Organizing Multidisciplinary Care for Children with Neuromuscular Diseases

The Academic Medical Center (AMC) in Amsterdam, The Netherlands, recently opened the `Children's Muscle Center Amsterdam' (CMCA). The CMCA diagnoses and treats children with neuromuscular diseases. These patients require care from a variety of clinicians. Through the establishment of the CMCA, children and their parents will generally visit the hospital only once a year, while previously they visited on average six times a year. This is a major improvement, because the hospital visits are both physically and psychologically demanding for the patients. This article describes how quantitative modelling supports the design and operations of the CMCA. First, an integer linear program is presented that selects which patients to invite for a treatment day and schedules the required combination of consultations, examinations and treatments on one day. Second, the integer linear program is used as input to a simulation to study to estimate the capacity of the CMCA, expressed in the distribution of the number patients that can be seen on one diagnosis day. Finally, a queueing model is formulated to predict the access time distributions based upon the simulation outcomes under various demand scenarios

Integral multidisciplinary rehabilitation treatment planning

This paper presents a methodology to plan treatments for rehabilitation outpatients. These patients require a series of treatments by therapists from various disciplines. In current practice, when treatments are planned, a lack of coordination between the different disciplines, along with a failure to plan the entire treatment plan at once, often occurs. This situation jeopardizes both the quality of care and the logistical performance. The multidisciplinary nature of the rehabilitation process complicates planning and control. An integral treatment planning methodology, based on an integer linear programming (ILP) formulation, ensures continuity of the rehabilitation process while simultaneously controlling seven performance indicators including access times, combination appointments, and therapist utilization. We apply our approach to the rehabilitation outpatient clinic of the Academic Medical Center (AMC) in Amsterdam, the Netherlands. Based on the results of this case, we are convinced that our approach can be valuable for decision-making support in resource capacity planning and control at many rehabilitation outpatient clinics. The developed model will be part of the new hospital information system of the AMC

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

Planning oncologists of ambulatory care units

International audienceThis paper addresses the problem of determining the work schedule, called medical planning, of oncologists for chemotherapy of oncology patients at ambulatory care units. A mixed integer programming (MIP) model is proposed for medical planning in order to best balance bed capacity requirements under capacity constraints of key resources such as beds and oncologists. The most salient feature of the MIP model is the explicit modeling of specific features of chemotherapy such as treatment protocols. The medical planning problem is proved to be NP-complete. A three-stage approach is proposed for determining good medical planning in reasonable computational time. From numerical experiments based on field data, the three-stage approach takes less than 10 min and always outperforms the direct application of MIP solvers with 10 h CPU time. Compared with the current planning, the three-stage approach reduces the peak daily bed capacity requirement by 20 h to 45 h while the maximum theoretical daily bed capacity is 162 h

Comparing Optimization Methods for Radiation Therapy Patient Scheduling using Different Objectives

Radiation therapy (RT) is one of the most common technologies used to treat cancer. To better use resources in RT, optimization models can be used to automatically create patient schedules, a task that today is done manually in almost all clinics. This paper presents a comprehensive study of different optimization methods for modeling and solving the RT patient scheduling problem. The results can be used as decision support when implementing an automatic scheduling algorithm in practice. We introduce an Integer Linear Programming (IP) model, a column generation IP model (CG-IP), and a Constraint Programming model. Patients are scheduled on multiple machine types considering their priority for treatment, session duration and allowed machines, while taking expected future patient arrivals into account. Different cancer centers may have different scheduling objectives, and therefore each model is solved using multiple different objective functions, including minimizing waiting times, and maximizing the fulfillment of patients' preferences for treatment times. The test data is generated from historical data from Iridium Netwerk, a large cancer center in Belgium with 10 linear accelerators. The results demonstrate that the CG-IP model can solve all the different problem instances to a mean optimality gap of less than 1% within one hour. The proposed methodology provides a tool for automated scheduling of RT treatments and can be generally applied to RT centers.Comment: 20 pages, 4 figures, Submitted to Operations Research Foru

Organizing timely treatment in multi-disciplinary care

Healthcare providers experience an increased pressure to organize their processes more efficiently and to provide coordinated care over multiple disciplines. Organizing multi-disciplinary care is typically highly constrained, since multiple appointments per patient have to be scheduled with possible restrictions between them. Furthermore, schedules of professionals from various facilities or with different skills must be aligned. Since it is important that patients are treated on time, access time targets are set on the time between referral to the facility and the actual start of the treatment. These targets may vary per patient type: e.g., urgent patients have shorter access time targets than regular patients. In this thesis, we use operations research methods to support multi-disciplinary care settings in providing timely treatments with an excellent quality of care, against affordable costs, while taking patient and employee satisfaction into account. We consider settings in rehabilitation care and radiotherapy, but the underlying planning problems are applicable to many other multi-disciplinary care settings, such as cancer care or specialty clinics. The developed models are applied to case studies in the Sint Maartenskliniek Nijmegen, the AMC Amsterdam and a BCCA cancer clinic in Vancouver, Canada. The results of the thesis demonstrate that adequate admission policies and capacity allocation to different activities and stages in complex treatment processes can improve compliance with access time targets for multi-disciplinary care systems considerably, while using the available resource capacities and taking patient and employee satisfaction into account

Operations Research Methods for Optimization in Radiation Oncology

Operations Research has a successful tradition of applying mathematical analysis to a wide range of applications, and problems in Medical Physics have been popular over the last couple of decades. The original application was in the optimal design of the uence map for a radiotherapy treatment, a problem that has continued to receive attention. However, Operations Research has been applied to other clinical problems like patient scheduling, vault design, and image alignment. The overriding theme of this article is to present how techniques in Operations Research apply to clinical problems, which we accomplish in three parts. First, we present the perspective from which an operations researcher addresses a clinical problem. Second, we succinctly introduce the underlying methods that are used to optimize a system, and third, we demonstrate how modern software facilitates problem design. Our discussion is supported by several publications to foster continued study. With numerous clinical, medical, and managerial decisions associated with a clinic, operations research has a promising future at improving how radiotherapy treatments are designed and delivered

Using the Sharp Operator for edge detection and nonlinear diffusion

In this paper we investigate the use of the sharp function known from functional analysis in image processing. The sharp function gives a measure of the variations of a function and can be used as an edge detector. We extend the classical notion of the sharp function for measuring anisotropic behaviour and give a fast anisotropic edge detection variant inspired by the sharp function. We show that these edge detection results are useful to steer isotropic and anisotropic nonlinear diffusion filters for image enhancement