Health Care Management Science最新文献

This study examines the inpatient service efficiency of safety-net and non-safety-net hospitals using a two-stage approach at both the hospital and physician levels. For the hospital-level analysis, we conducted 430 Data Envelopment Analysis (DEA) models at the first stage to measure efficiency at the Diagnosis-Related Groups (DRG) level. In the second stage, Tobit and logistic regression models were applied to compare safety-net hospitals to non-safety-net hospitals. For the physician-level analysis, we conducted 386 DEA models to measure individual physician efficiency within specific DRGs. In the second stage, we compared the performance of the same physicians working in safety-net versus non-safety-net hospitals. The findings reveal that non-safety-net hospitals demonstrate significantly higher efficiency than safety-net hospitals. However, comparisons of the same physicians across settings show no significant differences in individual efficiency. This suggests that the efficiency gap arises not from the support or motivation provided by hospitals but from differences in the quality of physicians employed. These results underscore the need for policies that help safety-net hospitals attract and retain high-quality physicians to bridge the efficiency gap and better serve vulnerable populations.

Standing orders allow triage nurses in emergency departments (EDs) to order tests for target patients prior to a physician evaluation. Standing orders specify the medical conditions for which a triage nurse is permitted to order tests but typically do not specify the operational conditions under which ordering tests is desirable, from either a system or a patient point of view. We examine the operational impacts of standing orders on the ED as a whole, and propose a threshold policy for activating standing orders as a function of ED congestion. To parameterize the threshold policy we develop three simplified models: 1) an infinite-server model to derive an easily-computed feature for predicting whether activating standing orders would be beneficial, 2) a Jackson network model, to demonstrate that standing orders can lead to diverse outcomes for different patient populations, and 3) a Markov decision process model, to quantify the optimality gap for our threshold policy. We confirm the tentative findings from the simplified models in a more realistic setting using a simulation model that is calibrated with real data. We find that the threshold policy, with a threshold that is a simple function of the aforementioned feature, performs well across a wide range of parameter values. We demonstrate potential unintended consequences of the use of standing orders, including overtesting and spillover effects on non-target patients. Medical studies demonstrate that the use of standing orders decreases average ED length of stay (LOS) for target patients. Our research shows the importance of investigating the impact of standing orders on the ED as a whole.

Ambulance location in Emergency Medical Services (EMS) is a widely studied problem requiring efficient resource allocation within budgetary constraints. The literature has focused on enhancing EMS performance with limited attention given to their economic performance. This study addresses EMS performance with an emphasis on budget constraints by revising three coverage maximization models: the time-dependent Maximum Expected Coverage Location Problem (time-dependent MEXCLP), the multi-period Double Standard Model (mDSM), and the multi-period Queueing Maximal Availability Location Problem (Q-MALP-M2). These models are adapted to incorporate ambulance types, multi-period relocation, and budget constraints related to costs associated with ambulance station openings, ambulance acquisition, transport, and multi-period relocation. The revised models, along with two hybrid models (model 1 and model 2), were evaluated and compared using a discrete-event simulation model based on three key performance indicators: 1) coverage, 2) waiting time, and 3) time to arrive at the hospital. Additionally, the study investigates ambulance sharing as a policy to enhance EMS performance, wherein a single ambulance serves two patients whenever feasible. The study uses data from the Fez-Meknes region in Morocco, collected in 2021. Results indicate that hybrid model 1 outperformed the other models in most scenarios, as it allows for the decentralization of ambulances by investing the allocated budget in constructing new ambulance stations and acquiring new ambulances, contrasting with the other models that allocate almost the entire budget to purchasing new ambulances. Furthermore, the findings reveal that ambulance sharing significantly improves EMS performance, particularly under tightening budgetary restrictions and increasing demand; however, the benefits of ambulance sharing diminish as the allocated budget increases.

Benchmark efficiency analysis in public health typically focuses on hospitals rather than primary care providers. Data Envelopment Analysis (DEA) is widely used to assess resource efficiency among decision-making units (DMUs). However, traditional DEA struggles to differentiate between efficient units and is sensitive to the selection of inputs and outputs. Methods like super-efficiency and cross-efficiency address some of these limitations but often exclude outliers and may overlook efficiency related to specialisation. DEA Visualisation integrates DEA with multivariate statistical methods allowing for the identification of inefficiency sources and specialisation patterns without losing discriminatory power or removing extreme cases from the sample. This study analyses 82 public primary health centres in Madrid serving senior citizens in 2018. The findings reveal inefficiencies such as a preference for prescribing specific rather than generic drugs, increasing public health costs. Additionally, two extreme cases (outliers or mavericks) were identified as having high infrastructure costs and disproportionate staffing. Redistributing patients from overcrowded centres could enhance efficiency, while centres focused on preventive care showed greater cost-effectiveness, particularly in reducing prescription costs.

Waiting list models can support improved strategic management of elective hospital care through estimating possible performance impacts resulting from different demand and capacity related interventions. Single-compartment models have previously been used to model the referral 'inflow' and treatment 'outflow' onto a waiting list, with some also considering the outflow of patients reneging from the waiting list before treatment. The conceptual simplicity of these models promotes scalability through aligning to various waiting list problems and routine data sources. However, these single-compartment models are only able to model waiting list size, and not waiting times. To address this, we extend the single-compartment model with reneging to consider a multi-compartment model, where each compartment represents the number of individuals awaiting treatment for progressively longer periods of time. This problem is formulated in discrete time and solved through a series of difference equations. Open-source code for implementing the model is made freely available. To illustrate the versatility of the methodology, the model is calibrated using routine data for the total England NHS waiting list as of year-end 2023 and used to project various scenarios over the following two years to year-end 2025. Model validation is performed through backtesting (running the model on past unseen data), with 0.4% and 4.7% MAPE attained on six and twelve month windows respectively.

Platelet products are vital in the blood transfusion system since they are used for treating serious diseases such as cancer. They are expensive products (C$504 per unit) with a short shelf life of five to seven days. Since platelet demand is uncertain and highly variable, platelet inventory management is a challenging task. In this work, we propose a data-driven inventory management model for platelet products that incorporates demand forecasts in the inventory management process. The proposed model uses forecast-dependent target inventory levels to determine an ordering policy that has a goal of minimizing both the shortage and wastage. The data used in this study is a large clinical dataset of daily platelet transfusions for a centralized blood distribution centre for four hospitals in Hamilton, Ontario, spanning from 2016 to 2018. Experimental results show that our proposed policy performs well in minimizing shortages and wastages and that larger forecast errors can be tolerated as the system scales (for example as a result of demand aggregation and inventory pooling). We also perform sensitivity analysis to provide a more in-depth study of the proposed model. In particular, we suggest that by incorporating demand forecasts in the inventory management model, ordering less frequently than daily is feasible.

Trauma centers (TCs) play a crucial role in improving patient safety of severely injured individuals, but require substantial financial resources to operate effectively. TCs in low-insured areas are particularly at risk of being confronted with financial deficits, and a threat of closure, due to the inability to recover costs from uncompensated care. While some states in the US provide financial subsidies to support these centers, the diversity of state subsidy policies and their impacts on TC financial viability are poorly understood. To address this, we introduce a generalized subsidy distribution formula that incorporates key components from various state policies. Based on that, we further propose a TC Financial Evaluation Model that employs Monte Carlo simulation to assess the effects of different subsidy policies along three proposed metrics. Utilizing realistic data from multiple US states and national insurance statistics, we conduct a comprehensive experimental study. Our findings suggest that the financial performance of TCs could be affected by the total subsidy amount, the Uninsured level within the Trauma Service Area (TSA), and the specific subsidy distribution policy employed. This research provides trauma decision-makers a quantitative tool to evaluate, compare, and design subsidy policies tailored to their unique demographic and economic contexts, potentially leading to a more standardized approach to mitigate existing policy disparities across states.

During the initial phase of a pandemic outbreak, the rapid increase in the number of infected patients leads to shortages of medical resources for both pandemic-related and non-pandemic (ordinary) patients. It is crucial to efficiently utilize limited existing resources and strike a balance between controlling the pandemic and sustaining regular healthcare system operations. To tackle this challenge, we introduce and investigate the problem of optimizing the location of designated hospitals, reallocating beds within these hospitals, and assigning different types of patients to these hospitals. Designated hospitals isolate pandemic-related patients from ordinary patients to prevent cross-infection. Moreover, isolation beds can be converted into ordinary beds and vice versa. Considering the stochasticity and evolving nature of the pandemic, we formulate this problem as a multi-stage stochastic programming model, integrating a compartmental model with time-varying random parameters to enable dynamic resource allocation as the pandemic progresses. The model is then solved by a data-driven rolling horizon solution approach. We illustrate the effectiveness of our model using real data from the COVID-19 pandemic. Compared with two other approaches, our model demonstrates superior performance in controlling the spread of the pandemic while addressing the needs of both pandemic-related and ordinary patients. We also conduct a series of experiments to uncover managerial insights for policymakers to better utilize existing resources in response to pandemic outbreaks. Results indicate that admitting as many pandemic-related patients as possible during the initial phases of the outbreak can effectively flatten the pandemic peaks and alleviate strain on the healthcare system.

The COVID-19 pandemic had a profound impact on the tertiary sector, particularly in healthcare, which faced unprecedented demand despite the existence of limited resources, such as hospital beds, staffing resources, and funding. The magnitude and global scale of this crisis provide a compelling incentive to thoroughly analyse its effects. This study aims to identify best practices within the Portuguese national healthcare service, with the goal of improving preparedness for future crises and informing policy decisions. Using a Benefit-of-the-Doubt (BoD) approach, this research constructs composite indicators to assess the pandemic's impact on the Portuguese public hospitals. The study analyzes monthly data from 2017 to May 2022, highlighting critical trends and performance fluctuations during this period. The findings reveal that each COVID-19 wave led to a decline in hospital performance, with the first wave being the most severe due to a lack of preparedness. Furthermore, the pandemic worsened the disparities among examined hospitals. Pre-pandemic top performers in each group improved their performance and were more consistently recognized as benchmarks, with their average benchmark frequency increasing from 66.5% to 83.5%. These top entities demonstrated greater resilience and adaptability, further distancing themselves from underperforming hospitals, which saw declines in both performance scores and benchmark frequency, widening the performance gap. The superior performance of top entities can be attributed to pre-existing strategic tools and contextual factors that enabled them to withstand the pandemic's challenges more effectively. HIGHLIGHTS: • The pandemic aggravated the differences between the hospitals examined. • The top-performing entities further distanced themselves from the remaining entities after the pandemic • Entities considered benchmarks before the pandemic remained the same, and became even more consistent during the pandemic. • The top-performing entities achieved higher scores than their pre-pandemic performance levels. • Benchmarking models for composite indicators with diverse decision-making preferences, and treatment of imperfect knowledge of data.

Healthcare efficiency is a critical concern for medical institutions, particularly in balancing service delivery and quality outcomes. This study aims to estimate the medical service efficiency (MSE) and medical quality efficiency (MQE) of 21 county-level and city-level medical institutions in Taiwan over the period from 2015 to 2019. We introduce a novel chance-constrained network Data Envelopment Analysis (DEA) model that integrates the advantages of the range directional measure (RDM), directional distance function (DDF), and enhanced Russell efficiency measure (ERM) to evaluate these efficiencies. Our findings reveal that non-metropolitan areas outperform metropolitan areas in MSE, while metropolitan areas excel in MQE. Furthermore, a truncated regression model is employed to identify the factors influencing MSE and MQE. The results indicate that the number of labor force and county or city attributes significantly negatively impact MSE, whereas these factors positively influence MQE. This study provides targeted optimization suggestions for medical institutions aiming to improve their operational and quality efficiencies.