Health Care Management Science最新文献

This paper deals with Emergency Department (ED) fast-tracks for low-acuity patients, a strategy often adopted to reduce ED overcrowding. We focus on optimizing resource allocation in minor injuries units, which are the ED units that can treat low-acuity patients, with the aim of minimizing patient waiting times and ED operating costs. We formulate this problem as a general multiobjective simulation-based optimization problem where some of the objectives are expensive black-box functions that can only be evaluated through a time-consuming simulation. To efficiently solve this problem, we propose a metamodeling approach that uses an artificial neural network to replace a black-box objective function with a suitable model. This approach allows us to obtain a set of Pareto optimal points for the multiobjective problem we consider, from which decision-makers can select the most appropriate solutions for different situations. We present the results of computational experiments conducted on a real case study involving the ED of a large hospital in Italy. The results show the reliability and effectiveness of our proposed approach, compared to the standard approach based on derivative-free optimization.

Discharge planning is integral to patient flow as delays can lead to hospital-wide congestion. Because a structured discharge plan can reduce hospital length of stay while enhancing patient satisfaction, this topic has caught the interest of many healthcare professionals and researchers. Predicting discharge outcomes, such as destination and time, is crucial in discharge planning by helping healthcare providers anticipate patient needs and resource requirements. This article examines the literature on the prediction of various discharge outcomes. Our review discovered papers that explore the use of prediction models to forecast the time, volume, and destination of discharged patients. Of the 101 reviewed papers, 49.5% looked at the prediction with machine learning tools, and 50.5% focused on prediction with statistical methods. The fact that knowing discharge outcomes in advance affects operational, tactical, medical, and administrative aspects is a frequent theme in the papers studied. Furthermore, conducting system-wide optimization, predicting the time and destination of patients after discharge, and addressing the primary causes of discharge delay in the process are among the recommendations for further research in this field.

We present a new model formulation for a multiproduct dynamic order quantity problem with product returns and a reprocessing option. The optimization considers the limited shelf life of sterile medical devices as well as the capacity constraints of reprocessing and sterilization resources. The time-varying demand is known in advance and must be satisfied by purchasing new medical devices or by reprocessing used and expired devices. The objective is to determine a feasible procurement and reprocessing plan that minimizes the incurred costs. The problem is solved in a heuristic manner in two steps. First, we use a Dantzig-Wolfe reformulation of the underlying problem, and a column generation approach is applied to tighten the lower bound. In the next step, the obtained lower bound is transformed into a feasible solution using CPLEX. Our numerical results illustrate the high solution quality of this approach. The comparison with a simulation based on the first-come-first-served principle shows the advantage of integrated planning.

Faced by a severe shortage of nurses and increasing demand for care, hospitals need to optimally determine their staffing levels. Ideally, nurses should be staffed to those shifts where they generate the highest positive value for the quality of healthcare. This paper develops an approach that identifies the incremental benefit of staffing an additional nurse depending on the patient mix. Based on the reasoning that timely fulfillment of care demand is essential for the healthcare process and its quality in the critical care setting, we propose to measure the incremental benefit of staffing an additional nurse through reductions in time until care arrives (TUCA). We determine TUCA by relying on queuing theory and parametrize the model with real data collected through an observational study. The study indicates that using the TUCA concept and applying queuing theory at the care event level has the potential to improve quality of care for a given nurse capacity by efficiently trading situations of high versus low workload.

Nowadays, emergency medical technicians (EMTs) decide to send a suspected stroke patient to a primary stroke center (PSC) or to an endovascular thrombectomy (EVT)-capable hospital, based on the Cincinnati Prehospital Stroke Scale (CPSS) and the number of symptoms a patient presents at the scene. Based on existing studies, the patient is likely to have a better functional outcome after three months if the time between the onset of symptoms and receiving EVT treatment is shorter. However, if an acute ischemic stroke (AIS) patient with large vessel occlusion (LVO) is first sent to a PSC, and then needs to be transferred to an EVT-capable hospital, the time to get definitive treatment is significantly increased. For this purpose, We formulate an integer programming model to minimize the expected time to receive a definitive treatment for stroke patients. We then use real-world data to verify the validity of the model. Also, we expand our model to find the optimal redistribution and centralization of EVT resources. It will enable therapeutic teams to increase their experience and skills more efficiently within a short period of time.

This paper addresses the management of patients' transportation requests within a hospital, a very challenging problem where requests must be scheduled among the available porters so that patients arrive at their destination timely and the resources invested in patient transport are kept as low as possible. Transportation requests arrive during the day in an unpredictable manner, so they need to be scheduled in real-time. To ensure that the requests are scheduled in the best possible manner, one should also reconsider the decisions made on pending requests that have not yet been completed, a process that will be referred to as rescheduling. This paper proposes several policies to trigger and execute the rescheduling of pending requests and three approaches (a mathematical formulation, a constructive heuristic, and a local search heuristic) to solve each rescheduling problem. A simulation tool is proposed to assess the performance of the rescheduling strategies and the proposed scheduling methods to tackle instances inspired by a real mid-size hospital. Compared to a heuristic that mimics the way requests are currently handled in our partner hospital, the best combination of scheduling method and rescheduling strategy produces an average 5.7 minutes reduction in response time and a 13% reduction in the percentage of late requests. Furthermore, since the total distance walked by porters is substantially reduced, our experiments demonstrate that it is possible to reduce the number of porters - and therefore the operating costs - without reducing the current level of service.

A patient fall is one of the adverse events in an inpatient unit of a hospital that can lead to disability and/or mortality. The medical literature suggests that increased visibility of patients by unit nurses is essential to improve patient monitoring and, in turn, reduce falls. However, such research has been descriptive in nature and does not provide an understanding of the characteristics of an optimal inpatient unit layout from a visibility-standpoint. To fill this gap, we adopt an interdisciplinary approach that combines the human field of view with facility layout design approaches. Specifically, we propose a bi-objective optimization model that jointly determines the optimal (i) location of a nurse in a nursing station and (ii) orientation of a patient's bed in a room for a given layout. The two objectives are maximizing the total visibility of all patients across patient rooms and minimizing inequity in visibility among those patients. We consider three different layout types, L-shaped, I-shaped, and Radial; these shapes exhibit the section of an inpatient unit that a nurse oversees. To estimate visibility, we employ the ray casting algorithm to quantify the visible target in a room when viewed by the nurse from the nursing station. The algorithm considers nurses' horizontal visual field and their depth of vision. Owing to the difficulty in solving the bi-objective model, we also propose a Multi-Objective Particle Swarm Optimization (MOPSO) heuristic to find (near) optimal solutions. Our findings suggest that the Radial layout appears to outperform the other two layouts in terms of the visibility-based objectives. We found that with a Radial layout, there can be an improvement of up to 50% in equity measure compared to an I-shaped layout. Similar improvements were observed when compared to the L-shaped layout as well. Further, the position of the patient's bed plays a role in maximizing the visibility of the patient's room. Insights from our work will enable understanding and quantifying the relationship between a physical layout and the corresponding provider-to-patient visibility to reduce adverse events.

This study reviews scholarly publications on data envelopment analysis (DEA) studies on acute care hospital (ACH) efficiency published between 1984 and 2022 in scholarly peer-reviewed journals. We employ systematic literature review (SLR) method to identify and analyze pertinent past research using predetermined steps. The SLR offers a comprehensive resource that meticulously analyzes DEA methodology for practitioners and researchers focusing on ACH efficiency measurement. The articles reviewed in the SLR are analyzed and synthesized based on the nature of the DEA modelling process and the key findings from the DEA models. The key findings from the DEA models are presented under the following sections: effects of different ownership structures; impacts of specific healthcare reforms or other policy interventions; international and multi-state comparisons; effects of changes in competitive environment; impacts of new technology implementations; effects of hospital location; impacts of quality management interventions; impact of COVID-19 on hospital performance; impact of teaching status, and impact of merger. Furthermore, the nature of DEA modelling process focuses on use of sensitivity analysis; choice of inputs and outputs; comparison with Stochastic Frontier Analysis; use of congestion analysis; use of bootstrapping; imposition of weight restrictions; use of DEA window analysis; and exogenous factors. The findings demonstrate that, despite several innovative DEA extensions and hospital applications, over half of the research used the conventional DEA models. The findings also show that the most often used inputs in the DEA models were labor-oriented inputs and hospital beds, whereas the most frequently used outputs were outpatient visits, followed by surgeries, admissions, and inpatient days. Further research on the impact of healthcare reforms and health information technology (HIT) on hospital performance is required, given the number of reforms being implemented in many countries and the role HIT plays in enhancing care quality and lowering costs. We conclude by offering several new research directions for future studies.

Efficiency analysis is crucial in healthcare to optimise resource allocation and enhance patient outcomes. However, the prompt adaptation of inputs can be hindered by adjustment costs, which impact Long-Run Technical Efficiency (LRTE). To bridge this gap in healthcare literature, this research employs a Bayesian Dynamic Stochastic Frontier Model to estimate parameters and explore healthcare efficiency dynamics over time. The study reveals the LRTE for New Zealand District Health Boards (DHBs) as 0.76, indicating around 32% more input utilisation due to adjustment costs. Most DHBs exhibit consistent short-run operational efficiency, with the national Short-Run Technical Efficiency (SRTE) very close to the LRTE. Among the tertiary providers, Auckland and Capital & Coast DHBs operate below the LRTE level, setting them apart from other tertiary providers. Similarly, Tairawhiti and West Coast DHBs also fall below the LRTE level, as indicated by their SRTE scores, potentially influenced by their unique healthcare settings and resource challenges. This research brings a new perspective to policy discussions by incorporating the temporal dynamics of decision-making and considering adjustment costs. It underscores the need to balance short-term and long-term technical efficiency, underlining their collective significance in fostering a sustainable and efficient healthcare system in New Zealand.