Modeling a healthcare system as a queueing network:The case of a Belgian hospital.

The performance of health care systems in terms of patient flow times and utilization of critical resources can be assessed through queueing and simulation models. We model the orthopaedic department of the Middelheim hospital (Antwerpen, Belgium) focusing on the impact of outages (preemptive and nonpreemptive outages) on the effective utilization of resources and on the flowtime of patients. Several queueing network solution procedures are developed such as the decomposition and Brownian motion approaches. Simulation is used as a validation tool. We present new approaches to model outages. The model offers a valuable tool to study the trade-off between the capacity structure, sources of variability and patient flow times.Belgium; Brownian motion; Capacity management; Decomposition; Health care; Healthcare; Impact; Model; Models; Performance; Performance measurement; Queueing; Queueing theory; Simulation; Stochastic processes; Structure; Studies; Systems; Time; Tool; Validation; Variability;

Applying and integer Linear Programming Model to an appointment scheduling problem

Dissertação de Mestrado, Ciências Económicas e Empresariais (Economia e Políticas Públicas), 28 de fevereiro de 2022, Universidade dos Açores.A gestão de consultas ambulatórias pode ser um processo complexo, uma vez que envolve vários stakeholders com diferentes objetivos. Para os utentes poderá ser importante minimizar os tempos de espera. Simultaneamente, para os trabalhadores do setor da saúde, condições de trabalho justas devem ser garantidas. Assim, é cada vez mais necessário ter em conta o equilíbrio de cargas horárias e a otimização dos recursos disponíveis como principais preocupações no agendamento e planeamento de consultas. Nesta dissertação, uma abordagem com dois modelos para a criação de um sistema de agendamento de consultas é proposta. Esta abordagem é feita em programação linear, com dois modelos que têm como objetivo minimizar as diferenças de cargas horárias e melhorar o seu equilíbrio ao longo do planeamento. Os modelos foram estruturados e parametrizados de acordo com dados gerados aleatoriamente. Para isso, o desenvolvimento foi feito em Java, gerando assim os dados referidos. O Modelo I minimiza as diferenças de carga horária entre os quartos disponíveis. O Modelo II, por outro lado, propõe uma nova função objetivo que minimiza a diferença máxima observada, com um processo de decisão minxmax. Os modelos mostram resultados eficientes em tempos de execução razoáveis para instâncias com menos de aproximadamente 10 quartos disponíveis. Os tempos de execução mais altos são observados quando as instâncias ultrapassam este número de quartos disponíveis. Em relação ao equilíbrio da carga horária, observou-se que o número de especialidades disponíveis para atendimento e a procura por dia foram o que mais influenciou a minimização da diferença da carga horária. Os resultados do Modelo II mostram melhor tempo de execução e um maior número de soluções ótimas. Uma vez que as diferenças entre os dois modelos não são consideráveis, o Modelo I poderá representar um melhor conjunto de soluções para os decisores já que minimiza a diferença da carga horária total entre quartos em vez de apenas minimizar o valor máximo da diferença de carga horária entre quaisquer dois quartos.ABSTRACT: Outpatient appointment management can be a complex process since it involves many conflicting stakeholders. As for the patients it might be important to minimize waiting time. Simultaneously, for healthcare workers, fair working conditions must be guaranteed. Thus, it is increasingly necessary to have workload balance and resource optimization as the main concerns in the scheduling and planning of outpatient appointments. In this dissertation, a two-model approach for designing an appointment scheduling is proposed. This approach is formulated as two mathematical Integer Linear Programming models that integrate the objective of minimizing workload difference and improving workload balance. The models were structured and parameterized according to randomly generated data. For this, the work was developed in Java, generating said data. Model I minimizes the workload differences among rooms. Model II, on the other hand, proposes a new objective function that minimizes the maximum workload difference, with a minxmax decision process. The computational models behaves efficiently in reasonable run times for numerical examples with less than approximately 10 rooms available. Higher run times are observed when numerical examples surpass these number of available rooms. Regarding workload balance, it was observed that the number of specialties available for appointments and the demand for each day were the most influential in the minimization of workload difference. Model II results show a shorter model run time and more optimal solutions. As the differences between both Models are not considerable, Model I might propose a better set of solution for decision makers since it minimizes the total workload difference amongst rooms instead of only minimizing the maximum workload difference between any two rooms

Reducing Same Day Missed Appointments

Radiology Associates (RA) is a diagnostic imaging center that offers high-quality, digital medical imaging and interventional radiology services for patients, physicians and healthcare organizations across the Central Coast. They are an ongoing problem that involves a considerable portion of their patients not showing up for their appointments Our project aims to reduce same day missed appointments at Radiology Associates. Radiology Associates currently has a no-show rate of 13.48%. They lose approximately $240 for every same day missed appointment. Our goal was to find new ways to reduce their no-show rate to 8$39,285.35 by reducing the no-show percentage by 5.5%. We researched literature on causes of no-shows and alternative scheduling methods. We then mapped out the scheduling process and analyzed data on no-shows. After discovering some potential causes for the high no-show rate, we constructed solutions and created standard operating procedures

Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

Improvement of surgery duration estimation using statistical methods and analysis of scheduling policies using discrete event simulation

The United States health care system currently faces many challenges, with the most notable one being rising costs. In an effort to decrease those costs, health providers are aiming to improve efficiency in their operations. A primary source of revenue for hospitals and some clinics is the surgery department, making it a key department for improvement in efficiency. Surgery schedules drive the department and affect the operations of many other departments. The most significant challenge to creating an efficient surgery schedule is estimating surgery durations and scheduling cases in a manner that will minimize the time a surgery is off schedule and maximize utilization of resources. To identify ways to better estimate surgery durations, an analysis of the surgery scheduling process at UnityPoint Health - Des Moines, in Des Moines, Iowa was completed. Estimated surgery durations were compared to actual durations using a t test. Multiple linear regression models were created for the most common surgeries including the input variables of age of the patient, anesthesiologist, operating room (OR), number of residents, and day of the week. To find optimal scheduling policies, simulation models were created, each representing a series of surgery cases in one operating room during one day. Four scheduling policies were investigated: shortest estimated time first, longest estimated time first, most common surgery first, and adding an extra twenty minutes to each case in the existing order. The performance of the policies was compared to those of the existing schedule. Using the historical data from a one-year period at UnityPoint Health - Des Moines, the estimated surgery durations for the top four surgeries by count and top surgeons were found to be statistically different in 75% of the data sets. After creating multiple linear regression models for each of the top four surgeries and surgeons performing those surgeries, the β values for each variable were compared across models. Age was found to have a minimal impact on surgery duration in all models. The binary variable indicating residents present, was found to have minimal impact as well. For the rest of the variables, consistencies were difficult to assess, making multiple linear regression an unideal method for identifying the impact of the variables investigated. On the other hand, the simulation model proved to be useful in identifying useful scheduling policies. Eight series based on real series were modeled individually. Each model was validated against reality, with 75% of durations simulated in the models not being statistically different than reality. Each of the four scheduling policies was modeled for each series and the average minutes off schedule and idle time between cases were compared across models. Adding an extra twenty minutes to each case in the existing order resulted in the lowest minutes off schedule, but significantly increased the idle time between cases. Most common surgery first did not have a consistent impact on the performance indicators. Longest estimated time first did not improve the performance indicators in the majority of the cases. Shortest estimated time first resulted in the best performance for minutes off schedule and idle time between cases in combination; therefore, we recommend this policy is employed when the scheduling process allows

Queueing Approaches to Appointment System Design

We develop useful queueing models to analyze appointment-based service systems. There are many factors that make appointment scheduling in service systems extremely complex. For example, scheduled customers may not arrive on time or show up at all, customers with different priorities may have conflict of service access, service may last shorter or longer than expected, and so on. These kinds of uncertainties make stochastic modeling a perfect tool to be used to analyze and improve the performance of such systems. The objective of our research is to identify appointment scheduling policies that balance the utilization of expensive service resources and customer waiting. We specifically consider two problems that have been commonly observed in practice but received little attention from the past appointment-scheduling literature. The first problem is how to schedule appointments when scheduled services may be interrupted by service requests with higher priority. We generate the optimal scheduling policies under various scenarios: finite and infinite time horizon, equally spaced and non-equally spaced scheduling, constant and time-dependent interruption rate, and preemptive and non-preemptive service interruptions. In the second problem, we consider the appointment system as two queues in tandem: the appointment queue followed by the service queue. The customers join the appointment queue when they call for an appointment, stay there (not physically) until the appointment time comes, and then leave the appointment queue and physically join the service queue, and wait there until served. We explicitly capture the dependence between these two queues and derive important performance measures of interest, such as service utilization and customer long-run average waiting times in both queues.Doctor of Philosoph

The organizational implications of medical imaging in the context of Malaysian hospitals

This research investigated the implementation and use of medical imaging in the context of Malaysian hospitals. In this report medical imaging refers to PACS, RIS/HIS and imaging modalities which are linked through a computer network. The study examined how the internal context of a hospital and its external context together influenced the implementation of medical imaging, and how this in turn shaped organizational roles and relationships within the hospital itself. It further investigated how the implementation of the technology in one hospital affected its implementation in another hospital. The research used systems theory as the theoretical framework for the study. Methodologically, the study used a case-based approach and multiple methods to obtain data. The case studies included two hospital-based radiology departments in Malaysia. The outcomes of the research suggest that the implementation of medical imaging in community hospitals is shaped by the external context particularly the role played by the Ministry of Health. Furthermore, influences from both the internal and external contexts have a substantial impact on the process of implementing medical imaging and the extent of the benefits that the organization can gain. In the context of roles and social relationships, the findings revealed that the routine use of medical imaging has substantially affected radiographers’ roles, and the social relationships between non clinical personnel and clinicians. This study found no change in the relationship between radiographers and radiologists. Finally, the approaches to implementation taken in the hospitals studied were found to influence those taken by other hospitals. Overall, this study makes three important contributions. Firstly, it extends Barley’s (1986, 1990) research by explicitly demonstrating that the organization’s internal and external contexts together shape the implementation and use of technology, that the processes of implementing and using technology impact upon roles, relationships and networks and that a role-based approach alone is inadequate to examine the outcomes of deploying an advanced technology. Secondly, this study contends that scalability of technology in the context of developing countries is not necessarily linear. Finally, this study offers practical contributions that can benefit healthcare organizations in Malaysia

Process improvement in healthcare: Overall resource efficiency

This paper aims to develop a unifying and quantitative conceptual framework for healthcare processes from the viewpoint of process improvement. The work adapts standard models from operation management to the specifics of healthcare processes. We propose concepts for organizational modeling of healthcare processes, breaking down work into micro processes, tasks, and resources. In addition, we propose an axiological model which breaks down general performance goals into process metrics. The connexion between both types of models is made explicit as a system of metrics for process flow and resource efficiency. The conceptual models offer exemplars for practical support in process improvement efforts, suggesting to project leaders how to make a diagrammatic representation of a process, which data to gather, and how to analyze and diagnose a process's flow and resource utilization. The proposed methodology links on to process improvement methodologies such as business process reengineering, six sigma, lean thinking, theory of constraints, and total quality management. In these approaches, opportunities for process improvement are identified from a diagnosis of the process under study. By providing conceptual models and practical templates for process diagnosis, the framework relates many disconnected strands of research and application in process improvement in healthcare to the unifying pursuit of process improvement