Health Care Management Science最新文献

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.

Vaccine hesitancy continues to be a public health challenge. This study explores the dynamic interplay between disease transmission, evolving vaccination opinions, and targeted vaccination campaigns. Using a numerical experiment calibrated to the COVID-19 epidemic in King County, WA, during 2023, we optimize vaccination campaigns across various demographics. Our findings suggest that vaccination campaigns are most effective in societies with medium vaccine hesitancy, with optimal outcomes achieved by focusing on the 18-34 age group in the most densely populated regions. In societies with low hesitancy, campaigns may be unnecessary, and resources should target rural areas and the 0-17 age range to maximize impact. In high hesitancy societies, campaigns are ineffective. In such cases, efforts should focus on reducing vaccine risk perceptions. This research advances the understanding of dynamic behavioral responses to vaccination campaigns through evolutionary game theory, moving beyond models that assume static vaccination behavior. By employing a demographic-based networked compartmental model, it derives actionable and interpretable campaign strategies, providing valuable guidance for real-world implementation.

Neonatology care, the care for premature and severely ill babies, is increasingly confronted with capacity challenges. The entire perinatal care chain, including the Neonatal Intensive Care Unit (NICU), operates at high occupation levels. This results in refusals, leading to undesirable transports to other centers or even abroad, which affects quality of care, length of stay, and safety of these babies, and places a heavy burden on patients, their families, and involved caregivers. In this work we assess the improvement potential of network collaboration strategies that focus on reducing the number of patient transports, by allowing flexible deployment of nurses over the existing NICUs to match short-term changes in patient demand. We develop a discrete event simulation with an integrated optimization module for shift allocation and transfer optimization. A case study for the Dutch national NICU network, involving 9 NICU locations and current transport of 15% of all NICU patients in case of no flexible deployment, shows the potential of transporting staff instead of patients: About 70% of patient transports can be eliminated in case of 15-50% capacity sharing, and about 35% of nationwide transports is eliminated with up to 15% capacity sharing in the Dutch's main conurbation area only.

As social and economic conditions are key determinants of HIV, the United States 'National HIV/AIDS Strategy (NHAS)', in addition to care and treatment, aims to address mental health, unemployment, food insecurity, and housing instability, as part of its strategic plan for the 'Ending the HIV Epidemic' initiative. Although mechanistic models of HIV play a key role in evaluating intervention strategies, social conditions are typically not part of the modeling framework. Challenges include the unavailability of coherent statistical data for social conditions and behaviors. We developed a method, combining undirected graphical modeling with copula methods, to integrate disparate data sources, to estimate joint probability distributions for social conditions and behaviors. We incorporated these in a national-level network model, Progression and Transmission of HIV (PATH 4.0), to simulate behaviors as functions of social conditions and HIV transmissions as a function of behaviors. As a demonstration for the potential applications of such a model, we conducted two hypothetical what-if intervention analyses to estimate the impact of an ideal 100% efficacious intervention strategy. The first analysis modeled care behavior (using viral suppression as proxy) as a function of depression, neighborhood, housing, poverty, education, insurance, and employment status. The second modeled sexual behaviors (number of partners and condom-use) as functions of employment, housing, poverty, and education status, among persons who exchange sex. HIV transmissions and disease progression were then simulated as functions of behaviors to estimate incidence reductions. Social determinants are key drivers of many infectious and non-infectious diseases. Our work enables the development of decision support tools to holistically evaluate the syndemics of health and social inequity.

Ambulances must be strategically placed to ensure timely patient care and save lives. The allocation problem considered in the current paper optimally distributes a fixed number of ambulances over predetermined bases with limited capacity. Ambulance allocation problems are usually solved through historical demand. In such cases, researchers process call record data that is shared by ambulance service providers. This paper proposes an alternative demand metric, namely the meters of covered road. Road network information is widely and publicly available, making it easily accessible. We demonstrate for a real ambulance region that the road coverage demand metric performs similarly to the historical call record metric in the case of static allocation, and that it outperforms when dynamic ambulance management is used.