1,644 research outputs found

    Guidelines for Scheduling in Primary Care: An Empirically Driven Mathematical Programming Approach

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    Primary care practices play a vital role in healthcare delivery since they are the first point of contact for most patients, and provide health prevention, counseling, education, diagnosis and treatment. Practices, however, face a complex appointment scheduling problem because of the variety of patient conditions, the mix of appointment types, the uncertain service times with providers and non-provider staff (nurses/medical assistants), and no-show rates which all compound into a highly variable and unpredictable flow of patients. The end result is an imbalance between provider idle time and patient waiting time. To understand the realities of the scheduling problem we analyze empirical data collected from a family medicine practice in Massachusetts. We study the complete chronology of patient flow on nine different workdays and identify the main patient types and sources of inefficiency. Our findings include an easy-to-identify patient classification, and the need to focus on the effective coordination between nurse and provider steps. We incorporate these findings in an empirically driven stochastic integer programming model that optimizes appointment times and patient sequences given three well-differentiated appointment types. The model considers a session of consecutive appointments for a single-provider primary care practice where one nurse and one provider see the patients. We then extend the integer programming model to account for multiple resources, two nurses and two providers, since we have observed that such team primary care practices are common in the course of our data collection study. In these practices, nurses prepare patients for the providers’ appointments as a team, while providers are dedicated to their own patients to ensure continuity of care. Our analysis focuses on finding the value of nurse flexibility and understanding the interaction between the schedules of the two providers. The team practice leads us to a challenging and novel multi step multi-resource mixed integer stochastic scheduling formulation, as well as methods to tackle the ensuing computational challenge. We also develop an Excel scheduling tool for both single provider and team practices to explore the performance of different schedules in real time. Overall, the main objective of the dissertation is to provide easy-to-implement scheduling guidelines for primary care practices using both an empirically driven stochastic optimization model and a simulation tool

    A permutation flowshop model with time-lags and waiting time preferences of the patients

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    The permutation flowshop is a widely applied scheduling model. In many real-world applications of this model, a minimum and maximum time-lag must be considered between consecutive operations. We can apply this model to healthcare systems in which the minimum time-lag could be the transfer times, while the maximum time-lag could refer to the number of hours patients must wait. We have modeled a MILP and a constraint programming model and solved them using CPLEX to find exact solutions. Solution times for both methods are presented. We proposed two metaheuristic algorithms based on genetic algorithm and solved and compared them with each other. A sensitivity of analysis of how a change in minimum and maximum time-lags can impact waiting time and Cmax of the patients is performed. Results suggest that constraint programming is a more efficient method to find exact solutions and changes in the values of minimum and maximum time-lags can impact waiting times of the patients and Cmax significantly

    A Universal Appointment Rule with Patient Classification for Service Times, No-Shows and Walk-Ins

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    Integrated Planning in Hospitals: A Review

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    Efficient planning of scarce resources in hospitals is a challenging task for which a large variety of Operations Research and Management Science approaches have been developed since the 1950s. While efficient planning of single resources such as operating rooms, beds, or specific types of staff can already lead to enormous efficiency gains, integrated planning of several resources has been shown to hold even greater potential, and a large number of integrated planning approaches have been presented in the literature over the past decades. This paper provides the first literature review that focuses specifically on the Operations Research and Management Science literature related to integrated planning of different resources in hospitals. We collect the relevant literature and analyze it regarding different aspects such as uncertainty modeling and the use of real-life data. Several cross comparisons reveal interesting insights concerning, e.g., relations between the modeling and solution methods used and the practical implementation of the approaches developed. Moreover, we provide a high-level taxonomy for classifying different resource-focused integration approaches and point out gaps in the literature as well as promising directions for future research

    Disease diagnosis in smart healthcare: Innovation, technologies and applications

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    To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer’s disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed

    Optimization of duty cycles in magnetic resonance imaging systems

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    About 30 years ago the first commercial Magnetic Resonance Imaging (MRI) scanner was installed at the Hammersmith Hospital in London. This revolutionary technique made it possible to image tissues surrounded by bone. This was a big advantage in comparison to X-ray based imaging methods. However, resolution of the first magnetic resonance images was low and the scanning time was long, due to problems of weak signal and high sensitivity to the patient motion. Since then a lot of research has been done to improve the overall performance of the machines. In mid 90s fast imaging techniques were developed that had a tremendous impact on the popularity of MRI among other medical imaging methods. Nowadays there are a lot of clinical imaging applications where MRI overtakes the X-ray successors. Moreover, MRI is believed to be harmless to the patient, because no ionizing radiation is utilized. However, the main disadvantages of MRI are strong magnetic field, extreme expense, and relatively long examination time when compared to X-ray. The first factor imposes high safety standards that must be respected in an MRI scanner room, whereas the last two factors prevent hospitals from fast investments return. Moreover, due to high demand on MRI examinations, the patient waiting lists in hospitals are often several weeks long. This backlog decreases patient satisfaction. In this dissertation, a new approach to reduce the examination time of MRI systems is described. The time reduction is accomplished by dividing parts of the MRI examination into segments that are then intermixed. The intermixing algorithms are based on scheduling technique from the field of Operations Research. There are a number of physical parameters that restrict performance of MRI systems, such as temperature of MRI hardware during the examination. Also, due to electromagnetic effects inside the bore of MRI scanner, the temperature of patient’s body can get close to an uncomfortable level. In current practice, all these duty cycle limitations are modeled and verified before the MRI examination starts. Then, if necessary, the MRI examination time is prolongated, in order not to exceed the temperature limits. Typical MRI examination consists of several discrete parts, i.e., scans. Different types of scans impose different duty cycle limitations. The approach proposes that the examination can be divided into small segments that are rescheduled in such a way that the adverse effects of duty cycle limited scans are reduced by non-limited scans. In this thesis, several scheduling algorithms are described that were designed to deal with different kinds of duty cycle limitations and to improve performance of MRI systems. The algorithms were verified on a large number of MRI examinations. According to collected statistics, time of MRI examinations can be reduced by up to 22%. As a result, the capacity of one MRI system can be increased by up to 4 patients per day. Moreover, special MRI experiments were carried out to validate the algorithms. Finally, the thesis presents an approach to patient flow modeling in MRI departments in hospitals. The patient flow is modeled by means of queuing theory in order to uncover bottlenecks. Then, discrete-event computer simulations are performed to overcome limitations of the classical queuing theory assumptions. The current hospitals practice demonstrates that the MRI scanners are not always the bottleneck in the overall examinations workflow. The resulting models can be utilized to predict patient flow for various layouts of MRI departments and appointment scheduling strategies. Based on these detailed models, recommendations on improving MRI departments’ workflow can be derived. The results of this study can be used to optimize performance of MRI departments in hospitals or free-standing imaging centers. First, the MRI scanning time can be reduced. Second, the patient flow can be optimized that yields the overall MRI examination time reduction. This will result in better patient comfort and faster return on investments in MRI equipment. The research described in this thesis was carried out as a part of the DARWIN project at Philips Healthcare under the responsibilities of the Embedded Systems Institute (ESI). This project is partially supported by the Dutch Ministry of Economic Affairs under the BSIK program

    Optimization of Surgery Scheduling in Multiple Operating Rooms with Post Anesthesia Care Unit Capacity Constraints

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    Surgery schedules are subject to disruptions due to duration uncertainty in surgical activities, patient punctuality, surgery cancellation and surgical emergencies. Unavailable recovery resources, such as post-anesthesia care unit (PACU) beds may also cause deviations from the surgical schedule. Such disruptions may result in inefficient utilization of medical resources, suboptimal patient care and patient and staff dissatisfaction. To alleviate these adverse effects, we study three open challenges in the field of surgery scheduling. The case we study is in a surgical suite with multiple operating rooms (ORs) and a shared PACU. The overall objective is to minimize the expected cost incurred from patient waiting time, OR idle time, OR blocking time, OR overtime and PACU overtime.In the first part of this work, we study surgery scheduling with PACU capacity constraints. With surgery sequences predetermined in each OR, a discrete event dynamic system (DEDS) and a DEDS-based stochastic optimization model are devised for the problem. A sample-gradient-based algorithm is proposed for the sample average approximation of our formulation. Numerical experiments suggest that the proposed method identifies near-optimal solutions and outperforms previous methods. It is also shown that considerable cost savings (11.8% on average) are possible in hospitals where PACU beds are a constraint.In the second part, we propose a two-stage solution method for stochastic surgery sequencing and scheduling with PACU capacity constraints. In the first stage, we propose a mixed-integer programming model with a surrogate objective that is much easier to solve than the original problem. The Lagrangian relaxation of the surrogate model can be decomposed by patients into network-structured subproblems which can be efficiently solved by dynamic programming. The first-stage model is solved by the subgradient method to determine the surgery sequence in each OR. Given the surgery sequence, scheduled start times are determined in the second stage using the sample-gradient descent algorithm. Our solution method outperforms benchmark methods that are proposed in the literature by 11% to 43% in numerical experiments. Our sequencing method contributes 45% to 80% of the overall improvement. We also illustrate the improvement on PACU utilization after using our scheduling strategy. In the third part, we propose a proactive and reactive surgery scheduling method for surgery scheduling under surgical disruptions. A surgical schedule considering possible disruptions is constructed prior to the day of surgery, and is then adjusted dynamically in response to disruptions on the day of surgery. The proposed method is based on stochastic optimization and a sample-gradient descent algorithm, which is the first non-metaheuristic approach proposed for this problem. In addition, the to-follow scheduling policy, which is widely used in practice, is considered in this study. This differs from previous surgical scheduling studies which assume no surgery can start before its scheduled start time. The proposed method finds near-optimal solutions and outperforms the scheduling method commonly used in practice

    A STOCHASTIC APPROACH TO APPOINTMENT SEQUENCING

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    Ph.DDOCTOR OF PHILOSOPH

    Stochastic Optimization Approaches for Outpatient Appointment Scheduling under Uncertainty

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    Outpatient clinics (OPCs) are quickly growing as a central component of the healthcare system. OPCs offer a variety of medical services, with benefits such as avoiding inpatient hospitalization, improving patient safety, and reducing costs of care. However, they also introduce new challenges for appointment planning and scheduling, primarily due to the heterogeneity and variability in patient characteristics, multiple competing performance criteria, and the need to deliver care within a tight time window. Ignoring uncertainty, especially when designing appointment schedules, may have adverse outcomes such as patient delays and clinic overtime. Conversely, accounting for uncertainty when scheduling has the potential to create more efficient schedules that mitigate these adverse outcomes. However, many challenges arise when attempting to account for uncertainty in appointment scheduling problems. In this dissertation, we propose new stochastic optimization models and approaches to address some of these challenges. Specifically, we study three stochastic outpatient scheduling problems with broader applications within and outside of healthcare and propose models and methods for solving them. We first consider the problem of sequencing a set of outpatient procedures for a single provider (where each procedure has a known type and a random duration that follows a known probability distribution), minimizing a weighted sum of waiting, idle time, and overtime. We elaborate on the challenges of solving this complex stochastic, combinatorial, and multi-criteria optimization problem and propose a new stochastic mixed-integer programming model that overcomes these challenges in contrast to the existing models in the literature. In doing so, we show the art of, and the practical need for, good mathematical formulations in solving real-world scheduling problems. Second, we study a stochastic adaptive outpatient scheduling problem which incorporates the patients’ random arrival and service times. Finding a provably-optimal solution to this problem requires solving a MSMIP, which in turn must optimize a scheduling problem over each random arrival and service time for each stage. Given that this MSMIP is intractable, we present two approximation based on two-stage stochastic mixed-integer models and a Monte Carlo Optimization approach. In a series of numerical experiments, we demonstrate the near-optimality of the appointment order (AO) rescheduling policy, which requires that patients are served in the order of their scheduled appointments, in many parameter settings. We also identify parameter settings under which the AO policy is suboptimal. Accordingly, we propose an alternative swap-based policy that improves the solution of such instances. Finally, we consider the outpatient colonoscopy scheduling problem, recognizing the impact of pre-procedure bowel preparation (prep) quality on the variability of colonoscopy duration. Data from a large OPC indicates that colonoscopy durations are bimodal, i.e., depending on the prep quality they can follow two different probability distributions, one for those with adequate prep and the other for those with inadequate prep. We define a distributionally robust outpatient colonoscopy scheduling (DRCOS) problem that seeks optimal appointment sequence and schedule to minimize the worst-case weighted expected sum of patient waiting, provider idling, and provider overtime, where the worst-case is taken over an ambiguity set characterized through the known mean and support of the prep quality and durations. We derive an equivalent mixed-integer linear programming formulation to solve DRCOS. Finally, we present a case study based on extensive numerical experiments in which we draw several managerial insights into colonoscopy scheduling.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/151727/1/ksheha_1.pdfDescription of ksheha_1.pdf : Restricted to UM users only
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