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

A <i>P</i>- and <i>T</i>-invariant characterization of product form and decomposition in stochastic Petri nets

Structural product form and decomposition results for stochastic Petri nets are surveyed,unifed and extended. The contribution is threefold. First, the literature on structural results for product form over the number of tokens at the places is surveyed and rephrased completely in terms of T-invariants. Second, based on the underlying concept of group-local-balance, the product form results for stochastic Petri nets are demarcated and an intuitive explanation is provided of these results based on T-invariants, only. Third, a decomposition result is provided that is completely formulated in terms of both T-invariants and P-invariants

Structural characterization of decomposition in rate-insensitive stochastic Petri nets

This paper focuses on stochastic Petri nets that have an equilibrium distribution that is a product form over the number of tokens at the places. We formulate a decomposition result for the class of nets that have a product form solution irrespective of the values of the transition rates. These nets where algebraically characterized by Haddad et al.~as $S\Pi^2$ nets. By providing an intuitive interpretation of this algebraical characterization, and associating state machines to sets of $T$-invariants, we obtain a one-to-one correspondence between the marking of the original places and the places of the added state machines. This enables us to show that the subclass of stochastic Petri nets under study can be decomposed into subnets that are identified by sets of its $T$-invariants

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

Organizing multidisciplinary care for children with neuromuscular diseases at the Academic Medical Center, Amsterdam:CASE STUDY

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. The patients with such diseases 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 used to visit 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 paper describes how quantitative modelling supports the design and operations of the CMCA. First, an integer linear program is presented that selects which patients are to be invited 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 study to estimate the capacity of the CMCA, expressed in terms of 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. Its contribution on the case under study is twofold. First, we design highly constrained appointment schedules for multiple patients that require service from multiple disciplines’ resources. Second, we study the effect of the trade-offs between scheduling constraints and access times. As such, the contribution of this case study paper is that it illustrates the value of applying Operations Research techniques in complex healthcare settings, by designing context-specific combinations of mathematical models, thereby improving delivery of the highly-constrained multidisciplinary care

Designing cyclic appointment schedules for outpatient clinics with scheduled and unscheduled patient arrivals

We present a methodology to design appointment systems for outpatient clinics and diagnostic facilities that offer both walk-in and scheduled service. The developed blueprint for the appointment schedule prescribes the number of appointments to plan per day and the moment on the day to schedule the appointments. The method consists of two models that are linked by an algorithm; one for the day process that governs scheduled and unscheduled arrivals on the day and one for the access process of scheduled arrivals. Appointment schedules that balance the waiting time at the facility for unscheduled patients and the access time for scheduled patients, are calculated iteratively using the outcomes of the two models. The method is of general nature and can therefore also be applied to scheduling problems in other sectors than health care

OR and simulation in combination for optimization

This chapter aims to promote and illustrate the fruitful combination of classical Operations Research (OR) and Computer Simulation. First, a highly instructive example of parallel queues will be studied. This simple example already shows the necessary combination of OR (queueing) and simulation that appears to be of practical interest such as for call center optimization. Next, two more ’real life’ applications are regarded:\ud - blood platelet production and inventory management at blood banks, and \ud - train conflict resolution for railway junctions.\ud Both applications show the useful combination of Simulation and optimization methods from OR, in particular Stochastic Dynamic Programming (SDP) and Markov decision theory (MDP), to obtain simple rules that are nearly optimal. The results are based on real life Dutch case studies and show that this combined OR-Simulation approach can be most useful for ’practical optimization’ and that it is still wide open for further application