Appointment scheduling model in healthcare using clustering algorithms

In this study we provided a scheduling procedure which is combination of machine learning and mathematical programming. Outpatients who request for appointment in healthcare facilities have different priorities. Determining the priority of outpatients and allocating the capacity based on the priority classes are important concepts that have to be considered in scheduling of outpatients. Two stages are defined for scheduling an incoming patient. In the first stage, We applied and compared different clustering methods such as k-mean clustering and agglomerative hierarchical clustering methods to classify outpatients into priority classes and suggested the best pattern to cluster the outpatients. In the second stage, we modeled the scheduling problem as a Markov Decision Process (MDP) problem that aims to decrease waiting time of higher priority outpatients. Due to the curse of dimensionality, we used fluid approximation method to estimate the optimal solution of the MDP. We applied our methodology on a dataset of Shaheed Rajaei Medical and Research Center in Iran, and we showed how our models work in prioritizing and scheduling of outpatients

Schedulability Analysis of Task Sets with Upper- and Lower-Bound Temporal Constraints

Increasingly, real-time systems must handle the self-suspension of tasks (that is, lower-bound wait times between subtasks) in a timely and predictable manner. A fast schedulability test that does not significantly overestimate the temporal resources needed to execute self-suspending task sets would be of benefit to these modern computing systems. In this paper, a polynomial-time test is presented that is known to be the first to handle nonpreemptive self-suspending task sets with hard deadlines, where each task has any number of self-suspensions. To construct the test, a novel priority scheduling policy is leveraged, the jth subtask first, which restricts the behavior of the self-suspending model to provide an analytical basis for an informative schedulability test. In general, the problem of sequencing according to both upper-bound and lower-bound temporal constraints requires an idling scheduling policy and is known to be nondeterministic polynomial-time hard. However, the tightness of the schedulability test and scheduling algorithm are empirically validated, and it is shown that the processor is able to effectively use up to 95% of the self-suspension time to execute tasks.Boeing Scientific Research LaboratoriesNational Science Foundation (U.S.). Graduate Research Fellowship (Grant 2388357

Managing Operational Efficiency And Health Outcomes At Outpatient Clinics Through Effective Scheduling

A variety of studies have documented the substantial deficiencies in the quality of health care delivered across the United States. Attempts to reform the United States health care system in the 1980s and 1990s were inspired by the system\u27s inability to adequately provide access, ensure quality, and restrain costs, but these efforts had limited success. In the era of managed care, access, quality, and costs are still challenges, and medical professionals are increasingly dissatisfied. In recent years, appointment scheduling in outpatient clinics has attracted much attention in health care delivery systems. Increase in demand for health care services as well as health care costs are the most important reasons and motivations for health care decision makers to improve health care systems. The goals of health care systems include patient satisfaction as well as system utilization. Historically, less attention was given to patient satisfaction compared to system utilization and conveniences of care providers. Recently, health care systems have started setting goals regarding patient satisfaction and improving the performance of the health system by providing timely and appropriate health care delivery. In this study we discuss methods for improving patient flow through outpatient clinics considering effective appointment scheduling policies by applying two-stage Stochastic Mixed-Integer Linear Program Model (two-stage SMILP) approaches. Goal is to improve the following patient flow metrics: direct wait time (clinic wait time) and indirect wait time considering patient’s no-show behavior, stochastic server, follow-up surgery appointments, and overbooking. The research seeks to develop two models: 1) a method to optimize the (weekly) scheduling pattern for individual providers that would be updated at regular intervals (e.g., quarterly or annually) based on the type and mix of services rendered and 2) a method for dynamically scheduling patients using the weekly scheduling pattern. Scheduling templates will entertain the possibility of arranging multiple appointments at once. The aim is to increase throughput per session while providing timely care, continuity of care, and overall patient satisfaction as well as equity of resource utilization. First, we use risk-neutral two-stage stochastic programming model where the objective function considers the expected value as a performance criterion in the selection of random variables like total waiting times and next, we expand the model formulation to mean-risk two-stage stochastic programming in which we investigate the effect of considering a risk measure in the model. We apply Conditional-Value-at-Risk (CVaR) as a risk measure for the two-stage stochastic programming model. Results from testing our models using data inspired by real-world OBGYN clinics suggest that the proposed formulations can improve patient satisfaction through reduced direct and indirect waiting times without compromising provider utilization

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

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

Mind the gap: a review of optimisation in mental healthcare service delivery

Well-planned care arrangements with effective distribution of available resources have the potential to address inefficiencies in mental health services. We begin by exploring the complexities associated with mental health and describe how these influence service delivery. We then conduct a scoping literature review of studies employing optimisation techniques that address service delivery issues in mental healthcare. Studies are classified based on criteria such as the type of planning decision addressed, the purpose of the study and care setting. We analyse the modelling methodologies used, objectives, constraints and model solutions. We find that the application of optimisation to mental healthcare is in its early stages compared to the rest of healthcare. Commonalities between mental healthcare service provision and other services are discussed, and the future research agenda is outlined. We find that the existing application of optimisation in specific healthcare settings can be transferred to mental healthcare. Also highlighted are opportunities for addressing specific issues faced by mental healthcare services

Flexible schedule optimization for human-robot collaboration

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 99-101).Robots are increasingly entering domains typically thought of as human-only. This convergence of human and robotic agents leads to a need for new technology to enable safe and efficient collaboration. The goal of this thesis is to develop a task allocation and scheduling algorithm for teams of robots working with or around teams of humans in intense domains where tight, fluid choreography of robotic schedules is required to guarantee the safety of all involved while maintaining high levels of productivity. Three algorithms are presented in this work: the Adaptive Preferences Algorithm, the Multi-Agent Optimization Algorithm, and Tercio. Tercio, the culminatory algorithm, is capable of assigning robots to tasks and producing near-optimal schedules for ten agents and hundreds of tasks in seconds while making guarantees about process specifications such as worker safety and deadline satisfaction. This work extends dynamic scheduling methods to incorporate flexible windows with an optimization framework featuring a mixed integer program and a satisficing hueristic scheduler. By making use of Tercio, a manufacturing facility or other high-intensity domain may fluidly command a team of robots to complete tasks in a quick, efficient manner while maintaining an ability to respond seamlessly to disturbances at execution. This greatly increases both productivity, by decreasing the time spent recompiling solutions, and responsiveness to humans in the area. These improvements in performance are displayed with multiple live demonstrations and simulations of teams of robots responding to disturbances. Tercio acts as an enabling step towards the ultimate goal of fully coordinated factories of dozens to hundreds of robots accomplishing many thousands of tasks in a safe, predictable, efficient manner.by Ronald J. Wilcox.S.M

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

Modélisation et simulation de la trajectoire des patients en radiothérapie

RÉSUMÉ : Modélisation et Simulation de la trajectoire des patients en radiothérapie - De nombreux traitements existent afin de lutter contre le cancer. Parmi les plus répandus, nous notons la chirurgie, la chimiothérapie et la radiothérapie. Cette dernière est considérée comme une référence étant donné que près de la moitié des patients ont passé par une phase de radiothérapie seule ou combinée durant leur régime de traitement. Le processus complet associé à cette technique implique une organisation complexe du point de vue des ressources et des flux des patients. Les réels enjeux d’une optimisation de ces processus sont multiples. En ce qui concerne les patients, l’enjeu majeur concerne la qualité des soins qui leur sont apportés et la minimisation des délais d’attente. En effet, diminuer ou éliminer les temps d’attente avant l’accès aux traitements signifie une meilleure qualité de service rendu au patient, mais également, une augmentation du nombre des patients pris en charge, et par conséquent, un accroissement de l’efficience de fonctionnement du centre de traitement. Depuis quelques dizaines d’années, les méthodes de recherche opérationnelle et les techniques de l’ingénierie industrielle font leur apparition dans le milieu hospitalier afin d’améliorer la performance de ces systèmes opératoires. En particulier, la radiothérapie présente une problématique intéressante liée à la conjonction de la rareté des ressources et à la complexité de la synchronisation de la trajectoire des soins. La résolution de ces problèmes nécessite une adoption de bonnes pratiques assurant d’une part, l’optimisation de la trajectoire des patients et d’autre part l’utilisation efficace des ressources disponibles. Dans ce contexte, les cliniques se sont retournées vers les outils d’aide à la décision. Parmi ces derniers, notons un outil permettant de modéliser toute la trajectoire des patients et comparer plusieurs politiques de gestion tout en étudiant leur impact sur la performance globale du système. Nous abordons cette question en débutant par une revue de la littérature sur la modélisation, la simulation et la planification en radiothérapie. Bien que cette étude se révèle limitée dans le contexte du processus complet, plusieurs approches ont été élaborées afin de traiter des parties du processus. En outre, elles sont appliquées à un cas spécifique lié à un seul centre de traitement. Dans cette optique, vient l’idée de ce projet ayant comme objectif le développement d’un modèle de simulation générique. Dans la première partie de ce travail, confronté aux limites du cadre de modélisation utilisé jusqu’aujourd’hui, nous avons développé une modélisation originale de la trajectoire des patients permettant une représentation plus fidèle de la réalité au niveau des flux physiques des patients, des ressources, des tâches, etc. En particulier, un langage de modélisation de processus normalisé (BMPN) a été utilisé pour élaborer une cartographie permettant de mieux comprendre le parcours d’un patient et sa trajectoire. Dans la deuxième partie, cette cartographie a été reproduite avec le langage de programmation Java et implémenté dans le modèle de simulation afin de pouvoir évaluer plusieurs nouvelles stratégies de gestion. Le simulateur obtenu nous a permis de modéliser et simuler la trajectoire des patients ainsi que leurs interactions avec les ressources tout en conservant la qualité de représentation de la réalité. En outre, il a permis de constater et d’évaluer l’impact de plusieurs scénarios par rapport aux différents indicateurs de performance. Finalement, la simulation du système radio-oncologique nous a abouti à prévoir des recommandations que nous avons jugé fiable, après la phase de validation, assurant l’amélioration de la qualité des soins apportés aux patients par la diminution des délais de prise en charge et le respect des temps d’attentes prescrits par la grille de classification. Mots-Clés : Modélisation, simulation, radiothérapie, trajectoires, stratégies et modèles de planification, ressources ----------ABSTRACT : Modeling and simulation of patient trajectory in radiotherapy centers - In the fight against cancer, major path are surgery, chemotherapy and radiotherapy. Radiation therapy is recognized as a reference since more than 50% of the patients received radiation during their treatment regimen. The treatment process associated with this technique involves a complex organization as regards as resources and patient flows. Concerning radiotherapy centers, they need to reduce treatment cost and manage resources utilization while keeping better quality of service. Regarding patients, major concern is quality of the care they receive and improvement of waiting time. In fact, reducing or eliminating waiting time before access to treatment means better quality of service to the patient, but also an increase in the number of treated patients, and consequently in the efficiency of treatment. Since several decades, operational research methods and industrial management techniques have appeared in healthcare. In particular, radiotherapy care trajectories are interesting because of the combination of critical resources and multiples care activity repetition. The resolution of these problems requires the adoption of good practices ensuring on the one hand the optimization of the trajectory of patients and on the other hand the efficient use of available resources. In this context, clinics have turned to decision-making tools. Among these, a tool that can model the entire patient trajectory and compare several management policies while studying their impact on the overall performance. We address this issue by starting with a review of the literature on modeling, simulation and planning in radiotherapy. Although several promising approaches have been developed, literature in this filed is scare. In fact, the majority of study reviewed handle only some phases of the process. In addition, they are applied to a specific case related to a single treatment center. In this perspective that this project was born with the objective of developing a generic simulation model. In the first part, we have developed an original model of the trajectory of patients allowing more accurate representation of reality of the processes. In particular, a standardized process modeling language (BMPN) was used to develop a precise mapping to better understand a patient's trajectory. In the second part, this mapping was reproduced and implemented in Java in order to evaluate several new management strategies. The simulator that we developed allowed us to simulate the trajectory of patients as well as their interactions with resources. In addition, it was possible to observe and evaluate the impact of several scenarios in terms of the objectives of centers, quantified using appropriate indicators such as waiting time. Finally, using the knowledge accumulated during the simulation of different scenarios, we made recommendations ensuring the improvement of quality of care given to patients by reducing delays and maximizing the uses of existing resources. Keywords: Modeling, simulation, radiotherapy, trajectories, planning strategies, resource