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We present a scalable forecasting framework with a Monte Carlo simulation to forecast the short-term bed occupancy of patients with confirmed and suspected COVID-19 in intensive care units and regular wards. Our forecasts were a central part of the official weekly reports of the Bavarian State Ministry of Health and Care from May 2020 to March 2021.

Lack of rapidly available neurological expertise, especially in rural areas, is one of the key obstacles in stroke care. Stroke care networks attempt to address this challenge by connecting hospitals with specialized stroke centers, stroke units, and hospitals of lower levels of care. While the benefits of stroke care networks are well-documented, travel distances are likely to increase when patients are transferred almost exclusively between members of the same network. This is particularly important for patients who require mechanical thrombectomy, an increasingly employed treatment method that requires equipment and expertise available in specialized stroke centers. This study aims to analyze the performance of the current design of stroke care networks in Bavaria, Germany, and to evaluate the improvement potential when the networks are redesigned to minimize travel distances. To this end, we define three fundamental criteria for assessing network design performance: 1) average travel distances, 2) the populace in the catchment area relative to the number of stroke units, and 3) the ratio of stroke units to lower-care hospitals. We generate several alternative stroke network designs using an analytical approach based on mathematical programming and clustering. Finally, we evaluate the performance of the existing networks in Bavaria via simulation. The results show that the current network design could be significantly improved concerning the average travel distances. Moreover, the existing networks are unnecessarily imbalanced when it comes to their number of stroke units per capita and the ratio of stroke units to lower-care hospitals.

To construct good quality plans or planning systems in hospitals, such as capacity planning, case mix planning, master surgery scheduling, and shift scheduling, knowing details about the duration of surgeries is paramount. Furthermore, the operating room is one of a hospital’s main cost drivers, thus making surgery duration a key to achieving cost effectiveness. To gain a better understanding of the interdependencies of determining surgery durations, we investigate the influence physicians have on the duration of a surgery. Since physician experience is a very generalizable factor across a heterogeneous group of hospitals, it is the most obvious influencing factor to analyze. Accordingly, we utilize information regarding a physician’s level of experience and examine its impact on surgery durations using data from a German hospital. Although we are forced to use aggregate data for privacy and labor law reasons, a combination of linear and quantile regression analysis allows us to derive several important insights. First, on average, an increase in a physician’s experience leads to a decrease in the duration of a surgery. Second, the effect of the first insight depends on the composition of the surgical team and diminishes in the case of teaching activities. Third, the relationship between experience level and surgery duration varies across the distribution of durations, i.e., the relationship is strongest for short surgeries and weakens as the duration of a surgery increases.

Performance modeling of hospitals using data envelopment analysis (DEA) has received steadily increasing attention in the literature. As part of the traditional DEA framework, hospitals are generally assumed to be functionally similar and therefore homogenous. Accordingly, any identified inefficiency is supposedly due to the inefficient use of inputs to produce outputs. However, the disparities in DEA efficiency scores may be a result of the inherent heterogeneity of hospitals. Additionally, traditional DEA models lack predictive capabilities despite having been frequently used as a benchmarking tool in the literature. To address these concerns, this study proposes a framework for analyzing hospital performance by combining two complementary modeling approaches. Specifically, we employ a self-organizing map artificial neural network (SOM-ANN) to conduct a cluster analysis and a multilayer perceptron ANN (MLP-ANN) to perform a heterogeneity analysis and a best practice analysis. The applicability of the integrated framework is empirically shown by an implementation to a large dataset containing more than 1,100 hospitals in Germany. The framework enables a decision-maker not only to predict the best performance but also to explore whether the differences in relative efficiency scores are ascribable to the heterogeneity of hospitals.

Background Since operating rooms are a major bottleneck resource and an important revenue driver in hospitals, it is important to use these resources efficiently. Studies estimate that between 60 and 70% of hospital admissions are due to surgeries. Furthermore, staffing cannot be changed daily to respond to changing demands. The resulting high complexity in operating room management necessitates perpetual process evaluation and the use of decision support tools. In this study, we evaluate several management policies and their consequences for the operating theater of the University Hospital Augsburg. Methods Based on a data set with 12,946 surgeries, we evaluate management policies such as parallel induction of anesthesia with varying levels of staff support, the use of a dedicated emergency room, extending operating room hours reserved as buffer capacity, and different elective patient sequencing policies. We develop a detailed simulation model that serves to capture the process flow in the entire operating theater: scheduling surgeries from a dynamically managed waiting list, handling various types of schedule disruptions, rescheduling and prioritizing postponed and deferred surgeries, and reallocating operating room capacity. The system performance is measured by indicators such as patient waiting time, idle time, staff overtime, and the number of deferred surgeries. Results We identify significant trade-offs between expected waiting times for different patient urgency categories when operating rooms are opened longer to serve as end-of-day buffers. The introduction of parallel induction of anesthesia allows for additional patients to be scheduled and operated on during regular hours. However, this comes with a higher number of expected deferrals, which can be partially mitigated by employing additional anesthesia teams. Changes to the sequencing of elective patients according to their expected surgery duration cause expectable outcomes for a multitude of performance indicators. Conclusions Our simulation-based approach allows operating theater managers to test a multitude of potential changes in operating room management without disrupting the ongoing workflow. The close collaboration between management and researchers in the design of the simulation framework and the data analysis has yielded immediate benefits for the scheduling policies and data collection efforts at our practice partner.

When scheduling surgeries in the operating theater, not only the resources within the operating theater have to be considered but also those in downstream units, e.g., the intensive care unit and regular bed wards of each medical specialty. We present an extension to the master surgery schedule, where the capacity for surgeries on ICU patients is controlled by introducing downstream-dependent block types – one for both ICU and ward patients and one where surgeries on ICU patients must not be performed. The goal is to provide better control over post-surgery patient flows through the hospital while preserving each medical specialty’s autonomy over its operational surgery scheduling. We propose a mixed-integer program to determine the allocation of the new block types within either a given or a new master surgery schedule to minimize the maximum workload in downstream units. Using a simulation model supported by seven years of data from the University Hospital Augsburg, we show that the maximum workload in the intensive care unit can be reduced by up to 11.22% with our approach while maintaining the existing master surgery schedule. We also show that our approach can achieve up to 79.85% of the maximum workload reduction in the intensive care unit that would result from a fully centralized approach. We analyze various hospital setting instances to show the generalizability of our results. Furthermore, we provide insights and data analysis from the implementation of a quota system at the University Hospital Augsburg.

The intensive care unit is a highly specialized and expensive hospital resource serving both emergency and scheduled patients. The vast majority of scheduled patients arrive from the operating thea...

Zusammenfassung Hintergrund Die COVID-19-Pandemie zeichnet sich durch einen sich langsam aufbauenden Bedarf von Ressourcen des Gesundheitswesens mit lokalen Hotspots aus und erzeugt dadurch enorme Probleme. Für die Krankenhäuser liegt eine der größten Herausforderungen in der Vorhaltung von Bettenkapazitäten, insbesondere da der Bedarf schwer vorherzusehen ist. Fragestellung Um den Entscheidungsträgern eine Hilfestellung zu geben, wurden mehrere simulationsbasierte Prognosen für die benötigte Bettenkapazität am Universitätsklinikum Augsburg durchgeführt, um bei variablen Pandemieverläufen die benötigten Bettenkapazitäten abschätzen zu können. Methode Als Input dienen aktuelle Erkenntnisse über den Verlauf der Ausbreitung, insbesondere die Wachstumsrate an kumulierten Neuinfektionen pro Tag. Zur Abbildung von Unsicherheit werden mittels Verteilungsfunktionen, basierend auf Realdaten der Wachstumsrate, die Verweildauer sowie der Anteil der hospitalisierten COVID-19-Patienten im Einzugsgebiet modelliert. Im Anschluss erfolgt eine Monte-Carlo-Simulation, die eine Abschätzung der benötigten Bettenkapazitäten für mehrere Tage in der Zukunft erlaubt. Ergebnisse Mithilfe von mehreren simulationsbasierten Kapazitätsprognosen im Zeitraum vom 28.03.2020 bis zum 08.06.2020 konnte die benötigte Intensiv- und Normalbettenkapazität am Universitätsklinikum Augsburg sowie im Rettungsdienstbereich Augsburg mit einer hohen Zuverlässigkeit prognostiziert werden. Schlussfolgerung Mithilfe des entwickelten Simulationsmodells zur Abschätzung der benötigten Bettenkapazität kann den Kliniken und dem Katastrophenschutz eine Hilfestellung zur Abschätzung der kurzfristigen Entwicklung des Kapazitätsbedarfs für Verdachtsfälle sowie bestätigte COVID-19-Patienten gegeben werden. Der operative Einsatz der Methode am Universitätsklinikum Augsburg zeigte verlässliche Ergebnisse.

The process of manually constructing monthly working schedules for physicians in medium-sized and large departments at hospitals is a very time-consuming and error-prone task. The scheduler, typically a senior physician, is an expensive resource and oftentimes almost irreplaceable because of his acquired expertise in the scheduling process. We develop a mathematical model that formalizes every rule and regulation necessary to generate lawful schedules in the anesthesiology department of a 626-bed hospital in Berlin, Germany. We embed our detailed and complex mixed-integer programming formulation, which generates schedules superior to the ones currently in use, in an Excel environment to ensure ease of use, maximum flexibility with respect to changing all relevant inputs, and a visual output representation for practitioners. The presented approach reduces the workload for the scheduler dramatically, thereby increasing his availability for medical services. Our generated schedules outperform manually created schedules by significantly reducing the number of rule and regulation violations, while also improving key performance measures such as assigned overtime, granted employee-preferred shifts, and fairness considerations. Our approach also highlights important aspects in modeling the physician scheduling problem for practical implementation that have been widely ignored in the existing literature.

Over the last decades, hospitals have faced increasing financial pressure due to rising costs and changes in reimbursement policies. One of the reasons for this development is the rising number of qualified personnel required to handle the consistently growing demand for hospital services. In the U.S., on average more than 50% of hospital costs are workforce related. In order to reduce these expenses without sacrificing quality of care, a stream of research has focused on building more efficient personnel schedules for nurses and physicians. We provide the first review that focuses on quantitative methods for physician scheduling in hospitals. We describe the relevant characteristics of various investigated physician scheduling problems, such as the problem type, i.e. Staffing, Rostering or Re-planning problems, personnel qualification and experience, and the utilized shift types. Furthermore, we analyze the existing literature with respect to additional modeling features including fairness aspects and underlying demand patterns, as well as employed solution approaches and real life applications. In total, we review 68 relevant publications in the OR/MS field. We use our presented framework to categorize the existing approaches and highlight gaps in the literature to initiate future research activities.

Hospitals continue to face the challenge of providing high-quality patient care in an environment of rising healthcare costs. In response, a great deal of attention has been given to advance planning decisions such as nurse staffing, bed mix, scheduling, and patient flow. However, less attention has been given to incorporating quick-response methods in the nurse scheduling process by both anticipating and responding to patient demand fluctuations. Therefore, in this paper, we present a model that incorporates two classes of quick-response decisions in hospitals’ nurse scheduling: (i) adjustments to the unit assignments of cross-trained float nurses and (ii) transfers of patients between units and off-unit admissions. Analyzing three hospitals that are subject to different regulations with respect to patient-to-nurse ratios allows us to draw conclusions on how these hotly debated ratios impact hospital performance, nurse workload, and patient experience. We find that quick-response via cross-trained nurses may lead to higher total costs in settings where an upper limit on patient-to-nurse ratios is enforced. This result has significant managerial and political relevance in locations such as California. Another takeaway is that only a small number of patient transfers or off-unit admissions provides close to the full potential benefit, thus minimizing the negative impact on patient satisfaction and quality of care. Moreover, our proposed scheduling approach reduces the number of undesired assigned shifts. Finally, bed and nurse capacity utilization are shown to be important considerations when determining how and whether to use quick-response methods.

The case mix planning problem deals with choosing the ideal composition and volume of patients in a hospital. With many countries having recently changed to systems where hospitals are reimbursed for patients according to their diagnosis, case mix planning has become an important tool in strategic and tactical hospital planning. Selecting patients in such a payment system can have a significant impact on a hospital's revenue. The contribution of this article is to provide the first literature review focusing on the case mix planning problem. We describe the problem, distinguish it from similar planning problems, and evaluate the existing literature with regard to problem structure and managerial impact. Further, we identify gaps in the literature. We hope to foster research in the field of case mix planning, which only lately has received growing attention despite its fundamental economic impact on hospitals.

The healthcare sector in general and hospitals in particular represent a main application area for Data Envelopment Analysis (DEA). This paper reviews 262 papers of DEA applications in healthcare with special focus on hospitals and therefore closes a gap of over ten years that were not covered by existing review articles. Apart from providing descriptive statistics of the papers, we are the first to examine the research purposes of the publications. These research goals can be grouped into four distinct clusters according to our proposed framework. The four clusters are (1) "Pure DEA efficiency analysis", i.e. performing a DEA on hospital data, (2) "Developments or applications of new methodologies", i.e. applying new DEAy approaches on hospital data, (3) "Specific management question", i.e. analyzing the effects of managerial specification, such as ownership, on hospital efficiency, and (4) "Surveys on the effects of reforms", i.e. researching the impact of policy making, such as reforms of health systems, on hospital efficiency. Furthermore, we analyze the methodological settings of the studies and describe the applied models. We analyze the chosen inputs and outputs as well as all relevant downstream techniques. A further contribution of this paper is its function as a roadmap to important methodological literature and publications, which provide crucial information on the setup of DEA studies. Thus, this paper should be of assistance to researchers planning to apply DEA in a hospital setting by providing information on a) what has been published between 2005 and 2016, b) possible pitfalls when setting up a DEA analysis, and c) possible ways to apply the DEA analysis in practice. Finally, we discuss what could be done to advance DEA from a scientific tool to an instrument that is actually utilized by managers and policymakers.

Purpose – In order to cope with steadily increasing healthcare costs in industrialized countries, hospital material management has been identified as one key cost containment lever. The purpose of this work is to present the state-of-the-art of research on material logistics management in hospitals.Design/methodology/approach – A systematic approach is undertaken in order to identify the relevant literature from 1998 to 2014. Applicable publications are categorized thematically and methodologically and future research opportunities are worked out. Particular focus is given to articles that apply optimization methods.Findings – In total, 88 publications are identified and discussed in this work. Literature is categorized into four streams, i.e., (1) Supply and procurement, (2) Inventory management, (3) Distribution and scheduling, and (4) Holistic supply chain management. The application of optimization techniques is constantly gaining importance. The number of respective publications has continually grown and has peaked over the last three years. Optimization has been successfully applied in research streams (1), (2), and (3). Category (4) comprises a rather qualitative research field of literature dealing with supply chain management issues and business process redesign tools, transfer of logistics concepts from other industries, and benchmarks, best practices, and cost analyses.Originality/value – Our contribution for practitioners and academics is threefold: First, we provide research guidance through categorizing literature and identifying major research streams. Second, we discuss applied methodologies and third, we identify future research directions.

The intensive care unit (ICU) is a crucial and expensive resource largely affected by uncertainty and variability. Insufficient ICU capacity causes many negative effects not only in the ICU itself, but also in other connected departments along the patient care path. Operations research/management science (OR/MS) plays an important role in identifying ways to manage ICU capacities efficiently and in ensuring desired levels of service quality. As a consequence, numerous papers on the topic exist. The goal of this paper is to provide the first structured literature review on how OR/MS may support ICU management. We start our review by illustrating the important role the ICU plays in the hospital patient flow. Then we focus on the ICU management problem (single department management problem) and classify the literature from multiple angles, including decision horizons, problem settings, and modeling and solution techniques. Based on the classification logic, research gaps and opportunities are highlighted, e.g., combining bed capacity planning and personnel scheduling, modeling uncertainty with non-homogenous distribution functions, and exploring more efficient solution approaches.

The intensive care unit (ICU) is a crucial and expensive resource largely affected by uncertainty and variability. Insufficient ICU capacity causes many negative effects not only for the ICU itself, but also makes it a bottleneck in hospital patient flows. Operations research/management science (OR/MS) plays an important role in identifying ways to manage ICU capacities efficiently and ensuring service quality simultaneously. As a consequence, numerous papers on the topic exist. The aim of this paper is to provide the first structured literature review on how OR/MS may support ICU scheduling and planning. We start our review by illustrating the important role the ICU plays in the hospital patient flow. Then we focus on the ICU scheduling problem (single department scheduling problem) and classify the literature from multiple angles, including decision horizons, problem settings, and modeling and solution techniques. Based on the classification logic, gaps in the current research and significant trends for future research are highlighted, for instance, combining bed capacity planning and personnel scheduling, applying non-homogenous distributions to model uncertainties, and exploring more efficient solution approaches.

Traditional multi-echelon inventory theory focuses on arborescent supply chains that use a central warehouse which replenishes remote warehouses. The remote warehouses serve customers in their respective regions. Common assumptions in the academic literature include use of the Poisson demand process and instantaneous unit-by-unit replenishment. In the practitioner literature, single-echelon approximations are advised for setting safety stock to deal with lead time, demand, and supply variations in these settings. Using data from a U.S. supplier of home improvement products, we find that neither the assumptions from the academic literature nor the approximations from the practitioner literature necessarily work well in practice. In a variation of the strictly arborescent supply chain, the central warehouse at our real company not only replenishes other warehouses but also meets demand from customers in the region near the central warehouse. In this paper, we study this dual-role central warehouse structure, which we believe is common in practice. Using high and low volume product demand data from this company, we use Monte Carlo simulations to study the impact of (1) the use of a dual-role centralized warehouse, (2) common demand assumptions made in multi-echelon research, and (3) single-echelon approximations for managing a multi-echelon supply chain. We explore each of these under both centralized and decentralized control logic. We find that the common assumptions of theoretical models impede their usefulness and that heuristics that ignore the actual supply chain structure fail to account for additional opportunities to utilize safety stock more effectively. Researchers should be aware of the gap between standard assumptions in traditional literature and actual practice, and critically evaluate their assumptions to find a reasonable balance between tractability and relevance.

The original version of this article unfortunately contained errors. The first column of Tables 5 and 6 in the Appendix section should contain the year of publication instead of the reference number in brackets. The reference citations were then placed in the second column.