Improving patient access in oncology clinics using simulation

Purpose: Providing timely access is an important measure of patient satisfaction in specialty care clinicssuch as cancer centers. Excessive patient wait time to see an oncologist is very critical for cancer patients asthey often benefit from starting the treatment process as soon as possible. This paper addresses capacityplanning for both new and returning patients in cancer clinics. This research is motivated by a cancercenter in Texas that seeks to improve its clinical performance to decrease new patient wait time to see anoncologist.Design/methodology/approach: A simulation model is proposed to assess new patient access tooncologists when employing several tactical and operational policies such as resource flexibility,specialization flexibility, and reserving slots for new patients. The model utilizes two years of data collectedfrom a cancer center in Texas.Findings:The results suggest the best combination of operating policies in order to allocate patientdemand to providers. This study also determines the required capacity level to provide timely access fornew patients.Originality/value: Although the literature in outpatient scheduling and capacity planning is rich, newpatient access in oncology clinics has received limited attention. The few existing studies do not considerpatient no-shows and cancellations, and to the best of our knowledge, no study addresses individualoncologist clinic flexibility and the idea of reserving slots for new patientsPeer Reviewe

Evaluation of Patient Throughput in an Outpatient Pediatric Hematology, Oncology, and Bone Marrow Transplant Clinic

Background: Outpatient oncology clinics are complex environments. The multi-step, sequential nature of oncology treatment contributes to delays. Prolonged wait time impacts patient compliance, satisfaction, and staff satisfaction. Objectives: To assess throughput in the outpatient pediatric oncology clinic and explore staff’s assessment of throughput and their opinions of what might be improved. Methods: Our descriptive-comparative study used retrospective reviews to measure four time intervals for 312 visits at our mid-Atlantic outpatient clinic. Patient and appointment factors were explored. Mean interval times were calculated and differences impacting throughput were analyzed using ANOVA. Prospective survey data were obtained from 22 clinic staff and themes were identified. Results: The shortest interval was check-in to triage (18.49 ± 18.21 minutes) while the longest was from receiving laboratory results to treatment initiation (136.73 ± 77.98 minutes). Throughput was significantly shorter for appointments consisting of provider visit and laboratory studies only compared to visits involving infusions and blood product transfusions (p \u3c .001). Throughput for 8:00-10:00 a.m. appointments was significantly longer than 2:01-6:00 p.m. appointments (p = .013). Staff respondents reported throughput was suboptimal. Common problems identified were appointment noncompliance, laboratory workflow, triage and front desk bottlenecks, physician timeliness, fellow workflow, and “saving seats”. Conclusions: Delays occurred at each clinic intersection but were significantly longer with early clinic appointments and infusion and transfusion visits. Staff highlighted problems at each clinic juncture and overarching problems that caused inefficiencies. We identified priority areas to be addressed via targeted interventions in a structured action plan to improve clinic efficiency and throughput

Planification des calendriers des rendez-vous des patients en chimiothérapie et du niveau de ressources infirmières

RÉSUMÉ : Les problèmes de planification et de confection des horaires en chimiothérapie étaient anciennement résolus à l’aide de gabarits. La formalisation mathématique du problème est relativement nouvelle. Elle consiste à résoudre dans un premier temps le problème de confection des calendriers des rendez-vous des patients pour ensuite utiliser la solution comme donnée d’entrée afin de résoudre le problème d’affectation patients-infirmier. Le problème général sélectionne en première phase les patients en considérant la capacité de la clinique, il permet également d’estimer le besoin en ressources. La seconde phase consiste à résoudre le problème d’ordonnancement quotidien en affectant les patients aux infirmiers. Le modèle développé dans le cadre de ce projet est une extension du travail de [1] qui affecte des rendez-vous aux traitements des nouveaux patients sans perturber les décisions précédentes. La première phase est modifiée de manière à résoudre le problème de planification et à répondre à une partie des questions opérationnelles ; elle attribue non seulement la date, mais également l’heure de début des rendez-vous. Le début des traitements sont fixés à la même heure d’une fois à l’autre pour chaque patient. La première phase considère en plus de la charge horaire, la charge quotidienne ainsi que la lourdeur des patients. L’objectif de cette phase consiste à maximiser le nombre de rendez-vous planifiés en prenant en compte leur niveau de priorité des patients, la capacité des ressources humaines à absorber la charge et la capacité horaire des ressources matériels (chaises/lits). Cette phase, permet donc de planifier en amont le besoin en infirmiers et à prévoir le nombre de ressources supplémentaires nécessaire afin de couvrir la demande assignée à la liste d’attente. La liste d’attente agit tel une ressource tampon pour le problème de planification. Cette modélisation permet de mieux contrôler la quantité de travail affectée à une journée. De plus, la surestimation de la charge peut être considérée tel un avantage, étant donné qu’elle permet de pallier aux annulations de rendez-vous. En chimiothérapie, les patients doivent faire une prise de sang et/ou consulter leur oncologiste avant chaque administration de traitement, suite à ces visites, leurs rendez vous peuvent soient être annulés, reportés ou remplacés par un autre protocole médical. Le taux d’annulation varie de 4% à 20% avec une moyenne de 13%. La deuxième phase du modèle résout le problème d’ordonnancement quotidien pour la liste finale des patients et des infirmiers. Durant cette phase, la charge est distribuée équitablement avec comme but de minimiser l’écart à la charge maximale. Nous avons testé le modèle proposé à l’aide de 10 instances générées à partir des donnés collecter auprès de la clinique d’oncologie du Centre Hospitalier de l’Université de Montréal (CHUM). La clinique accueille en moyenne 70±12 patients par jour, ceci correspond à 246±24 heures de traitements. Le CHUM emploie 19 infirmiers, l’infirmière en charge assigne généralement entre 8 et 12 infirmiers à la zone d’infusion. Les autres membres du personnel sont assignés à différentes zones telle que le centre de support pour la chimiothérapie à domicile. L’infirmière en charge réaffecte les postes aux infirmiers, au courant de la journée en fonction de la charge de travail. L’optimisation permet de mieux gérer l’utilisation des ressources infirmières. Le modèle permet d’améliorer le ratio de productivité des infirmiers de 3% à 23% selon le scénario testé. En effet, au total, trois règles d’ordonnancement sont évaluées pour la deuxième phase : (1) interdiction de modifier l’heure de début des rendez-vous (3% de potentiel d’amélioration), (2) perturbation partielle de l’heure du début (8% de potentiel d’amélioration) et (3) perturbation totale (23% de potentiel d’amélioration). La résolution du modèle mathématique permet également aux patients de voir leur cas géré dans de meilleur délai, ainsi le nombre total moyen de patients en retard diminue de 9,9 à 5,5 pour l’ensemble de la période de planification.----------ABSTRACT : Planning and scheduling problems in chemotherapy were formerly solved using templates. The mathematical formalization of the problem is relatively new; it consists of first solving the planning problem and then using the solution as input to address the daily patientsnurses assignment problem. The model was proposed initially by Turkcan et al. [1] to assign a date to new patients’ treatments without changing past decisions. The general problem selects in the first phase the patients by considering the capacity of the clinic. The second phase assigns patients to nurses. The model developed as part of this project is an extension of [1] work. The first phase is modified to solve the planning problem and to answer some of the operational questions; it attributes the date and the starting time of patients’ treatments. The starting time of each treatment is fixed at the same time from one appointment to the next. The procedure considers in addition to the hourly workload, the daily workload as well as the heaviness of the patients. The objective of this phase is to maximize the number of patients by taking into account their priority level, the capacity of the medical staff to absorb the workload and the hourly capacity of the material resources chairs/beds). This phase,therefore, makes it possible to plan the need for nurses upstream and to plan the number of additional resources needed to cover the demand assigned to the waiting list. The second phase of the model solves the problem of daily patients-nurse assignment for the final list of patients. The workload is distributed with the aim to minimize the level of resources needed. Thus, the originality of this project lies in the representation of a waiting list that acts as a buffer. This modelization allows controlling the amount of work assigned to a day. Moreover, the overestimation of the workload can be considered as an advantage, since it makes it possible to compensate for cancellations. The cancellation rate varies from 4% to 20% with an average of 13%. Optimization allows patients to see their case managed in a shorter period depending on their state of health. For example, the total average of patients starting their treatments late decreases from 9,9 to 5,5. The clinic also has the potential to reduce inefficiencies due to poor planning; the model can improve the productivity ratio from 3 % to 23 % depending on the scenario tested. Indeed, in total, three scheduling rules are evaluated for the second phase: (1) prohibition the change of starting time (3% improvement), (2) partial shuffling of the starting time (8 %) and (3) total shuffling (23%). The resolution of the mathematical model thus improves the process of patients’ allocation and the clinical productivity ratio

A two-stage stochastic integer programming approach to integrated staffing and scheduling with application to nurse management

We study the problem of integrated staffing and scheduling under demand uncertainty. This problem is formulated as a two-stage stochastic integer program with mixed-integer recourse. The here-and-now decision is to find initial staffing levels and schedules. The wait-and-see decision is to adjust these schedules at a time closer to the actual date of demand realization. We show that the mixed-integer rounding inequalities for the second-stage problem convexify the recourse function. As a result, we present a tight formulation that describes the convex hull of feasible solutions in the second stage. We develop a modified multicut approach in an integer L-shaped algorithm with a prioritized branching strategy. We generate twenty instances (each with more than 1.3 million integer and 4 billion continuous variables) of the staffing and scheduling problem using 3.5 years of patient volume data from Northwestern Memorial Hospital. Computational results show that the efficiency gained from the convexification of the recourse function is further enhanced by our modifications to the L-shaped method. The results also show that compared with a deterministic model, the two-stage stochastic model leads to a significant cost savings. The cost savings increase with mean absolute percentage errors in the patient volume forecast

Simulation and optimization techniques applied in semiconductor assembly and test operations

The importance of back-end operations in semiconductor manufacturing has been growing steadily in the face of higher customer expectations and stronger competition in the industry. In order to achieve low cycle times, high throughput, and high utilization while improving due-date performance, more effective tools are needed to support machine setup and lot dispatching decisions. In previous work, the problem of maximizing the weighted throughput of lots undergoing assembly and test (AT), while ensuring that critical lots are given priority, was investigated and a greedy randomized adaptive search procedure (GRASP) developed to find solutions. Optimization techniques have long been used for scheduling manufacturing operations on a daily basis. Solutions provide a prescription for machine setups and job processing over a finite the planning horizon. In contrast, simulation provides more detail but in a normative sense. It tells you how the system will evolve in real time for a given demand, a given set of resources and rules for using them. A simulation model can also accommodate changeovers, initial setups and multi-pass requirements easily. The first part of the research is to show how the results of an optimization model can be integrated with the decisions made within a simulation model. The problem addressed is defined in terms of four hierarchical objectives: minimize the weighted sum of key device shortages, maximize weighted throughput, minimize the number of machines used, and minimize makespan for a given set of lots in queue, and a set of resources that includes machines and tooling. The facility can be viewed as a reentrant flow shop. The basic simulation was written in AutoSched AP (ASAP) and then enhanced with the help of customization features available in the software. Several new dispatch rules were developed. Rule_First_setup is able to initialize the simulation with the setups obtained with the GRASP. Rule_All_setups enables a machine to select the setup provided by the optimization solution whenever a decision is about to be made on which setup to choose subsequent to the initial setup. Rule_Hotlot was also proposed to prioritize the processing of the hot lots that contain key devices. The objective of the second part of the research is to design and implement heuristics within the simulation model to schedule back-end operations in a semiconductor AT facility. Rule_Setupnum lets the machines determine which key device to process according to a machine setup frequency table constructed from the GRASP solution. GRASP_asap embeds a more robust selection features of GRASP in the ASAP model through customization. This allows ASAP to explore a larger portion of the feasible region at each decision point by randomizing machine setups using adaptive probability distributions that are a function of solution quality. Rule_Greedy, which is a simplification of GRASP_asap, always picks the setup for a particular machine that gives the greatest marginal improvement in the objective function among all candidates. The purpose of the third part of the research is to statistically validate the relative effectiveness of our top six dispatch rules by comparing their performance on 30 real and randomly generated data sets. Using both GRASP and our ASAP discrete event simulation model, we have (1) identified the general order of dispatch rule performance, (2) investigated the impact of having setups installed on machines at time zero on rule performance, (3) determined the conditions under which restricting the maximum number of changeover affects the rule performance, and (4) studied the factors that might simultaneously affect rule performance with the help of a common random numbers experimental design. In the analysis, the first two objectives, weighted key device shortages and weighted throughput, are used to measure outcomes.Operations Research and Industrial Engineerin

Chemotherapy Outpatient Scheduling at the Segal Cancer Center Using Mixed Integer Programming Models

Appointment scheduling in an outpatient oncology clinic is a daunting task due to the stochastic and dynamic nature of the appointment requests. Each patient has a different trajectory and varying requirements of appointment length time that differ from one another. It is not possible to predict the amount of time required nor the amount of patients that will be treated in a day. Due to the oncologist's prescribed regimen, there is almost no flexibility to choose an appointment date because of the strict resting period required between treatments to achieve the best curative outcome. The purpose of this thesis is to demonstrate the benefit of using integer programming to model and to solve some of the challenges faced by the Segal Cancer Center of the Jewish General Hospital in Montreal, Quebec, when designing appointment schedules. We study two scheduling problems. The chemotherapy outpatient scheduling problem determines the allocation of patient appointment to days and the determination of appointment start time on those days for a planning horizon of four weeks. The objectives are to maximize the adherence to protocol, maximize the proper assignment of primary nurses to patients and minimize the completion date of treatments. With this model, the clinic can schedule appointment requests as they arise. When taking an integrated approach to solve the oncology clinic multi-stage scheduling problem, it is possible to coordinate the clinic's departments and determine the start time of each activity required by patients no matter their trajectory through the system. Due to the minimization of patient wait time and the completion time of their visit, there will be a better coordination within the clinic, reduction of staff idle time and a balance of resource utilization. Most importantly, it will ensure the completion of tasks within a single day, eliminating the current two-days scheduling policy of the Segal Cancer Center. The findings of this thesis will facilitate decision making in healthcare scheduling, improve the service level of oncology clinics and serve as a workforce management tool

Operations Research Frameworks for Improving Make-Ahead Drug Policies at Outpatient Chemotherapy Infusion Centers

Outpatient chemotherapy infusion is one of the most common forms of treatment used to cure, control, and ease symptoms of cancer. Patients who require outpatient chemotherapy infusion undergo lengthy and physically demanding infusion sessions over the course of their treatment. While the frequency and duration of visits vary by patient, drug, and cancer type, most patients will require several treatments over the course of months or years to complete their regimen/treat their disease. Receiving infusion is just one part of the complex treatment process. Patients may have their blood work done, wait for the results to process, visit their oncologist, wait on their order to be placed by their oncologist and prepared by the pharmacy, and then have the infusion administered by infusion clinic staff. Each step introduces randomness which can lead to propagated delays. These delays negatively affect patients as well as clinical operation cost and staff workload. We focus on optimizing drug preparation at the pharmacy to reduce patient delays. Drugs can be prepared the morning before patients arrive to prevent the patient from waiting the additional time needed to prepare their prescribed drugs in addition to any other wait time incurred during peak pharmacy hours. However, patients scheduled for outpatient chemotherapy infusion sometimes may need to cancel at the last minute even after arriving for their appointment (i.e. patient may be deemed too ill to receive treatment). This results in the health system incurring waste cost if the drug was made ahead since the drugs are patient specific and have a short shelf life. Infusion centers must implement policies to balance this potential waste cost with the time savings for their patients and staff. In support of this effort, this dissertation focuses on methods and strategies to improve the process flow of chemotherapy infusion outpatients by optimizing pharmacy make-ahead policies. We propose using three different methods which build upon each other. First we develop a predictive model which utilizes patient-specific data to estimate the probability that a patient will defer or not show for treatment on a given day. Generally, the ability to generate high-quality predictions of patient deferrals can be highly valuable in managing clinical operations, such as scheduling patients, determining which drugs to make before patients arrive, and establishing the proper staffing for a given day. We also introduce how the patient-specific probability of deferral can help determine a ``general rule of thumb" policy for what should be made ahead on a given day. Next we utilize these probabilities in two integer programming models. These multi-criteria optimization models prioritize which and how many drugs to make ahead given a fixed window of time. This is done with the dual objectives of reducing the expected waste cost as well as the expected value of reduced patient waiting time. Lastly, we utilize simulation to better quantify the impact of our proposed policies. We show that making chemotherapy drugs ahead of an infusion appointment not only benefit the patient they are prescribed for but also subsequent patients due to the decrease load (i.e., reduced blocking) on the pharmacy system as a whole. Each method utilizes electronic medical record data from the University of Michigan Rogel Cancer Center (UMRCC) but may be generalized to any cancer center infusion clinic.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/151706/1/donalric_1.pdfDescription of donalric_1.pdf : Restricted to UM users only

Solving Multi-objective Integer Programs using Convex Preference Cones

Esta encuesta tiene dos objetivos: en primer lugar, identificar a los individuos que fueron víctimas de algún tipo de delito y la manera en que ocurrió el mismo. En segundo lugar, medir la eficacia de las distintas autoridades competentes una vez que los individuos denunciaron el delito que sufrieron. Adicionalmente la ENVEI busca indagar las percepciones que los ciudadanos tienen sobre las instituciones de justicia y el estado de derecho en Méxic