Operations Research for Health Care最新文献

Providing timely access to costly surgical services in a manner that balances needs of multiple stakeholders (patients, staff, administrators) is made even more challenging by inherent uncertainty. Decisions about clinician scheduling, shift preferences, operating room planning, and patient assignment also often are decentralized or made separately. We develop a robust optimization model that combines staffing and scheduling decisions to minimize the impact of foreseeable variation in surgery durations, staff availability, and urgent or emergency arrivals. Model performance is tested with data from a major academic medical center, resulting in improved service level (% patients served), overtime, utilization, and shift preferences. Although robustness to staffing, duration, and urgent or emergency uncertainty increases operating costs by 6% on average, overtime is reduced by 68% while utilization decreases by only 6%. The number of necessary schedule adjustments on the day of surgery also is reduced by 13% on average in the robust model compared to the nominal model.

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.

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.

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).

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.

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.

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.

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.

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.

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.