Analysis of scheduling in a diagnostic imaging department: A simulation study

In this thesis we present an Agent-Based Modelling Tool (ABMT) for use in the investigation of the impact that operational level changes have on diagnostic imaging scheduling and patient wait times. This tool represents a novel application of agent-based modelling in the outpatient scheduling/simulation fields. The ABMT is a decision support tool with a user friendly graphical user interface that is capable of modelling a wide array of outpatient scheduling scenarios. The tool was verified and validated using data and expertise from Hotel Dieu Grace Hospital, Windsor, Ontario, Canada. The ABMT represents a technological advancement in the modelling of multi-server, multi-priority class customer queueing systems with deterministic service times and uneven distribution of server up-time

Probability Study of Medical Clinic Scheduling Procedures

In this thesis, we consider the scheduling of patients in a single server medical clinic. We present the probability distribution for the number of patients in the system under certain settings using four different methods. The four methods used are theoretical calculations using convolution, simulation, probability generating functions, and Markov chains. Further, the best scheduling strategy is obtained on the basis of a minimum objective function in the case of fixed interval lengths (for service and interarrival times). Modified simulation annealing is used to aid in finding the best appointment strategy in the case of variable interval lengths

Optimization of Stochastic Models in Health Care: Appointment Scheduling and Disease Testing

We consider two different problems: appointment scheduling and asymptomatic disease testing. For the appointment scheduling problem, the goal is to assign appointment times to minimize a weighted sum of patient wait times, doctor idle time, and clinic overtime. We make the assumption that patients are unpunctual with respect to assigned appointment times and distributional information on unpunctuality is available. We first consider a model with heterogeneous patient distributions in both service time and unpunctuality. This is a complex system that requires heuristic approaches. We are able to show the benefits of capturing patient heterogeneity in addition to the superior performance of our heuristics. Our best methods do not scale well to large patient systems; thus, we consider a second model that allows a large number of patients. For large systems, we assume patient homogeneity; however, patient unpunctuality is permitted to be time-heterogeneous. With this model, we examine the fluid limits of the queue processes to develop a fluid control problem that seeks an asymptotically optimal appointment schedule in the form of an RCLL function. This problem is difficult to solve analytically, so we propose a numerical scheme that converts the control problem into a quadratic program. We examine asymptotically optimal appointment schedules under various unpunctuality distributions, then the superior performance of these schedules in discrete-event simulations. For the asymptomatic disease testing problem, we consider the individual decision-maker problem of choosing when to use disease test kits from a limited supply. We assume an underlying SIR Markov model with split states for asymptomatic and symptomatic states. As only symptoms are directly observable, the decision process is modeled as a partially-observable Markov decision process for deciding when to use tests. The goal is to produce simple instructions for the average consumer to follow. We derive policies that do not require probability computations by the user. Under certain assumptions, we are able to prove that these policies are optimal. Last, we examine a community simulation where infection probabilities are dependent on community infected. Our methods are shown to outperform existing baselines.Doctor of Philosoph

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

Applying Value Stream Mapping to Improve the Delivery of Patient Care in the Oncology Day Hospital

[EN] Improving the delivery of patient care is an ongoing challenge in the National Health Service (NHS). This challenge is not insignificant in the process of chemotherapy administration for oncology patients. The present research is motivated by a public Spanish hospital in which oncology patients receive medical care in the Oncology Day Hospital (ODH). At the ODH, oncology patients receive different health services by different specialists on a single day. Any discoordination in patient flow will contribute to longer waiting times and stays in the ODH. As oncology patients tend to have special health conditions, any extra time in the hospital is a source of risk and discomfort. This study applies value stream mapping methodology in a Spanish ODH to improve this situation, reducing hospital waiting times and shorting the length of stay. For that purpose, the path of the oncology patients is mapped and the current state of the system is analyzed. Working at takt time and levelling the workload are proposed for improving the working conditions for healthcare personnel. As a result, the quality of service for oncology patients who need a well-defined care profile is improved. The singular characteristics of the Spanish NHS make it challenging to implement new ways of working, so this study has significant theoretical and managerial implications offering directions in which improvement is possible.Vidal-Carreras, PI.; García Sabater, JJ.; Marin-Garcia, JA. (2022). Applying Value Stream Mapping to Improve the Delivery of Patient Care in the Oncology Day Hospital. International Journal of Environmental research and Public Health. 19(7):1-18. https://doi.org/10.3390/ijerph1907426511819

Sequential Appointment Scheduling Considering Walk-In Patients

This paper develops a sequential appointment algorithm considering walk-in patients. In practice, the scheduler assigns an appointment time for each call-in patient before the call ends, and the appointment time cannot be changed once it is set. Each patient has a certain probability of being a no-show patient on the day of appointment. The objective is to determine the optimal booking number of patients and the optimal scheduling time for each patient to maximize the revenue of all the arriving patients minus the expenses of waiting time and overtime. Based on the assumption that the service time is exponentially distributed, this paper proves that the objective function is convex. A sufficient condition under which the profit function is unimodal is provided. The numerical results indicate that the proposed algorithm outperforms all the commonly used heuristics, lowering the instances of no-shows, and walk-in patients can improve the service efficiency and bring more profits to the clinic. It is also noted that the potential appointment is an effective alternative to mitigate no-show phenomenon

Measurements of operational efficiency in the outpatient setting

The results of patient flow, work sampling, and demographic analyses of the General Medicine outpatient Clinic at the Medical University of South Carolina are presented. Data collection procedures are discussed. The association between clinic efficiency and selected demographic characteristics of the patient population is examined. Inhibitors to smooth patient flow are revealed through the examination of the distributions of service and queue times. Hourly distributions of staff time expended in various activities are presented, and the work sampling data is correlated with results from the patient flow study. The usefulness of the results as indices of clinic efficiency is considered

A flexible and optimal approach for appointment scheduling in healthcare

Appointment scheduling is generally applied in outpatient clinics and other healthcare services. The challenge in scheduling is to find a strategy for dealing with variability and unpredictability in service duration and patient arrivals. The consequences of an ineffective strategy include long waiting times for patients and idle time for the healthcare provider. In turn, these have implications for the perceived quality, cost-efficiency, and capacity of healthcare services. The generation of optimal schedules is a notoriously intractable problem, and earlier attempts at designing effective strategies for appointment scheduling were based on approximation, simulation, or simplification. We propose a novel strategy for scheduling that exploits three tactical ideas to make the problem manageable. We compare the proposed strategy to other approaches, and show that it matches or outperforms competing methods in terms of flexibility, ease of use, and speed. More importantly, it outperforms competing approaches nearly uniformly in approaching the desired balance between waiting and idle times as specified in a chosen objective function. Therefore, the strategy is a good basis for further enrichments

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