Automated Radiation Therapy Patient Scheduling: A Case Study at a Belgian Hospital

The predicted increase in the number of patients receiving radiation therapy (RT) to treat cancer calls for an optimized use of resources. To manually schedule patients on the linear accelerators delivering RT is a time-consuming and challenging task. Operations research (OR), a discipline in applied mathematics, uses a variety of analytical methods to improve decision-making. In this paper, we study the implementation of an OR method that automatically generates RT patient schedules at an RT center with ten linear accelerators. The OR method is designed to produce schedules that mimic the objectives used in the clinical scheduling while following the medical and technical constraints. The resulting schedules are clinically validated and compared to manually constructed, historical schedules for a time period of one year. It is shown that the use of OR to generate schedules decreases the average patient waiting time by 80%, improves the consistency in treatment times between appointments by 80%, and increases the number of treatments scheduled the machine best suited for the treatment by more than 90% compared to the manually constructed clinical schedules, without loss of performance in other quality metrics. Furthermore, automatically creating patient schedules can save the clinic many hours of administrative work every week.Comment: 11 page

An analytical approach for improving patient-centric delivery of dialysis services

In this paper, we report on the development of an analytical model and a decision support tool for meeting the complex challenge of scheduling dialysis patients. The tool has two optimization objectives: First, waiting times for the start of the dialysis after the patients’ arrivals must be minimized. Second, the minimization of lateness after the scheduled finish time, which is relevant for transport services, are pursued. We model the problem as a mathematical program considering clinical pathways, a limited number of nurses managing the patients, and dialysis stations. Furthermore, information about patients' drop-off and pick-up time windows at/from the dialysis unit are considered. We develop a platform in Microsoft Excel and implement the analytical model using an Open Source optimization solver. A case study from a dialysis unit in the UK shows that a user can compute a schedule efficiently and the results provide useful information for patients, caregivers, clinicians and transport services

An analytical approach for improving patient-centric delivery of dialysis services

In this paper, we report on the development of an analytical model and a decision support tool for meeting the complex challenge of scheduling dialysis patients. The tool has two optimization objectives: First, waiting times for the start of the dialysis after the patients’ arrivals must be minimized. Second, the minimization of lateness after the scheduled finish time, which is relevant for transport services, are pursued. We model the problem as a mathematical program considering clinical pathways, a limited number of nurses managing the patients, and dialysis stations. Furthermore, information about patients' drop-off and pick-up time windows at/from the dialysis unit are considered. We develop a platform in Microsoft Excel and implement the analytical model using an Open Source optimization solver. A case study from a dialysis unit in the UK shows that a user can compute a schedule efficiently and the results provide useful information for patients, caregivers, clinicians and transport services

Strategic Level Proton Therapy Patient Admission Planning: A Markov Decision Process Modeling Approach

A relatively new consideration in proton therapy planning is the requirement that the mix of patients treated from different categories satisfy desired mix percentages. Deviations from these percentages and their impacts on operational capabilities are of particular interest to healthcare planners. In this study, we investigate intelligent ways of admitting patients to a proton therapy facility that maximize the total expected number of treatment sessions (fractions) delivered to patients in a planning period with stochastic patient arrivals and penalize the deviation from the patient mix restrictions. We propose a Markov Decision Process (MDP) model that provides very useful insights in determining the best patient admission policies in the case of an unexpected opening in the facility (i.e., no-shows, appointment cancellations, etc.). In order to overcome the curse of dimensionality for larger and more realistic instances, we propose an aggregate MDP model that is able to approximate optimal patient admission policies using the worded weight aggregation technique. Our models are applicable to healthcare treatment facilities throughout the United States, but are motivated by collaboration with the University of Florida Proton Therapy Institute (UFPTI)

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

Escalonamento de pacientes num serviço hospitalar de radioterapia

Mestrado em Métodos Quantitativos para a Decisão Económica e EmpresarialO tema do presente Trabalho de Final de Mestrado é a otimização do escalonamento de pacientes no serviço de Radioterapia do Hospital da Luz. Semanalmente são escalonados os pacientes que iniciam os seus tratamentos mantendo a escala dos pacientes cujos tratamentos se encontram a decorrer. Numa primeira fase foi necessário compreender todo o funcionamento do serviço, bem como os tratamentos de radioterapia. Numa segunda fase, foi recolhida informação mais específica acerca do processo de escalonamento utilizado para decidir quais seriam as abordagens a considerar. Para traduzir o problema encontrado são apresentados dois modelos de Programação Linear Inteira. Os modelos são testados numa instância gerada aleatoriamente com dimensão próxima da real. Os resultados obtidos foram analisados comparativamente procurando-se igualmente estudar uma possibilidade de combinação de ambos.The theme of this Masters Final Work is focused on the optimization of a schedule of patients in the Radiotherapy service of Hospital da Luz. Patients who start their treatments are weekly staggered, keeping the scale of patients whose treatments are in progress. First, it was necessary to understand the whole operation of the service, as well as the radiotherapy treatments. In a second phase, more specific information about the scheduling process used was collected in order to decide which approaches should be considered. To translate the problem found, two Integer Linear Programming Models are presented. The models were tested in a randomly generated instance with a dimension close to what was observed. The obtained results were comparatively analyzed, and a possibility of combining the two models was also studied.info:eu-repo/semantics/publishedVersio

Escalonamento de pacientes de radioterapia

Mestrado em Decisão Económica e EmpresarialO tema deste Trabalho Final de Mestrado (TFM) centra-se na otimização de um escalonamento de pacientes de radioterapia, no contexto do Hospital da Luz, em Lisboa. Este TFM foi desenvolvido sob a forma de um projeto, tendo como principal objetivo maximizar a oferta de tratamentos por dia, minimizando o tempo de inatividade do serviço no seu horário de funcionamento diário. Assim, a fase inicial do projeto consistiu em compreender como funcionava o serviço de radioterapia e aprender algumas noções acerca dos tratamentos que podem ser feitos. Posteriormente, procedeu-se à recolha de dados, que correspondem aos tempos específicos de tratamento de cada paciente, tendo em consideração o tumor a ser tratado e a técnica utilizada. Este estudo foi elaborado tendo como base um escalonamento inicial correspondente aos pacientes em tratamento. Esta afetação é revista semanalmente por forma a planear a semana seguinte. Assim, conhecidos os pacientes que finalizam e os que iniciam tratamento na semana seguinte, pretende-se afetar os vários pacientes às vagas disponíveis. O problema identificado e formulado foi resolvido com recurso ao Solver do Excel. A solução obtida é posteriormente escrita numa folha de Excel com recurso a um programa desenvolvido em VBA.This Masters' Final Work (MFW) is focused on optimizing a schedule for radiotherapy patients in the context of Hospital da Luz, in Lisbon. My MFW is considered as a project, with the main objective of maximizing the number of treatments per day, while minimizing service idle time within its daily workload. Thus, the initial phase of the project was to understand how the radiotherapy service works and learn about treatments that can be done. Then, it was proceeded to the collection of data, which correspond to specific time of treatment of each patient, taking into account the tumor to be treated and the technique used. This study was based on an initial schedule according to the patients under treatment. The allocation is reviewed on a weekly basis in order to plan the next week. Thus, knowing the patients who complete and those who start treatment in the following week, the aim is to affect the new patients to available places, as much as possible. The identified and formulated problem was solved via Excel Solver. The solution obtained is subsequently written in an Excel spreadsheet using a program developed in VBA

Three Essays on Data-Driven Optimization for Scheduling in Manufacturing and Healthcare

This dissertation consists of three essays on data-driven optimization for scheduling in manufacturing and healthcare. In Chapter 1, we briefly introduce the optimization problems tackled in these essays. The first of these essays deals with machine scheduling problems. In Chapter 2, we compare the effectiveness of direct positional variables against relative positional variables computationally in a variety of machine scheduling problems and we present our results. The second essay deals with a scheduling problem in healthcare: the team primary care practice. In Chapter 3, we build upon the two-stage stochastic integer programming model introduced by Alvarez Oh (2015) to solve this challenging scheduling problem of determining patient appointment times to minimize a weighted combination of patient wait and provider idle times for the team practice. To overcome the computational complexity associated with solving the problem under the large set of scenarios required to accurately capture uncertainty in this setting, our approach relies on a lower bounding technique based on solving an exhaustive and mutually exclusive group of scenario subsets. Our computational results identify the structure of optimal schedules and quantify the impact of nurse flexibility, patient crossovers and no-shows. We conclude with practical scheduling guidelines for team primary care practices. The third essay deals with another scheduling problem observed in a manufacturing setting similar to first essay, this time in aerospace industry. In Chapter 4, we propose mathematical models to optimize scheduling at a tactical and operational level in a job shop at an aerospace parts manufacturer and implement our methods using real-life data collected from this company. We generalize the Multi-Level Capacitated Lot-Sizing Problem (MLCLSP) from the literature and use novel computational techniques that depend on the data structure observed to reduce the size of the problem and solve realistically-sized instances in this chapter. We also provide a sensitivity analysis of different modeling techniques and objective functions using key performance indicators (KPIs) important for the manufacturer. Chapter 5 proposes extensions of models and techniques that are introduced in Chapters 2, 3 and 4 and outlines future research directions. Chapter 6 summarizes our findings and concludes the dissertation

Simulation du flux de patients en clinique externe

RÉSUMÉ : Au Canada, nous avons la chance de profiter d’un système de santé public gratuit lors des consultations et pour la grande majorité des traitements. Cependant, cette méthode de fonctionnement est très coûteuse pour les contribuables ce qui limite l’augmentation du nombre de centres, de ressources matérielles et qui ne permet pas d’engager du personnel supplémentaire. Avec tous les progrès technologiques qui permettent aux médecins de réduire les temps de traitement ou de suivi, nous constatons encore la présence de listes d’attente pour la majeure partie des spécialités médicales. En clinique externe, les gestionnaires cherchent à augmenter le débit de patients vus en une journée, mais il est difficile de prévoir le temps exact de consultation, les délais d’arrivée des patients, le taux d’absentéisme aux rendez-vous, etc. Ainsi, créer un horaire qui permet de voir un maximum de patients sans toutefois engendrer des heures supplémentaires pour les ressources devient une tâche complexe. Encore aujourd’hui, la plupart des séquences de traitement sont fixées par une agente administrative lorsqu’une place est disponible. Les plages de traitements sont de durées fixes et parfois sont toutes en début de journée ce qui engendre une attente très importante pour les patients. Nous avons donc développé un modèle de simulation qui permet d’exécuter des milliers de scénarios pour analyser l’impact de changements dans l’horaire et ainsi bâtir des horaires plus robustes. Ce modèle nous a permis d’étudier les gains dans l’attente indirecte, c’est-à-dire entre les rendez-vous d’un patient à l’hôpital, pour une clinique de radiothérapie et l’attente directe, celle encourue lors d’une visite à l’hôpital. La structure de ce modèle permet d’être adapté à plusieurs types de cliniques externes et pourrait être utilisée par un gestionnaire d’unité afin de créer des créneaux pour différents types de cliniques ou d’analyser en temps réel l’impact de l’ajout d’un patient dans une certaine plage horaire. Ce gestionnaire pourrait donc estimer le temps total qu’un patient passerait dans l’unité, le temps qu’il mettrait avant d’obtenir son traitement, le temps supplémentaire effectué par le personnel ainsi que le taux d’utilisation de chacune des ressources.----------ABSTRACT : In Canada, we are lucky to have a public health system in which all the consultations and most of the treatments are free. The principal downside of this system is that it is costly so it is impossible to expand the centers, buy new equipment or hire extra personnel. With all the technologic developments that helped physicians reduce the treatment times or follow up delays, we still encounter large queues for an appointment with a specialist. In the outpatient clinics, managers are looking to increase the rate of patients seen daily, but it is really hard to predict the exact time of consultation, the time of arrival of the patients, the no-show rate to the appointment, etc. Therefore, it is almost impossible to build a patient schedule in which a maximum number of patients are seen without creating overtime for the personnel and waiting time for the patients. There are many aspects someone has to consider while building a patient schedule and even nowadays most of these schedules are hand made by a resource of the unit. The first available slot is given to the patient currently asking for an appointment in a template where all slots are of mostly equal length. Some units also book all patients at the beginning of the day to minimize the idle time of physicians, strategy that creates an important wait time for most of the patients. During this project, we developed a simulation model that allowed us to replicate thousands of scenarios in a short time to analyze the impact of movement in the schedule and to build more robust schedules. This model allowed us to see potential reduction in the wait experienced between two separate appointments (indirect wait), and in the wait endured during one hospital visit (direct wait). We built this model so it can be adapted to multiple types of outpatient clinics and so it could be used by a unit manager to make a real time analysis of the impact of adding a particular patient at a certain time during the day. The manager could estimate the total length of the clinic, the overtime of the personnel, the utilization rate of all the resources, the total time spent by a patient in the unit and the time elapsed between the first consultation and the end of the last treatment in the unit

ROBUST RADIOTHERAPY APPOINTMENT SCHEDULING

Optimal scheduling of patients waiting for radiation treatments is a quite challenging operational problem in radiotherapy clinics. Long waiting times for radiotherapy treatments is mainly due to imbalanced supply and demand of radiotherapy services, which negatively affects the effectiveness and efficiency of the healthcare delivered. On the other hand, variations in the time required to set-up machines for each individual patient as well as patient treatment times make this problem even more involved. Efficient scheduling of patients on the waiting list is essential to reduce the waiting time and its possible adverse direct and indirect impacts on the patient. This research is focused on the problem of scheduling patients on a prioritized radiotherapy waiting list while the rescheduling of already booked patients is also possible. The aforementioned problem is formulated as a mixed-integer program that aims for maximizing the number of newly scheduled patients such that treatment time restrictions, scheduling of patients on consecutive days on the same machine, covering all required treatment sessions, as well as the capacity restriction of machines are satisfied. Afterwards, with the goal of protecting the schedule against treatment time perturbations, the problem is reformulated as a cardinality-constrained robust optimization model. This approach provides some insights into the adjustment of the level of robustness of the patients schedule over the planning horizon and protection against uncertainty. Further, three metaheuristics, namely Whale Optimization Algorithm, Particle Swarm Optimization, and Firefly Algorithm are proposed as alternative solution methods. Our numerical experiments are designed based on a case study inspired from a real radiotherapy clinic. The first goal of experiments is to analyze the performance of proposed robust radiotherapy appointment scheduling (ASP) model in terms of feasibility of schedule and the number of scheduled patients by the aid of Monte-Carlo simulation. Our second goal is to compare the solution quality and CPU time of the proposed metaheuristics with a commercial solver. Our experimental results indicate that by only considering half of patients treatment times as worst-case scenario, the schedule proposed by the robust RAS model is feasible in the presence of all randomly generated scenarios for this uncertain parameter. On the other hand, protecting the schedule against uncertainty at the aforementioned level would not significantly reduce the number of scheduled patients. Finally, our numerical results on the three metaheuristics indicate the high quality of their converged solution as well as the reduced CPU time comparing to a commercial solver