Health Care Management Science最新文献

{"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":"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":"591-607"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12743688/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can past variants of SARS-CoV-2 predict the impact of future variants? Machine learning for early warning of US counties at risk.","authors":"Kevin B Smith, Siqian Shen, Brian T Denton","doi":"10.1007/s10729-025-09728-4","DOIUrl":"10.1007/s10729-025-09728-4","url":null,"abstract":"<p><p>In this paper, we determine whether machine learning (ML) models created using data from the novel SARS-CoV-2 Alpha variant can prospectively predict county-level incidence of emerging variants, validated using data of the Omicron variant. We first select publicly-available sociodemographic, economic, and health-related characteristics of 3140 United States (US) counties at the time of the confirmed early US outbreak of the novel SARS-CoV-2 virus in March 2020 for analysis. Our primary result is the set of US counties that experienced the upper quartile of population-adjusted Omicron variant incidence at certain period (e.g., 100 days) after Omicron variant's appearance in the US. We show more predictive results by incorporating additional data and features (e.g., human mobility) that can be acquired dynamically after the outbreak, to improve prediction accuracy at the cost of additional waiting time and effort. Towards the goal of decision support, we aim to prospectively evaluate our models' ability to classify and rank US counties at risk. We measure their classification performance using the area under receiver operating characteristic curve score with 95% confidence intervals. We further calculate the proportion of the top counties by Omicron incidence that our models correctly identify, and compare their score with those of individual county-level features that can serve as a heuristic predictive performance baseline. Our results show that ML algorithms predict county-level Omicron variant incidence with better performance than natural heuristics that decision makers might otherwise use. More generally, historical data from the first wave of a novel pandemic can help predict the incidence of future variants and strengthen state or federal pandemic response interventions. HIGHLIGHTS: Data-driven predictive models that capture patterns from early viral variants can support policymaking related to emerging viral variants. County-level sociodemographic, health, and economic characteristics are predictive of early COVID-19 outcomes in the United States (US). Machine learning models trained on early US county-level COVID-19 outcomes are additionally predictive of county-level SARS-CoV-2 Omicron variant outcomes. County-level machine learning models can be used as a critical policymaking tool given the inevitability of novel emerging viruses.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"738-758"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12743673/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145280091","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 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":"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":"608-621"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12743685/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing clinical and non-clinical risk management: A case study using ELECTRE Tri-nC.","authors":"Joana Lemos Alves, Miguel Alves Pereira","doi":"10.1007/s10729-025-09734-6","DOIUrl":"10.1007/s10729-025-09734-6","url":null,"abstract":"<p><p>Adverse events in healthcare continue to challenge hospital management practices, often resulting in avoidable patient harm and substantial financial costs. Despite technological progress and the availability of risk management tools, healthcare institutions still struggle to systematically monitor and evaluate risk dynamics over time. This study proposes a multi-criteria decision analysis framework based on the ELECTRE Tri-nC method to assess the evolution of clinical and non-clinical risks at Hospital da Luz Lisboa, a private Portuguese hospital. A panel of risk management experts evaluated twelve criteria across five years (2018-2022), enabling the classification of each quarter into one of five predefined risk categories. The model accommodates the non-compensatory nature of risk indicators and integrates expert-defined thresholds. Results reveal critical periods of heightened risk, underscoring the importance of analysing risk trends over time rather than focusing on isolated incidents. A stability analysis confirms the robustness of the weight structure and highlights the model's sensitivity to changes in the credibility threshold. Overall, the proposed approach provides healthcare decision-makers with a transparent and structured framework for retrospective risk analysis and supports the design of timely, targeted mitigation strategies. The methodology is adaptable to other hospital settings.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"824-841"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145400713","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":"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":"672-690"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12743686/pdf/","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":"OA","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":"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":"715-737"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12743681/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expanding modeling boundaries to design more resilient vaccine supply networks.","authors":"Donovan Guttieres, Carla Van Riet, Nico Vandaele, Catherine Decouttere","doi":"10.1007/s10729-025-09726-6","DOIUrl":"10.1007/s10729-025-09726-6","url":null,"abstract":"<p><p>The COVID-19 pandemic shed light on the fragility of today's public health systems and failure to sufficiently invest in preparedness. These shortcomings are observed in delays achieving timely, equitable, and sufficient access to life-saving vaccines when faced with erratic demand. This Current Opinion describes vaccine supply networks (VSNs) from a complex adaptive systems (CAS) lens, highlighting interactions between system elements and co-evolution with the environment in which they operate. More specifically, it shows how broadening the boundaries of VSNs reveals the high degree of complexity that leads to unexpected and emergent system behavior, especially when disease threats evolve over time and across geographies. A CAS lens allows for the design of improved management strategies to ensure continued performance of VSNs during both outbreak and inter-epidemic periods, thus contributing to sustained disease management. It points to ample opportunities for more integrated modeling across disciplines to capture inherent feedback loops that influence both VSNs and disease dynamics. Furthermore, it reveals how pandemic preparedness relies on a broader understanding of the mechanisms that drive outbreak prevention and control, beyond vaccines and their direct supply chains. Finally, it highlights the value of adaptive management to navigate inevitable future disruptions and associated uncertainties, overcoming limitations of typical risk-mitigation strategies based on prediction and control.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"583-590"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12743676/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145336777","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":"Foreword to the special issue: management science for pandemic prevention, preparedness, and response.","authors":"Hrayer Aprahamian, Vedat Verter, Manaf Zargoush","doi":"10.1007/s10729-025-09739-1","DOIUrl":"10.1007/s10729-025-09739-1","url":null,"abstract":"","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"572-574"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145774460","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":"Multi-objective dynamic prioritized routing and scheduling for home healthcare services with cooperating service providers.","authors":"Mert Parçaoğlu, F Sibel Salman, Ozgur M Araz","doi":"10.1007/s10729-025-09730-w","DOIUrl":"10.1007/s10729-025-09730-w","url":null,"abstract":"<p><p>In home healthcare service systems, each healthcare service provider (HSP) is assigned a list of patients to be visited at their homes. We focus on generating a daily patient visit plan that selects the patients to be visited according to their priorities and locations, and determines the route of each HSP. Additionally, we address unexpected urgent patients by solving an optimization problem involving all HSPs cooperating when an urgent patient visit request arises. This problem is formulated with multiple objectives in a lexicographic optimization framework. Two approaches have been implemented: a mixed integer programming model solved within a time limit (TL-MIP) and a Greedy Randomized Adaptive Search Procedure followed by Variable Neighborhood Search (GRASP+VNS). These approaches are compared in a case study that considers serving patients, with several performance metrics analyzed through extensive simulation experiments. The results indicate that the heuristic approach (GRASP+VNS) significantly reduces run times (by approximately 85% on the average overall instances) compared to the TL-MIP approach, while providing solutions that are not far from the TL-MIP approach in terms of the total priority of visited patients, the heuristic deviates at most 2% over different types of instances. Centralized planning with cooperation among two or three service providers reduced the total travel time by 30% and 45%, respectively, and decreased the number of postponed visits by 50% compared to the non-cooperation model.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"759-786"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145312775","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":"Positive and unlabeled learning from hospital administrative data: a novel approach to identify sepsis cases.","authors":"Justus Vogel, Johannes Cordier","doi":"10.1007/s10729-025-09733-7","DOIUrl":"10.1007/s10729-025-09733-7","url":null,"abstract":"<p><p>In positive and unlabeled (PU) learning problems, only positive examples are labeled. Unlabeled data contain both positive and negative examples. Studies show that positive examples of (secondary) diagnoses, and clinical conditions, such as sepsis, are present in unlabeled hospital administrative data, potentially distorting hospital reimbursement systems, and negatively affecting hospitals' revenue and profitability. We investigate whether PU learning is suitable for improving the quality of hospital administrative data. We train three models on 313,434 hospital cases using hospital cost features: two based on the two-step \"spy\" approach and one using a robust PU learning method. For model evaluation, we rely exclusively on positive examples due to the PU setting. To further assess model performance, we perform an external validity check: We relabel unlabeled sepsis cases, derive new sepsis rates, and compare them to those reported in medical record review studies. All models identify true positives well in unseen data. External validity checks show, however, that only the robust PU learner effectively discriminates between positives and negatives in the unlabeled data, yielding new sepsis rates within the range of sepsis rates reported in medical record review studies. PU learning can improve the quality of hospital administrative data, but its effectiveness depends strongly on the choice of learning approach and classifier. The output of a PU learner can potentially improve hospital reimbursement systems, hospital revenue and profitability management, and sensitivity analyses in healthcare management science, health economics, health services research, and disease surveillance.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"787-805"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12743714/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145388905","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}