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Planning out-of-hours services for pharmacies 规划药房的非工作时间服务
IF 2.1 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2020-12-01 DOI: 10.1016/j.orhc.2020.100277
Christina Büsing, Timo Gersing, Arie M.C.A. Koster

The supply of pharmaceuticals is one important factor in a functioning health care system. In the German health care system, the chambers of pharmacists are legally obliged to ensure that every resident can find an open pharmacy at any day and night time within an appropriate distance. To that end, the chambers of pharmacists create an out-of-hours plan for a whole year in which every pharmacy has to take over some 24 h shifts. These shifts are important for a reliable supply of pharmaceuticals in the case of an emergency but also unprofitable and stressful for the pharmacists. Therefore, an efficient planning that meets the needs of the residents and reduces the load of shifts on the pharmacists is crucial.

In this paper, we present a model for the assignment of out-of-hours services to pharmacies, which arises from a collaboration with the Chamber of Pharmacists North Rhine. Since the problem, which we formulate as an MILP, is very hard to solve for large-scale instances, we propose several tailored solution approaches. We aggregate mathematically equivalent pharmacies in order to reduce the size of the MILP and to break symmetries. Furthermore, we use a rolling horizon heuristic in which we decompose the planning horizon into a number of intervals on which we iteratively solve subproblems. The rolling horizon algorithm is also extended by an intermediate step in which we discard specific decisions made in the last iteration.

A case study based on real data reveals that our approaches provide nearly optimal solutions. The model is evaluated by a detailed analysis of the obtained out-of-hours plans.

药品供应是一个正常运转的卫生保健系统的一个重要因素。在德国的医疗保健系统中,药剂师协会有法律义务确保每个居民在任何时候都能在适当的距离内找到一家开放的药房。为此,药剂师协会制定了一项全年的非工作时间计划,其中每个药房都必须接受大约24小时轮班。这些轮班对紧急情况下可靠的药品供应很重要,但对药剂师来说也无利可图,而且压力很大。因此,一个有效的规划,满足居民的需求,减少轮班药剂师的负担是至关重要的。在这篇论文中,我们提出了一个模式的非工作时间的服务分配给药店,这是由与药剂师北莱茵商会合作产生的。由于我们将该问题表述为MILP,很难解决大规模实例的问题,因此我们提出了几种定制的解决方案。为了减小MILP的大小并打破对称性,我们聚合了数学上相等的药房。此外,我们使用滚动水平启发式方法,将规划水平分解为若干区间,在这些区间上迭代求解子问题。滚动地平线算法还通过中间步骤进行了扩展,在中间步骤中我们丢弃了上次迭代中做出的特定决策。基于真实数据的案例研究表明,我们的方法提供了近乎最优的解决方案。通过对得到的下班计划的详细分析,对模型进行了评价。
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引用次数: 1
Collaborative duty rostering in health care professions 卫生保健专业的协作值班
IF 2.1 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2020-12-01 DOI: 10.1016/j.orhc.2020.100278
Sandy Heydrich, Rasmus Schroeder , Sebastian Velten

For nurses the duty roster and its reliability has a significant impact on the compatibility of work and private life. In the research project GamOR (Game of Roster) ergonomists, designers and mathematicians cooperated with application partners to address this issue and developed a novel collaborative planning process for creating duty rosters.

The collaborative planning process consists of two parts. On the one hand, employees are informed about conflicts among their wishes for free time and are encouraged to solve these conflicts within the team. On the other hand, decision makers are supported in the creation of the final roster.

In this paper we present Constraint Programming (CP) approaches to support both of these parts. Based on a set of CP model components, which model for example staff requirements and legal regulations, we introduce a domain driven algorithm for detecting conflicts of wishes and argue that it outperforms approaches known from the literature. Moreover, we develop a backtracking search for generating complete rosters. This is done by appropriate variable and value selection strategies reflecting the objectives — balanced work time accounts, alternating free weekends, lengths of shift sequences (total and with the same shift definition) and forward rotation.

The presented approach was introduced for testing in various institutions and has been positively evaluated by both nurses and decision makers. Nurses particularly appreciate the transparency and timely feedback of conflicts. For decision makers, the time saved when creating the duty roster is a great benefit.

护士值班表及其可靠性对护士工作与生活的兼容性有显著影响。在GamOR(名册游戏)研究项目中,人类工效学家、设计师和数学家与应用伙伴合作解决了这个问题,并开发了一种新的协作规划流程来创建值班表。协同规划过程包括两个部分。一方面,员工被告知他们对自由时间的愿望之间存在冲突,并被鼓励在团队内部解决这些冲突。另一方面,在编制最后的名册时支持决策者。在本文中,我们提出了约束规划(CP)方法来支持这两个部分。基于一组CP模型组件(例如对员工需求和法律法规进行建模),我们引入了一种用于检测愿望冲突的领域驱动算法,并认为它优于文献中已知的方法。此外,我们开发了一个回溯搜索来生成完整的名单。这是通过适当的变量和价值选择策略来实现的,这些策略反映了目标——平衡的工作时间账户、交替的自由周末、轮班序列的长度(总数和相同的轮班定义)和向前轮换。所提出的方法已在各机构进行了测试,并得到了护士和决策者的积极评价。护士尤其欣赏冲突的透明度和及时反馈。对于决策者来说,在创建值班表时节省的时间是一个很大的好处。
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引用次数: 3
Safely learning Intensive Care Unit management by using a Management Flight Simulator 使用管理飞行模拟器安全学习重症监护病房管理
IF 2.1 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2020-12-01 DOI: 10.1016/j.orhc.2020.100274
Daniel Garcia-Vicuña , Laida Esparza , Fermin Mallor

This paper presents the development of the first Management Flight Simulator of an Intensive Care Unit (ICU). It allows analyzing the physician decision-making related to the admission and discharge of patients and it can be used as a learning–training tool. The discrete event simulation model developed mimics real admission and discharge processes in ICUs, and it recreates the health status of the patients by using real clinical data (instead of using a single value for the length of stay). This flexible tool, which allows recreating ICUs with different characteristics (number of beds, type of patients that arrive, congestion level...), has been used and validated by ICU physicians and nurses of four hospitals. We show through preliminary results the variability among physicians in the decision-making concerning the dilemma of the last bed, which is dealt in a broad sense: it is not only about how the last available ICU bed is assigned but also about how the physician makes decisions about the admission and discharge of patients as the ICU is getting full. The simulator is freely available on the internet to be used by any interested user (https://emi-sstcdapp.unavarra.es/ICU-simulator).

本文介绍了重症监护病房(ICU)的第一个管理飞行模拟器的发展。它允许分析与病人入院和出院相关的医生决策,它可以用作学习培训工具。所开发的离散事件模拟模型模拟了icu的真实入院和出院过程,并通过使用真实的临床数据(而不是使用单一的住院时间值)重新创建了患者的健康状态。这个灵活的工具允许重新创建具有不同特征(床位数量、到达的患者类型、拥堵程度……)的ICU,已被四家医院的ICU医生和护士使用并验证。我们通过初步结果展示了医生在决策中关于最后一张床的困境的可变性,这是在广义上处理的:它不仅是关于如何分配最后一个可用的ICU床位,而且关于医生如何在ICU满员时做出关于患者入院和出院的决定。模拟器可以在互联网上免费获得,任何感兴趣的用户都可以使用(https://emi-sstcdapp.unavarra.es/ICU-simulator)。
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引用次数: 3
The impact of oversampling with “ubSMOTE” on the performance of machine learning classifiers in prediction of catastrophic health expenditures 使用“ubSMOTE”进行过采样对机器学习分类器在预测灾难性医疗支出中的性能的影响
IF 2.1 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2020-12-01 DOI: 10.1016/j.orhc.2020.100275
Songul Cinaroglu

As a common problem in classification tasks, class imbalance degrades the performance of the classifier. Catastrophic out-of-pocket (OOP) health expenditure is a specific example of a rare event faced by very few households. The objective of the present study is to demonstrate a two-step learning approach for modeling highly unbalanced catastrophic OOP health expenditure data. The data are retrieved from the nationally representative Household Budget Survey collected in 2012 by the Turkish Statistical Institute. In total, 9987 households returned valid survey responses. The predictive models are based on eight common risk factors of catastrophic OOP health expenditure. The minority class in the training dataset is oversampled by using a synthetic minority oversampling technique (SMOTE) function, and the original and balanced oversampled training datasets are used to establish the classification models. Logistic regression (LR), random forest (RF) (100 trees), support vector machine (SVM), and neural network (NN) are determined as classifiers. The weighted percentage of households faced with catastrophic OOP health expenditure is 0.14. Balanced oversampling increases the area under the receiver operating characteristic (ROC) curve of LR, RF, SVM, and NN by 0.08%, 0.62%, 0.20%, and 0.23%, respectively. The ROC curve shows NN and RF to be the best classifiers for a balanced oversampled dataset. Identifying a classifier to model highly imbalanced catastrophic OOP health expenditure requires the two-stage procedure of (i) considering a balance between classes and (ii) comparing alternative classifiers. NN and RF are good classifiers in a prediction task with imbalanced catastrophic OOP health expenditure data.

分类不平衡是分类任务中的一个常见问题,它会降低分类器的性能。灾难性自费医疗支出是极少数家庭面临的罕见事件的一个具体例子。本研究的目的是展示一种两步学习方法来建模高度不平衡的灾难性面向对象卫生支出数据。数据来自土耳其统计研究所2012年收集的具有全国代表性的家庭预算调查。总共有9987户家庭收到了有效的调查回复。预测模型是基于灾难性OOP卫生支出的8个常见风险因素。利用合成少数派过采样技术(SMOTE)函数对训练数据集中的少数派类进行过采样,并利用原始和平衡过采样训练数据集建立分类模型。确定了逻辑回归(LR)、随机森林(RF)(100棵树)、支持向量机(SVM)和神经网络(NN)作为分类器。面临灾难性OOP卫生支出的家庭加权百分比为0.14。均衡过采样使LR、RF、SVM和NN的受试者工作特征(ROC)曲线下面积分别增加0.08%、0.62%、0.20%和0.23%。ROC曲线显示NN和RF是平衡过采样数据集的最佳分类器。确定一个分类器来模拟高度不平衡的灾难性面向对象卫生支出,需要两个阶段的过程:(i)考虑类别之间的平衡,(ii)比较替代分类器。神经网络和射频在具有不平衡的灾难性面向对象卫生支出数据的预测任务中是很好的分类器。
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引用次数: 1
An optimization model for multi-appointment scheduling in an outpatient cardiology setting 心脏病门诊多预约调度的优化模型
IF 2.1 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2020-09-01 DOI: 10.1016/j.orhc.2020.100267
Lida Anna Apergi , John S. Baras , Bruce L. Golden , Kenneth E. Wood

In this paper, we tackle the problem of outpatient scheduling in the cardiology department of a large medical center. The outpatients have to go through a number of diagnostic tests and treatments before they are able to complete the final interventional procedure or surgery. We develop an integer programming (IP) formulation to ensure that the outpatients will go through the necessary procedures on time, that they will have enough time to recover after each step, and that their availability will be taken into account. Our goal is to schedule appointments that are convenient for the outpatients, by minimizing the number of visits that the patients have to make to the hospital and the time they spend waiting in the hospital. We propose formulation improvements and introduce valid inequalities to the IP, which help the running times to decrease significantly. Furthermore, we investigate whether scheduling outpatients in groups can lead to better schedules for the patients. This would require coordination between the different members of the scheduling staff within the cardiology department. The results show improvements in the total objective value over a period of one month, ranging from 0.45% to 2.33% on average, depending on the scenario taken into account.

本文研究了某大型医疗中心心内科的门诊调度问题。门诊病人在能够完成最后的介入程序或手术之前,必须经过许多诊断测试和治疗。我们开发了一个整数规划(IP)公式,以确保门诊病人按时完成必要的程序,每一步都有足够的时间恢复,并考虑到他们的可用性。我们的目标是通过减少病人到医院的次数和他们在医院等待的时间,为门诊病人安排方便的预约。我们提出了公式改进,并引入有效的不等式到IP中,这有助于显着减少运行时间。此外,我们还调查了分组门诊是否能给患者带来更好的安排。这需要心脏科内不同的调度人员之间的协调。结果表明,在一个月的时间里,总目标值的改善幅度从0.45%到2.33%不等,具体取决于所考虑的情景。
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引用次数: 3
Behind-the-Scenes Weight Tuning for applied nurse rostering 幕后体重调整应用护士名册
IF 2.1 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2020-09-01 DOI: 10.1016/j.orhc.2020.100265
Elín Björk Böðvarsdóttir , Pieter Smet , Greet Vanden Berghe

Although researchers have developed countless nurse rostering algorithms throughout the years, the paradigm of manual scheduling continues to hinder their application in practice. While manual scheduling gives practitioners full control in assigning nurses to shifts based on their knowledge of the personnel, it has some severe drawbacks. Manual scheduling is tremendously time-consuming and often fails to reach organizational targets, as practitioners need to address numerous constraints and objectives, which frequently conflict with one another. Until now, most nurse rostering formulations have employed weighted sum objective functions that rely on manually-set weights. Understanding the impact of those weights, and thus selecting appropriate values for them, is not trivial. Consequently, the optimization objective often does not capture the desired outcome, resulting in poor quality rosters with an unacceptable combination of constraint violations. This paper introduces a general methodology, Behind-the-Scenes Weight Tuning, which uses measurable targets for guidance in order to automatically set weights. As the methodology does not require practitioners to provide accurate objective weights, the level of manual effort is substantially reduced. Outcome of experiments has shown that by enabling the computer to make quantitatively-supported decisions in this manner, we consistently obtain better rosters than when relying on practitioners to set appropriate weights.

尽管研究人员多年来开发了无数的护士名册算法,但人工调度的范式仍然阻碍了它们在实践中的应用。虽然手动调度给从业者完全控制分配护士轮班根据他们的人员的知识,它有一些严重的缺点。手动调度非常耗时,并且经常不能达到组织目标,因为从业者需要处理大量的约束和目标,这些约束和目标经常相互冲突。到目前为止,大多数护士名册公式已采用加权和目标函数,依赖于手动设置的权重。理解这些权重的影响,并因此为它们选择合适的值,并不是微不足道的。因此,优化目标通常不能获得期望的结果,从而导致质量差的名单和不可接受的违反约束的组合。本文介绍了一种通用的方法,幕后权重调优,它使用可测量的目标为指导,以自动设置权重。由于该方法不需要从业者提供准确的客观权重,因此大大减少了手工工作的水平。实验结果表明,通过使计算机以这种方式做出定量支持的决策,我们始终获得比依赖从业者设置适当权重时更好的名单。
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引用次数: 2
A hybrid system dynamics, discrete event simulation and data envelopment analysis to investigate boarding patients in acute hospitals 混合系统动力学、离散事件模拟和数据包络分析研究急症医院住院病人
IF 2.1 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2020-09-01 DOI: 10.1016/j.orhc.2020.100266
Leila Keshtkar, Wael Rashwan, Waleed Abo-Hamad, Amr Arisha

Timely access to health services has become increasingly difficult due to demographic change and aging people growth. These create new heterogeneous challenges for society and healthcare systems. Congestion at acute hospitals has reached unprecedented levels due to the unavailability of acute beds. As a consequence, patients in need of treatment endure prolonged waiting times as a decision whether to admit, transfer, or send them home is made. These long waiting times often result in boarding patients in different places in the hospital. This threatens patient safety and diminishes the service quality while increasing treatment costs. It is argued in the extant literature that improved communication and enhanced patient flow is often more effective than merely increasing hospital capacity. Achieving this effective coordination is challenged by the uncertainties in care demand, the availability of accurate information, the complexity of inter-hospital dynamics and decision times. A hybrid simulation approach is presented in this paper, which aims to offer hospital managers a chance at investigating the patient boarding problem. Integrating ‘System Dynamic’ and ‘Discrete Event Simulation’ enables the user to ease the complexity of patient flow at both macro and micro levels. ‘Design of Experiment’ and ‘Data Envelopment Analysis’ are integrated with the simulation in order to assess the operational impact of various management interventions efficiently. A detailed implementation of the approach is demonstrated on an emergency department (ED) and Acute Medical Unit (AMU) of a large Irish hospital, which serves over 50,000 patients annually. Results indicate that improving transfer rates between hospital units has a significant positive impact. It reduces the number of boarding patients and has the potential to increase access by up to 40% to the case study organization. However, poor communication and coordination, human factors, downstream capacity constraints, shared resources and services between units may affect this access. Furthermore, an increase in staff numbers is required to sustain the acceptable level of service delivery.

由于人口变化和人口老龄化,及时获得保健服务变得越来越困难。这给社会和医疗保健系统带来了新的挑战。由于急症病床不足,急症医院的拥挤程度已达到前所未有的程度。因此,需要治疗的病人在决定是入院、转院还是送回家时,要忍受很长时间的等待。这些漫长的等待时间往往导致病人在医院的不同地方寄宿。这威胁到患者的安全,降低了服务质量,同时增加了治疗费用。现有文献认为,改善沟通和增强病人流量往往比仅仅增加医院容量更有效。实现这种有效的协调受到护理需求的不确定性、准确信息的可用性、医院间动态和决策时间的复杂性的挑战。本文提出了一种混合模拟方法,旨在为医院管理者提供一个研究病人登机问题的机会。集成“系统动态”和“离散事件模拟”使用户能够在宏观和微观层面上缓解患者流程的复杂性。“实验设计”和“数据包络分析”与模拟相结合,以便有效地评估各种管理干预措施的操作影响。在爱尔兰一家大型医院的急诊科和急症医疗科(AMU)中详细展示了该方法的实施情况,该医院每年为5万多名患者提供服务。结果表明,提高医院单位之间的转诊率具有显著的积极影响。它减少了住院患者的数量,并有可能将案例研究组织的访问率提高40%。然而,沟通和协调不畅、人为因素、下游能力限制、各单位之间共享资源和服务可能会影响这种接入。此外,需要增加工作人员人数,以维持可接受的服务水平。
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引用次数: 9
Maximum matchings in graphs for allocating kidney paired donation 用于分配肾脏配对捐赠的图中的最大匹配
IF 2.1 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2020-06-01 DOI: 10.1016/j.orhc.2020.100246
Sommer Gentry , Michal A. Mankowski , T.S. Michael

Living donors are often incompatible with their intended recipients. Kidney paired donation matches one patient and his or her incompatible donor with another pair in the same situation for an exchange. Let patient-donor pairs be the vertices of an undirected graph G, with edges connecting reciprocally compatible vertices. A matching in G is a feasible set of paired donations. Because the lifespan of a transplant depends on the immunologic concordance of donor and recipient, we weight the edges of G and seek a maximum edge-weight matching. Unfortunately, such matchings might not have the maximum cardinality; there is a risk of an unpredictable trade-off between quality and quantity of paired donations. We prove that the number of paired donations is within a multiplicative factor of the maximum possible donations, where the factor depends on the edge weighting. We propose an edge weighting of G which guarantees that every matching with maximum weight also has maximum cardinality, and also maximizes the number of transplants for an exceptional subset of recipients, while favoring immunologic concordance. We partially generalize this result to k-way exchange and chains, and we implement our weightings using a real patient dataset from Brazil.

活体捐赠者往往与其预定的接受者不相容。肾脏配对捐赠将一名患者及其不相容的捐赠者与另一对处于相同情况的患者配对进行交换。设患者-供体对是无向图G的顶点,边连接相互兼容的顶点。G中的匹配是一组可行的成对捐赠。因为移植的寿命取决于供体和受体的免疫一致性,我们对G的边缘进行加权,并寻求最大的边缘权重匹配。不幸的是,这样的匹配可能没有最大基数;配对捐赠的质量和数量之间存在不可预测的权衡风险。我们证明了配对捐赠的数量在最大可能捐赠的乘积因子内,其中该因子取决于边缘权重。我们提出了G的边缘加权,它保证每一个具有最大权重的匹配也具有最大基数,并且也最大化了特殊受体子集的移植数量,同时有利于免疫一致性。我们将这一结果部分推广到k路交换和链,并使用来自巴西的真实患者数据集实现我们的权重。
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引用次数: 3
Modeling patient preference in an operating room scheduling problem 手术室调度问题中患者偏好的建模
IF 2.1 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2020-06-01 DOI: 10.1016/j.orhc.2020.100257
Abdulaziz Ahmed , Haneen Ali

When a patient needs plastic surgery and there are multiple available surgeons, the patient selects the surgeon based on different criteria. Accommodating patient preference while scheduling such surgeries is important as it is related to patient satisfaction. In this study, we propose a framework for integrating patient preference in an operating room (OR) scheduling problem. To model patient preference to a surgeon, we propose nine criteria: responsive and caring, reputation, professional experiences, communication skills, same ethnicity, same gender, age, same language, and online rating. Fuzzy TOPSIS (namely, Technique for Order of Preference by Similarity to Ideal Solution) is then employed to quantify patient preference to surgeons. The outcomes of fuzzy TOPSIS are then fed into a multi-objective mixed-integer linear programming (MILP) model to optimize daily surgery schedule. The proposed study is based on a real-life case study that was conducted in a plastic surgery department at a partner hospital. The computational results show that when patient preference to surgeon is considered, more than 70% of patients are assigned to their most preferred surgeons, and less than 5% are assigned to their least preferred surgeons. However, when patient preference is not considered, less than 20% of patients are assigned to most preferred surgeons, and the others are assigned to less preferred surgeons. When it comes to the total costs, the two scenarios results are similar. This concludes that the proposed framework is robust and able to increase patient satisfaction in OR scheduling without sacrificing the total OR operational costs.

当患者需要整形手术,并且有多个可用的外科医生时,患者根据不同的标准选择外科医生。在安排此类手术时考虑患者的偏好是很重要的,因为这关系到患者的满意度。在这项研究中,我们提出了一个框架,以整合患者的偏好在手术室(或)调度问题。为了模拟患者对外科医生的偏好,我们提出了9个标准:反应和关怀、声誉、专业经验、沟通技巧、同种族、同性别、年龄、同语言和在线评分。然后使用模糊TOPSIS(即,根据理想解决方案的相似性排序偏好技术)来量化患者对外科医生的偏好。然后将模糊TOPSIS的结果输入到多目标混合整数线性规划(MILP)模型中,以优化每日手术计划。拟议的研究是基于在合作医院的整形外科进行的真实案例研究。计算结果表明,当考虑患者对外科医生的偏好时,超过70%的患者被分配到他们最喜欢的外科医生,不到5%的患者被分配到他们最不喜欢的外科医生。然而,当不考虑患者偏好时,不到20%的患者被分配到最喜欢的外科医生,而其他患者被分配到不太喜欢的外科医生。当涉及到总成本时,两种情况的结果是相似的。由此得出结论,所提出的框架是稳健的,能够在不牺牲手术室总运营成本的情况下提高患者对手术室调度的满意度。
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引用次数: 10
Robust multi-class multi-period patient scheduling with wait time targets 具有等待时间目标的鲁棒多类多周期患者调度
IF 2.1 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2020-06-01 DOI: 10.1016/j.orhc.2020.100254
Houra Mahmoudzadeh, Akram Mirahmadi Shalamzari, Hossein Abouee-Mehrizi

Long wait times for health care services is a known challenge in most health care systems. This is partially due to limited capacity and increased demand, but also due to sub-optimal scheduling policies. In this paper, we consider a health system in which patients are prioritized based on their acuity level. We assume that there is a wait time target for each acuity level to ensure that patients of lower acuity do not wait for an unreasonable amount of time while higher acuity patients are being served. We apply a robust optimization (RO) approach to schedule patients over a multi-period finite horizon considering the wait targets. First, we present a deterministic mixed-integer programming model which considers patient priorities, available capacity, and wait time targets for each priority level. We then investigate the robust counterpart of the model by considering uncertainty in demand and employing the notion of budget of uncertainty. Finally, we numerically compare the proposed robust model with the deterministic method. Our results demonstrate that the proposed robust approach provides solutions with higher service levels and lower wait times. Our results also provide insights on how expanding capacity and choosing the level of uncertainty affect the performance of the system.

在大多数卫生保健系统中,等待卫生保健服务的时间过长是一个众所周知的挑战。这部分是由于有限的容量和增加的需求,但也是由于次优调度策略。在本文中,我们考虑了一个医疗系统,其中病人是根据他们的敏锐度优先级。我们假设每个视力水平都有一个等待时间目标,以确保较低视力的患者在接受服务时不会等待不合理的时间。我们采用鲁棒优化(RO)方法在考虑等待目标的多周期有限水平上安排患者。首先,我们提出了一个确定性混合整数规划模型,该模型考虑了每个优先级级别的患者优先级、可用容量和等待时间目标。然后,我们通过考虑需求的不确定性并采用不确定性预算的概念来研究该模型的鲁棒对应物。最后,对所提出的鲁棒模型与确定性方法进行了数值比较。我们的结果表明,所提出的健壮方法提供了具有更高服务级别和更短等待时间的解决方案。我们的结果还提供了关于扩展容量和选择不确定性水平如何影响系统性能的见解。
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引用次数: 8
期刊
Operations Research for Health Care
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