Health Care Management Science最新文献

{"title":"Impact of subsidy policies on the financial status of trauma centers.","authors":"Lin Lin, Pratik J Parikh","doi":"10.1007/s10729-025-09701-1","DOIUrl":"10.1007/s10729-025-09701-1","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"160-177"},"PeriodicalIF":2.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143977581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blood platelet inventory management: Incorporating data-driven demand forecasts.","authors":"Maryam Motamedi, Jessica Dawson, Na Li, Douglas Down","doi":"10.1007/s10729-025-09706-w","DOIUrl":"10.1007/s10729-025-09706-w","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"191-206"},"PeriodicalIF":2.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144063504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Location planning, resource reallocation and patient assignment during a pandemic considering the needs of ordinary patients.","authors":"Yu Lu, Shaochong Lin, Zuo-Jun Max Shen, Junlong Zhang","doi":"10.1007/s10729-025-09703-z","DOIUrl":"10.1007/s10729-025-09703-z","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"234-258"},"PeriodicalIF":2.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144004525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Looking for the crystal ball in unscheduled care: a systematic literature review of the forecasting process.","authors":"Mingzhe Shi, Bahman Rostami-Tabar, Daniel Gartner","doi":"10.1007/s10729-025-09711-z","DOIUrl":"https://doi.org/10.1007/s10729-025-09711-z","url":null,"abstract":"<p><p>The ability to accurately forecast unscheduled care needs is of paramount importance for decision making in healthcare operations, ensuring a continuous and high-quality level of care. In this work, we provide a literature review of 156 research articles of forecasting applications with special focus on care services that are not scheduled in advance such as emergency departments. Our paper presents two key contributions. Firstly, we propose a novel framework designed to characterize the application of forecasting process across various unplanned healthcare services. Our taxonomy facilitates the detection, decomposition, and categorization of forecasting processes, enhancing the understanding of their deployment in different unscheduled care settings. Secondly, we conduct a comprehensive literature review based on a systematic search, critically analyzing the state of forecasting research in unscheduled care services and identifying key research gaps. We explore forecasting problems in depth, examining their purpose, the various methodologies used, the rigor used in generating and evaluating forecasts, and the reproducibility of results, all within the context of the proposed framework. By consolidating the current state of the art, this paper provides valuable insights to both healthcare professionals and academics regarding the effective application of forecasting in unscheduled care services. Finally, it serves as a roadmap for identifying major research gaps and outlines an agenda for future investigations.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144127434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A two-phase method for layout optimization: The case of a referral cancer center in Latin America.","authors":"Anderson Coutinho, Rafael Morais, Anand Subramanian, Matheus Silva, Oscar Porto, Luciano Costa","doi":"10.1007/s10729-025-09710-0","DOIUrl":"https://doi.org/10.1007/s10729-025-09710-0","url":null,"abstract":"<p><p>This work proposes a two-phase approach for optimizing the layout of a referral cancer center in Latin America, which results from a collaboration between a university, in Brazil, and a consultancy company. The objective of the problem is to minimize the total transportation cost of patients, medical/non-medical staff, materials, and equipment. In the first phase, an integer programming model is used to assign departments to floors in such a way that the vertical transportation cost between departments is reduced. In the second phase, a heuristic method is employed to determine the layout of the blocks across a given floor, when applicable, while minimizing the transportation cost within the floor. Process mining is employed to gather data associated with the movement flow between rooms and departments within the unit. The developed approach was used by the managers of the cancer center to help design a new hospital building, and the layout produced by the optimization procedure was compared with the initial layout originally built by the architects of the hospital. The results obtained demonstrate that our method was capable of significantly reducing both vertical ( <math><mrow><mo>-</mo> <mn>19.7</mn> <mo>%</mo></mrow> </math> ) and horizontal ( <math><mrow><mo>-</mo> <mn>22.7</mn> <mo>%</mo></mrow> </math> ) transportation costs within the hospital.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144127431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A decomposition-based approach for multi-level appointment planning and scheduling.","authors":"Tine Meersman, Broos Maenhout","doi":"10.1007/s10729-025-09707-9","DOIUrl":"https://doi.org/10.1007/s10729-025-09707-9","url":null,"abstract":"<p><p>We study the scheduling of appointments for population-based breast cancer screening, considering different patient types in view of their stochastic no-show behaviour and service duration. The associated multi-level problem under study comprises both tactical planning decisions, assigning patients in advance to a mammography machine at a dispersed unit and appointment day, and operational scheduling decisions, stipulating the appointment time for patients. To mitigate the impact of operational variability, performance is safeguarded by optimising the minimum performance associated with defined chance constraints relative to the minimum number of performed screenings and the maximum patient wait time, resource idle time and overtime. We develop a decomposition method that iterates between tactical and operational decision levels with feedback loops. The tactical problem is reformulated as a deterministic mixed-integer quadratic-constrained programming problem and solved via a heuristic that defines a promising solution region based on problem-specific estimates. The operational problem is solved via Sample Average Approximation and decomposition of patient sequencing and appointment time assignment decisions. Computational results show that the developed decomposition-based procedure with feedback and the phase-specific methodologies are superior in terms of time and solution quality compared to alternative methods.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144110306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the performance of Portuguese public hospitals before and during COVID-19 outbreak, with optimistic and pessimistic benchmarking approaches.","authors":"Guilherme Mendes Vara, Marta Castilho Gomes, Diogo Cunha Ferreira","doi":"10.1007/s10729-024-09693-4","DOIUrl":"10.1007/s10729-024-09693-4","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"1-27"},"PeriodicalIF":2.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11976781/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142739415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A chance-constrained network DEA approach for evaluating medical service and quality efficiency: a case study of Taiwan.","authors":"Shiu-Wan Hung, Kai-Chu Yang, Wen-Min Lu, Minh-Hieu Le","doi":"10.1007/s10729-025-09700-2","DOIUrl":"10.1007/s10729-025-09700-2","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"99-118"},"PeriodicalIF":2.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143515441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing vaccination campaign strategies considering societal characteristics.","authors":"Serin Lee, Zelda B Zabinsky, Shan Liu","doi":"10.1007/s10729-025-09696-9","DOIUrl":"10.1007/s10729-025-09696-9","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"84-98"},"PeriodicalIF":2.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143382295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inter-organizational pooling of NICU nurses in the Dutch neonatal network: a simulation-optimization study.","authors":"Gréanne Leeftink, Kimberley Morris, Tim Antonius, Willem de Vries, Erwin Hans","doi":"10.1007/s10729-025-09697-8","DOIUrl":"10.1007/s10729-025-09697-8","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"64-83"},"PeriodicalIF":2.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11976774/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143255508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}