Health Care Management Science最新文献

{"title":"Public health interventions for developing resilience to contagious diseases: a system dynamics approach.","authors":"Hajar Sadegh Zadeh, Amir Hossein Ansaripoor, Md Hossan Maruf Chowdhury, Ali Haghparast","doi":"10.1007/s10729-025-09731-9","DOIUrl":"https://doi.org/10.1007/s10729-025-09731-9","url":null,"abstract":"<p><p>Contagious diseases severely impact health systems and economies, with close contact leading to further spread and fatalities. This paper examines the effects of government interventions on controlling such diseases. Key interventions include media isolation of susceptible individuals, effective quarantining of infected persons, and vaccination. A system dynamics approach models the complexities of government interventions in coronary conditions. We used the SEIR (Susceptible, Exposed, Infected, and Recovered) model and developed a new model to address its shortcomings for a new virus. Resilience actions were defined and plotted based on the emergency management cycle phases: Prevention, Preparedness, Response, and Recovery. The model can be applied to any contagious disease worldwide. We calibrated the model using data from sources like the World Health Organization (WHO) and Centers for Disease Control (CDC), and validated it against official and historical data. A sensitivity analysis was conducted based on various resilience strategies: Isolation Rate Slope, Isolation Efficiency, Minimum Isolation Rate, Quarantine Portion, Quarantine Transmission, Vaccination Rate, and Media Rate Slope. The study identifies key conditions for controlling outbreaks: achieving rapid isolation with a minimum rate above 50% and efficiency above 95%, rapid detection and quarantine above 90% with efficiency over 92%, and an optimal contact rate below 0.2, achieved with a media rate slope of 0.005 and vaccination rate above 90%. These measures can control the disease within 455 days or less.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145238458","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":"Optimal capacity planning for long-term care facilities considering patients' gender, language, and age group.","authors":"Ghazal Khalili, Mohsen Zargoush, Kai Huang","doi":"10.1007/s10729-025-09717-7","DOIUrl":"https://doi.org/10.1007/s10729-025-09717-7","url":null,"abstract":"<p><p>Long-term care facility networks in Canada face significant challenges in balancing demand and capacity, a problem exacerbated by rising demand. In other words, the growing elderly population is escalating the need for long-term care resources. To address this issue, this study proposes a Mixed-Integer Linear Programming model based on the current standing of the long-term care system in Ontario, a representative case for considering varied patient supports. The proposed model simultaneously optimizes the timing and location of constructing new long-term care facilities while dynamically adjusting each facility's capacity, including human resources and beds. Moreover, patient assignments are optimized based on their demand region, gender, language, and age group over a finite time horizon. The model incorporates multiple constraints to accommodate patients' gender and language, addressing language barriers, alleviating feelings of loneliness, and aligning with Canada's commitment to inclusive care. Additionally, it considers patient journeys by incorporating age groups and assigning patients from different demand regions in an equitable manner through the geographical equity constraint. To validate our proposed model, we conduct a case study on the existing network in Hamilton, Ontario. An extensive set of numerical analyses is executed to provide insights into the problem. Most importantly, the results demonstrate that the model effectively optimizes facility placement and patient allocation while significantly reducing un-assignment and misassignment rates. Specifically, the results indicate that over 88% of patient demand can be accommodated annually throughout a five-year planning horizon. In addition, patients can be assigned based on language and gender with marginal additional costs. Lastly, operational costs constitute the largest share of total expenditures, whereas misassignment costs account for the smallest proportion.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145232491","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":"Enhancing patient accessibility of primary care: the redesign of Italian territorial medicine.","authors":"Antonio Diglio, Chiara Morlotti, Giuseppe Bruno, Mattia Cattaneo, Stefano Paleari, Carmela Piccolo","doi":"10.1007/s10729-025-09721-x","DOIUrl":"https://doi.org/10.1007/s10729-025-09721-x","url":null,"abstract":"<p><p>Ensuring widespread accessibility of healthcare services is a crucial policy objective. Accordingly, the Italian National Recovery and Resilience Plan (NRRP) has prioritized territorial medicine, channeling post-pandemic investments toward the restructuring of primary care services. A notable change is the establishment of Community Healthcare Centers (CHCs). This paper investigates how CHCs contribute to the accessibility of healthcare in urban and rural areas. By leveraging a comprehensive dataset of general practitioners' availability and estimating future demand-and-supply scenarios, we examine the impact of CHCs under two different capacity allocation strategies. Strategy 1-Capacity expansion-involves allocating additional service hours of general practitioners to CHCs in order to maximize accessibility. Strategy 2-Capacity redistribution-accounts for the persistent shortage of healthcare professionals faced by Italy in the recent years by reallocating a portion of general practitioners' current services from their existing workplace locations to CHCs. Our results indicate that CHCs have the potential to maintain current accessibility levels and also enhance them in the years to come. Moreover, we demonstrate that simply redistributing the current capacity can improve future accessibility. Finally, we show that a mix of the capacity expansion and redistribution strategies (Strategy 3) can maximize accessibility in the future, limiting the need for new professional staff.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225408","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":"County-level mobility and sociopolitical context in the spread of COVID-19 during spring 2020.","authors":"Chris Parker, Jorge Mejia","doi":"10.1007/s10729-025-09722-w","DOIUrl":"https://doi.org/10.1007/s10729-025-09722-w","url":null,"abstract":"<p><p>The implementation of social distancing policies is key to reduce the spread of the recent COVID-19 pandemic and future pandemics. However, their effectiveness ultimately depends on human behavior. For example, in the United States, compliance with social distancing policies widely varied in Spring 2020. What factors were associated with the observed variability in behavioral compliance with the policies? Utilizing detailed county-level data, we estimate the association between human mobility and the growth rate of COVID-19 cases across approximately 3,100 U.S. counties from January 1, 2020 to June 20, 2020. In addition, using data from U.S. presidential elections we measured how the association between mobility and COVID-19 growth rate varied as a function of county voting pattern. Our results generalize previous reports in finding a significant association between political leaning and the COVID-19 growth rate. These results highlight how it might be beneficial to consider political orientation when building models of the multivariate relationships between the spread of pandemics and public health policies intended to curb the expansion of the pandemic.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225437","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 descriptive investigation of the impact of statewide distribution policies and consumer vulnerabilities on COVID-19 vaccination in the united States.","authors":"Kathleen Iacocca, Beth Vallen, Alicia Strandberg, Laura Meinzen-Dick","doi":"10.1007/s10729-025-09727-5","DOIUrl":"https://doi.org/10.1007/s10729-025-09727-5","url":null,"abstract":"<p><p>This research leverages data from various disparate sources to examine how state-level policy distribution decisions and local, county-level population vulnerability factors likely to hinder vaccination influenced COVID-19 vaccination efforts across the United States. Unlike other nations that coordinated their responses at a national level, this study uses U.S. states and counties as individual units of analysis. This approach allows for an assessment of which policies and population attributes were most impactful in driving vaccination and ensuring efficient and equitable distribution among citizens. By focusing on the diverse strategies employed by different states in terms of (1) defining the entity responsible for distribution policy, (2) determining the groups eligible for vaccination, and (3) the timing for communication of distribution plans for vaccination, this descriptive investigation sheds light on the effectiveness of state-level interventions and contributes to a deeper understanding of how to manage large-scale public health initiatives. By identifying successful strategies and potential pitfalls, the study provides a roadmap for responding to future pandemics, ensuring that vaccination efforts can be swiftly and fairly implemented to protect public health.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225428","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 stochastic programming model for trauma hospital network expansion considering rural communities and COVID-19.","authors":"Eduardo Pérez, Alakshendra Joshi, Sabhasachi Saha, Francis A Méndez-Mediavilla","doi":"10.1007/s10729-025-09719-5","DOIUrl":"https://doi.org/10.1007/s10729-025-09719-5","url":null,"abstract":"<p><p>Trauma care services are a vital part of all healthcare-based networks as timely accessibility is important for citizens. Trauma care access is even more relevant when unexpected events, such as the COVID-19 pandemic, overload the capacity of the hospitals. Research literature has highlighted that access to trauma care is not even for all populations, especially when comparing rural and urban groups. Traditionally, the focus in trauma systems was on the designation and verification of individual hospitals as trauma centers, rather than on the overall configuration of the system. Recognition of the benefits of an inclusive trauma system has precipitated a more integrated approach. The optimal geographic configuration of trauma care centers is key to maximizing accessibility while promoting the efficient use of resources. This research reports on the development of a two-stage stochastic optimization model for geospatial expansion of a trauma network in a delimited area. The stochastic optimization model recommends the siting of new trauma care centers according to the geographic distribution of the injured population. The model has the potential to benefit both patients and institutions, by facilitating prompt access and promoting the efficient use of resources. The findings indicate that the model significantly improves trauma care coverage, particularly in rural counties, thereby enhancing equitable access to critical healthcare services.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225375","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":"The crucial role of explainable artificial intelligence (XAI) in improving health care management.","authors":"Arne Johannssen, Nataliya Chukhrova","doi":"10.1007/s10729-025-09720-y","DOIUrl":"https://doi.org/10.1007/s10729-025-09720-y","url":null,"abstract":"<p><p>This current opinion explores the transformative potential of explainable artificial intelligence (XAI) for health care management systems. While AI has already demonstrated substantial benefits in clinical decision-making, operational efficiency and patient outcomes, its adoption is often hindered by the lack of transparency in AI-driven decision-making. XAI bridges this gap by providing interpretability, thereby increasing trust between policy-makers, clinicians, administrators and patients. However, despite promising examples, the explicit integration of XAI remains underexplored in health care management research. This current opinion therefore aims to emphasize the crucial role of XAI in improving health care management and to position it as an important topic for advancing the field, with Health Care Management Science (HCMS) playing a leadership role in fostering this development.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145199104","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":"Synergizing artificial intelligence and operations research for advancements in biomanufacturing.","authors":"Tugce Martagan, Tinglong Dai","doi":"10.1007/s10729-025-09725-7","DOIUrl":"https://doi.org/10.1007/s10729-025-09725-7","url":null,"abstract":"<p><p>Harnessing the synergy between artificial intelligence (AI) and operations research (OR) helps drive efficiency, safety, and innovation in biomanufacturing. AI offers predictive capabilities, while OR represents the pinnacle of prescriptive analytics. AI and OR complement each other by offering structured, interpretable, and verifiable solutions to complex operational challenges. In this commentary, we reflect on how to realize the full potential of AI-OR implementations in biomanufacturing. We elaborate on recent university-industry partnerships demonstrating these benefits and propose a roadmap for AI-OR integration in biomanufacturing.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174799","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 operating room scheduling through multi-level learning and column generation: a novel hybrid approach.","authors":"Rong Zhao, Yaqin Quan, Guangrui Fan","doi":"10.1007/s10729-025-09723-9","DOIUrl":"https://doi.org/10.1007/s10729-025-09723-9","url":null,"abstract":"<p><p>Operating room (OR) scheduling is a critical challenge in healthcare, directly impacting patient outcomes and hospital efficiency. Traditional methods often struggle with the complex, multi-level constraints and uncertainties inherent in OR scheduling, such as resource limitations, variable surgery durations, and emergency cases. This study aims to develop a novel hybrid framework that optimizes OR scheduling by integrating multi-level optimization with reinforcement learning and column generation techniques. The proposed framework decomposes the OR scheduling problem into strategic, tactical, and operational levels, enabling focused optimization at each layer while ensuring cohesive decision-making across the hierarchy. Reinforcement learning guides the column generation process, learning policies that identify promising scheduling options to enhance solution quality and computational efficiency. Robust uncertainty handling mechanisms are incorporated to manage variability in surgery durations and resource availability without compromising tractability. Experiments were conducted using three years of real-world data from Shanxi Provincial People's Hospital, complemented by large-scale synthetic datasets to evaluate scalability and robustness of the framework. The framework demonstrates meaningful improvements in key operational metrics compared to traditional approaches. Analysis of three years of implementation shows consistent enhancements in operational efficiency, including a reduction in average patient waiting time by 15.8% (from 10.1 to 8.5 days), an increase in OR utilization by 5.4 percentage points (from 73.8% to 79.2%), and improved workload balance among surgeons. The framework maintains robust performance under uncertainty, achieving a 92.5% feasibility rate and reducing schedule disruptions by 26.2%. The proposed hybrid framework offers a practical and scalable solution for optimizing OR scheduling, demonstrating improvements in healthcare delivery and operational performance in real hospital environments. By effectively balancing multiple operational objectives while handling practical constraints and uncertainties, the framework provides a viable approach for healthcare systems seeking incremental yet sustainable improvements in efficiency and patient care.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174773","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":"Innovations in early detection of chronic non-communicable diseases among adolescents through an easy-to-Use AutoML paradigm.","authors":"Nevena Rankovic, Dragica Rankovic, Igor Lukic","doi":"10.1007/s10729-025-09718-6","DOIUrl":"https://doi.org/10.1007/s10729-025-09718-6","url":null,"abstract":"<p><p>In this research, we present an interpretable AutoML approach for the early diagnosis of hypertension and hyperinsulinemia among adolescents, conditions that are critical to identify during these formative years due to their requirement for lifelong care and monitoring. The dataset, collected from 2019 to 2022 by Serbia's Healthcare Center through an observational cross-sectional study, posed challenges common to medical datasets, including imbalances, data scarcity, and a need for transparent, explainable predictive models. To counter these issues, we utilized three AutoML frameworks - AutoGluon, H2O, and MLJAR - in conjunction with a Tabular Variational Autoencoder (TVAE) to synthetically augment the data points, Prinicipal Component Analysis (PCA) for dimensionality reduction, and SHapley Additive exPlanations (SHAP) and Permutation feature importance analyses to extract insights from the results. AutoGluon outperformed the others on the original dataset, delivering better results with weighted ensemble models for both conditions under a 12-minute budget-time constraint and maintaining all evaluation metrics below a 4% threshold, all without the need for further scaling or calibration in the experimental setup. Our research underscores the broad applicability of the current AutoML paradigm, highlighting its particular benefits for the healthcare domain and diagnostics, where such advanced tools can enhance patient care.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144951809","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}