uma clínica especializada em cirurgia bariátrica do Rio de Janeiro, considerando os fatores desse sistema: tempos de serviços estocásticos, impontualidade do paciente, atrasos e interrupções do serviço, e presenças de no-shows. Esta dissertação analisou diversos cenários com overbooking (marcação de pacientes adicionais), e testou as principais políticas por meio de simulação, propondo a mais adequada para a clínica. As 18 políticas de agendamento aplicadas foram encontradas através de uma extensa revisão sistemática da literatura. Com a utilização da política de agendamento OFFSET, foi possível aumentar o número de atendimentos em 30 por cento para o agendamento do principal provedor da clínica, mantendo o nível de serviço prestado. Além disto, foi proposta uma nova política de agendamento, denominada DOME CYCLE, que teve resultados superiores às demais políticas da literatura na média dos cenários realizados. / [en] Appointment-scheduling systems traditionally schedule patient appointments at fixed intervals, without taking into account several complexity factors of health-care system. This schedule can make the server idle at certain times of the day and can produce long queues of patients at other times. In this context, the objective of this study is to propose a new scheduling policy for a clinic specialized in bariatric surgery in Rio de Janeiro, considering the following factors of this system: stochastic service times, patient unpunctuality, delays and interruptions of the provider and presences of no-shows. This study analyzed several scenarios with overbooking, and tested the main policies with a simulation model, proposing the most appropriate for the clinic. The 18 scheduling policies applied were found through an extensive systematic review of the literature. With the use of the OFFSET scheduling policy, it was possible to increase the number of appointments by 30 percent to the scheduling of the main clinic provider, maintaining the level of service provided. In addition, a new scheduling policy, called DOME CYCLE, was proposed, which has outperformed the other policies in the average of the tests performed.