Analyzing the accuracy of variable returns to scale data envelopment analysis models

The data envelopment analysis (DEA) model is extensively used to estimate efficiency, but no study has determined the DEA model that delivers the most precise estimates. To address this issue, we advance the Monte Carlo simulation-based data generation process proposed by Kohl and Brunner (2020). The developed process generates an artificial dataset using the Translog production function (instead of the commonly used Cobb Douglas) to construct well-behaved scenarios under variable returns to scale (VRS). Using different VRS DEA models, we compute DEA efficiency scores with artificially generated decision-making units (DMUs). We employ five performance indicators followed by a benchmark value and ranking as well as statistical hypothesis tests to evaluate the quality of the efficiency estimates. The procedure allows us to determine which parameters negatively or positively influence the quality of the DEA estimates. It also enables us to identify which DEA model performs the most efficiently over a wide range of scenarios. In contrast to the widely applied BCC (Banker-Charnes-Cooper) model, we find that the Assurance Region (AR) and Slacks-Based Measurement (SBM) DEA models perform better. Thus, we endorse the use of AR and SBM models for DEA applications under the VRS regime

Stable annual scheduling of medical residents using prioritized multiple training schedules to combat operational uncertainty

For educational purposes, medical residents often have to pass through many departments, which place different requirements on them. They are informed about the upcoming departments by an annual training schedule which keeps the individual departments’ service level as constant as possible. Due to poor planning and uncertain events, deviations in the schedule can occur. These deviations affect the service level in the departments, as well as the training progress and satisfaction of the residents. This article analyzes the impact of priorities on residents’ annual planning based on department assignments to combat uncertainty that might result in departmental changes. We present a novel two-stage formulation that combines residents’ tactical planning with duty and daily scheduling’s operational level. We determine an analytical bound for the problem that is superior to the LP bound. Additionally, we approximate a bound based on the solution approach using the objective value of the deterministic solution of an instance and the absences in each scenario. In a computational study, we analyze the performance of various bounds, our solution approach, and the effects of additional priorities in residents’ annual planning. We show that additional priorities can significantly reduce the number of unexpected department assignments. Finally, we derive a practical number of priorities from the results

Optimizing physician schedules with resilient break assignments

This article presents a novel model for building biweekly rosters for physicians according to the regulations of a German teaching hospital, while also ensuring the viability of breaks. Currently, rosters are manually prepared by experienced physicians with basic spreadsheet knowledge, leading to significant costs and time consumption because of the complexity of the problem and the individual working conditions of the physicians. Unfortunately, manually generated rosters frequently prove to be non-compliant with labor regulations and ergonomic agreements, resulting in potential overtime hours and employee dissatisfaction. A particular concern is the inability of physicians to take mandatory breaks, which negatively affects both employee motivation and the hospital service level. To address these challenges, we propose a data-driven formulation of an operational physician scheduling problem, considering overstaffing and overtime hours as primary cost drivers and integrating shift preferences and break viability as ergonomic objectives. We develop and train a survival regression model to predict the viability of breaks, allowing practitioners to define break-time windows appropriately. Given the limitations of standard solvers in producing high-quality solutions within a reasonable timeframe, we adopt a Dantzig–Wolfe decomposition to reformulate the proposed model. Furthermore, we develop a branch-and-price algorithm to achieve optimal solutions and introduce a problem-specific variable selection strategy for efficient branching. To assess the algorithm’s effectiveness and examine the impact of the new break assignment constraint, we conducted a comprehensive computational study using real-world data from a German training hospital. Using our approach, healthcare institutions can streamline the rostering process, minimize the costs associated with overstaffing and overtime hours, and improve employee satisfaction by ensuring that physicians can take their legally mandated breaks. Ultimately, this contributes to better employee motivation and improves the overall level of hospital service

Homogeneity and best practice analyses in hospital performance management: an analytical framework

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

Balancing control and autonomy in master surgery scheduling: benefits of ICU quotas for recovery units

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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10729-021-09588-8

Pollen allergy and health behavior: patients trivializing their disease

Allergies are increasing in prevalence worldwide, with socioeconomic impacts and effects on quality of life. The aim of this study was to explore the health behavior and the utilization of different treatment options via questionnaires and to investigate for relationships of the above with socioeconomic factors. This cross-sectional survey was carried out among pollen allergic subjects in 2016, using questionnaires. A total of 679 allergics participated in the study (61.2% females). Their average age was 26.8 +/- 8.8years. Their symptom severity was 6.1 +/- 1.9, measured on a 10-step scale and symptoms lasted for 9.0 +/- 6.8weeks during pollen season. Of all allergics, 9.1% were not aware of the causative agent of their allergy and 17.4% had never undergone allergy testing. Symptoms, especially in females, had strong impact on social life, everyday routines and sleep quality. Almost half of the participants treated their allergy without medical supervision, while only 32.3% sought medical support. Nevertheless, three quarters reported self-management of their allergies with oral antihistamines. Compared to males, females sought significantly more medical support, medications and allergen avoidance strategies. Knowledge about allergy increased the likelihood of treatment under supervision of a medical expert than no treatment, as well as symptom severity and interaction between female gender and symptom severity. The attitude of not considering allergy as a serious disease significantly reduced the likelihood of undergoing specific immunotherapy. This survey not only highlights the negative impact of pollen allergies on everyday life of allergics, but also that allergies are often neglected and untreated because of their trivialization by allergic subjects themselves

A scalable forecasting framework to predict COVID-19 hospital bed occupancy

The coronavirus disease 2019 (COVID-19) pandemic has led to capacity problems in many hospitals around the world. During the peak of new infections in Germany in April 2020 and October to December 2020, most hospitals had to cancel elective procedures for patients because of capacity shortages. 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. We apply the simulation to different granularity and geographical levels. Our forecasts were a central part of the official weekly reports of the Bavarian State Ministry of Health and Care, which were sent to key decision makers in the individual ambulance districts from May 2020 to March 2021. Our evaluation shows that the forecasting framework delivers accurate forecasts despite data availability and quality issues