Operations Research for Health Care最新文献

Operating Room (OR) Scheduling is one of the most critical problems at the operational level for hospital managers. A useful strategy for OR scheduling, especially in large hospitals is the block strategy. In this strategy, a specific time is blocked for each surgeon or surgical team. This strategy usually leads to unused operating rooms’ capacity. To overcome this problem, in this article, a novel modified block strategy is presented for the daily scheduling of elective patients. This study aims to find the optimal sequence and schedule of patients by minimizing the cost of overtime, makespan and completion time of surgeons’ operations by considering the resource constraints. Considering the limitations and real conditions of Al-Zahra Hospital, the largest educational hospital in Isfahan, Iran, is also an aspect of this study. The problem is modeled by mixed integer programming and Constraint Programming (CP). The performance of the models is verified by several random test instances. The results indicate that CP is more efficient than mathematical modeling in terms of the computational time for solving the considered problems, especially for large-size instances. The average percent of improvement in computational time is about 53% using the CP model. The proposed CP model is also used to solve real problem instances from Al-Zahra hospital. The results show that by using the CP model, the completion time of surgeons’ operations is shortened by 9% and ORs’ overtime and makespan objectives are reduced by 55% and 20% respectively.

Creating shift schedules for medical residents is challenging, not only because of the large number of conflicting rules and requirements needed to ensure both adequate patient care and resident educational opportunities, but also because there is no one clear, well-defined single objective function to optimize. Instead, many factors should be taken into account when selecting the “best” schedule. In our practical experience, it is impossible for the scheduler (typically, a Chief Resident) to accurately determine weights that would allow these factors to be captured in a mathematical objective function that truly represented their preferences. We therefore propose to instead provide the Chief with a set of Pareto-dominant schedules from which to select. We present an integer programming-based approach embedded within a recursive algorithm to generate these schedules. We then present both computational results to assess the tractability of our approach and a case study, based on a real-world scheduling problem at the University of Michigan Pediatric Emergency Department, to study how a Chief Resident would evaluate the Pareto set.

Recommendations made by the UK’s National Institute for Health and Care Excellence (NICE) consider a range of relevant factors. Most famously, this includes interventions’ incremental cost-effectiveness ratios (ICER). Given the ICER’s primacy in such decision-making, it is sometimes assumed as almost analogous to an optimisation problem, maximising the number of Quality Adjusted Life Years generated by the health system subject to costs. However, structured OR techniques could still prove beneficial in informing the broader decision-making problem. Decisions are currently arrived at by advisory committees through a combination of structured processes and relatively unstructured deliberations. In principle, decision makers are expected to consider dozens of relevant factors after the completion of the economic modelling stage. No model is currently used to combine these, and MCDA may be suitable to better structure and aid these discussions and to highlight the opportunity costs associated with them. This paper outlines some of the factors currently considered in public health settings, proposes a number of approaches as to how MCDA-inspired techniques could be grafted onto current NICE processes incrementally, and considers the appropriateness of their use in this setting given NICE’s role in the health system. The paper focuses on the formulation of NICE’s public health guidance, as this area has a specific focus on equity and the determinants of health, and is therefore has the most obvious need to balance ICERs and other factors.

In this paper, we present a multitiered competitive supply chain network model for the blood banking industry, with a focus on the United States, that captures the economic interactions between three tiers of stakeholders; namely, the blood service organizations, the hospitals or medical centers, which transfuse blood to patients, and the payer groups that patients belong to. In addition, the supply chain framework for this life-saving product includes the competition among blood service organizations and their various supply chain activities. We model the behavior of each category of stakeholder and use the theory of variational inequalities to derive the equilibrium conditions for the entire supply chain. Illustrative examples are provided, along with qualitative properties, followed by an algorithm, accompanied by convergence results, that is used to solve simulated numerical examples. Results from these examples demonstrate that such a model can be effectively used to determine the prices and blood pathways from blood service organizations to hospitals to payers.

Manufacturing and maintenance processes significantly benefit from effective and integrated Supply Chain Management (SCM). Recently, hospitals start to recognize the importance of these logistics initiatives to improve their operational performance while also maintaining high quality of patient care. Patient care processes are supported by a range of supply chain activities including inventory management and distribution of medical supplies to point-of-care locations. At the operating room for instance, the logistics staff’s goal is managing materials and information flows to have the requested materials at the right operating room at the right time, in the most efficient way. However, poor inventory management, lack of standardization and lack of coordination between departments complicate healthcare logistics processes, and hence result in many waste. Opportunities for efficiency gains in these logistics processes can be identified by measuring the performance of the internal supply chain. This paper presents a rigorously defined logistics performance measurement framework to evaluate the efficiency of logistics processes in operating rooms. The Analytic Network Process (ANP) is utilized as a popular Multi-Criteria Decision-Making (MCDM) technique to provide effective decision-support models. The proposed ANP-based framework is a first step towards measuring the performance of operating room supply chain processes by selecting and prioritizing logistics objectives and associated Key Performance Indicators (KPIs). Further research is required to validate the ANP framework by including multiple stakeholders’ preferences, as they may have conflicting views on performance definitions. The final goal of the framework is to support hospital logistics managers in making transparent and informed decisions to improve inventory and distribution policies in the operating room while considering all stakeholders’ preferences.

Intensity-modulated radiation therapy (IMRT) planning is an inherently multi-criteria task. A multi-criteria workflow (MCW) typically passes the following steps: create an optimisation model with multiple criteria, approximate the Pareto frontier, and visualise the generated plans to the decision-maker (DM) for inspection. This interactive plan selection and manipulation allow to create better treatment plans as judged by physicians. However, once an optimisation model is specified, optimisation objectives cannot be modified any more. Thus this fixed model implies that a planner has to guess an appropriate model to begin with. Only after Pareto frontier approximation is calculated, the planner can assess the goodness of the model by exploring the trade-offs. The shortcoming of a MCW becomes apparent when the proposed model fails to generate expected trade-offs and the planner is thus forced to refine the model and repeat the calculations. To circumvent this drawback in the MCW, we propose a local multi-criteria workflow (L-MCW) designed and implemented in a collaboration between Fraunhofer ITWM and Varian Medical Systems. L-MCW enables local exploration around an initial, promising plan. The initial plan is automatically inferred by a knowledge-based algorithm (RapidPlan™). The decision-maker can thus evaluate trade-offs in the most interesting region surrounding the initial plan. Clinical results of the combination of knowledge-based planning and L-MCW with a cohort of Prostate and stereotactic ablative radiotherapy (SABR) Lung cases demonstrate substantially reduced planning time and improved organ-at-risk sparing compared to manual planning. The L-MCW provides an intuitive and flexible mechanism to adapt knowledge-based-planning models to similar, but not identical clinical situations and allows the practitioner to quickly determine and realise the most beneficial trade-offs in a treatment plan.

This paper experimentally investigates the relationships among three major stakeholders that are involved in drug inventory management at Intensive Care Units (ICUs), namely: i) nurses, who in person manage drug orders and carry out storage operations, ii) clinicians, who choose the therapy and shape demand, and iii) the hospital management, who is in charge of the economic sustainability of the hospital. As a case study, we consider the ICU ward of a major Italian public hospital and we focus on antibiotics. We exploit a previously developed Mixed Integer Linear Programming model which decides, for each drug, when and how much to order, and we improve it by adding different sets of constraints to represent each stakeholders’ point of view. By solving three generalized models, each of which ties the satisfaction of a single stakeholder to different thresholds, we explore the mutual effects of taking explicitly into account different perspectives within the inventory policy. We implemented an instance generator, built on the basis of empirical probability distributions extracted from a large set of observed historical data and representing the decision flow ruling drugs prescription. Extensive experiments have been carried out on a set of realistic instances provided by the generator. Results based on our test case not only provide computational evidence to intuitive relations among stakeholders, but also suggest possible levels of compromise. Improved stakeholder satisfaction would also benefit the patient, the passive stakeholder who is the ultimate subject of the caring process.

Improving the quality of healthcare in emergency departments (EDs) is at the forefront of many hospital managers’ efforts, as they strive to plan and implement better patient flow strategies. In this paper, a new approach to manage the patient flow in EDs after triage is proposed. The new queue discipline, named accumulative priority queue with finite horizon and denoted by APQ-h, is an extension of the accumulative priority queue (APQ) discipline that considers not only the acuity level of patients and their waiting time but also the stage of the healthcare treatment. APQ disciplines have been studied in the literature from a queueing theory point of view, which requires assumptions rarely found in real EDs, such as homogeneity in the patient arrival pattern and only one service stage. The APQ-h discipline accumulates priority from the point of waiting for the first physician consultation until the moment the waiting time exceeds the upper time limit set to access the physician after the patient’s arrival. A recent study shows that a management strategy of this type is applied in practice in several Canadian EDs. The main aim of this paper is to explore the implementation of APQ-h managing policies in a real ED. For this purpose, a simulation model replicating a real ED is developed. This simulation model is also used to obtain the optimal APQ type polices through a simulation-based optimization method that solves a multi-objective and stochastic optimization problem. Arrival to provider time and total waiting time in the ED are considered to be the key ED performance indicators. An extensive computational analysis shows the flexibility of the APQ-h and APQ discipline and their superiority over other pure priority disciplines in a real setting and in a variety of ED scenarios. In addition, no superiority over the APQ discipline is demonstrated.

Emergency Department (ED) overcrowding is a public health issue associated with harmful effects simultaneously on patients and ED staff. Despite increased policies and efforts to manage this issue, it continues to rise in many EDs all over the world. ED overcrowding is not caused only by the high number of incoming patients and resources shortage, the most affecting factor leading to such problem is the inpatient boarding. In fact, the patient has to wait too long for an available hospital bed. This paper suggests a new approach to improve the inpatient flow using Multi-Criteria Decision Making (MCDM) methods. The aim is to make a rational choice of the appropriate department in the ward to which the inpatient can be assigned even if the department related to its pathology is already crowded. The Analytic Hierarchy Process (AHP) based Delphi is used to collect data. Then, the AHP method is used to determine the weights of criteria that have an impact on the assignment decision. Finally, Elimination and Choice Expressing Reality (ELECTRE) II, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE) II are applied separately to rank the possible inpatient departments in ward in decreasing order of suitability to patient’s pathology. The provided approach is tested to the ED of Habib Bourguiba University hospital of Sfax, Tunisia where the aggregation of AHP-Delphi and TOPSIS is considered.