Health Care Management Science最新文献

{"title":"Strategic analysis of heterogeneous fleet composition for aerial interhospital transport in underserved areas of Canada.","authors":"Joelle Cormier, Valérie Bélanger, Marie-Éve Rancourt","doi":"10.1007/s10729-025-09748-0","DOIUrl":"10.1007/s10729-025-09748-0","url":null,"abstract":"<p><p>Emergency aeromedical interhospital transfers are critical for ensuring timely access to specialized care in underserved regions. While emergency ground transportation has been extensively studied, strategic decisions regarding aeromedical fleet composition remain underexplored, despite their significant impact on response times, patient outcomes, and operational efficiency. In this study, we develop a discrete-event simulation model to analyze how fleet size, patient-carrying capacity, aircraft speed, and fleet mix influence overall performance. The conceptual model and its outputs were validated using real-world data from Évacuations Aéromédicales du Québec (EVAQ) and integrated into EVAQ’s fleet renewal planning process, demonstrating its practical relevance for decision-making. By incorporating demand patterns, network constraints, and both strategic and operational considerations, the model provided quantitative insights into service performance, particularly patient transfer times. For example, doubling the fleet from one to two aircraft reduces average transfer times by nearly 30%, whereas adding more than two yields only marginal gains; likewise, increasing capacity from two to four stretchers lowers transfer times for non-urgent patients by up to 18% with little effect on urgent cases. The results offer valuable guidance for aeromedical managers seeking to improve fleet planning and enhance access to specialized healthcare in underserved areas.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147316659","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":"Identifying the most influential employees in infectious disease spread using stochastic mixed integer linear programming optimization.","authors":"Mohadese Basirati, Saeed Najafi-Zangeneh, Mireille Batton-Hubert","doi":"10.1007/s10729-025-09744-4","DOIUrl":"10.1007/s10729-025-09744-4","url":null,"abstract":"<p><p>Managers aim to minimize financial losses during pandemics by preventing the spread of infections among employees. Identifying employees who are most likely to infect others is crucial for implementing effective preventive measures. To do so, the infectious among employees should be modelled at first. Secondly, the members who directly or indirectly infect the maximum number of other employees are identified. This study addresses this problem as an Influence Maximization (IM) problem using a Mixed Integer Linear Programming (MILP) for analytical optimization. The MILP optimization guarantees the global optimal solution. However, given the stochastic nature of influence, the IM problem is formulated as a stochastic optimization based on a limited number of scenarios. Therefore, the whole stochastic nature of the influence process may not be captured. It is of high importance to check whether the number of scenarios used in stochastic MILP is adequate. This study evaluates it by simulating the results with numerous scenarios to assess the gap between the objective function and the exact expected value. The proposed methodology is tested on real case studies from companies of varying sizes, including a small-scale company with twelve employees and a larger-scale company with more than thousand employees. The results discuss the efficiency and adequacy of the scenarios used in the stochastic MILP approach.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147316656","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 predictive factors of US hospital bankruptcy - a multi-model comparison.","authors":"Brad Beauvais, Zo Ramamonjiarivelo, C Scott Kruse, Lawrence Fulton, Ramalingam Shanmugam, Arvind Sharma, Aleksandar Tomic","doi":"10.1007/s10729-025-09750-6","DOIUrl":"10.1007/s10729-025-09750-6","url":null,"abstract":"<p><p>In response to the growing number of hospital bankruptcies across the United States, this study sought to develop a predictive and interpretable model tailored specifically to the healthcare industry. Utilizing a longitudinal dataset of 3,091 short-term acute care hospitals from 2008 to 2021, we evaluated and compared traditional bankruptcy prediction models-Altman's Z'', Ohlson's O-score, and Zmijewski's model-against a newly developed hospital-specific logistic regression model (BRKFSST). We incorporated over 30 financial and hospital-level variables, including quality indicators, ownership type, and market characteristics. Unlike prior models, ours lagged all unknowable variables to ensure true out-of-sample prediction. The BRKFSST model achieved strong performance, with an Area Under the Curve (AUC) of 81.8%, balanced accuracy of 72.2%, and a mean recall of 60.6% across multiple test/train splits, outperforming all benchmark models. Importantly, the model retained interpretability, allowing for the identification of key predictors such as labor compensation ratio, adjusted patient days, and quality ratings. These findings provide actionable insights for hospital leaders and policymakers to identify at-risk institutions and implement early interventions to prevent financial collapse and preserve access to care.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12924785/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146258017","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":"Optimization of tray inventory levels in hospitals from an integral perspective.","authors":"Hayo Bos, Gaspard Hosteins, Wick Wijnholds, Aleida Braaksma, Gréanne Leeftink","doi":"10.1007/s10729-025-09743-5","DOIUrl":"10.1007/s10729-025-09743-5","url":null,"abstract":"<p><p>Surgical instrument trays are crucial for any hospital’s surgical department; patients cannot undergo surgery if the right instruments are not available. It is therefore of great significance to provide a steady and sufficient supply of sterile medical devices to the surgery department, which can be guaranteed by having sufficient inventory of sterile trays. However, the value of surgical tray inventory is substantial, and given the pressure on the health care industry to reduce cost, a properly aligned tray inventory is of utmost importance. Choosing the appropriate tray inventory levels is hard due to the complex interplay between the surgical schedule and the sterilisation cycle. In this paper, we propose a practice-driven heuristic and a Discrete Event Simulation (DES) model to choose and evaluate the appropriate tray levels respectively. We propose a novel surgery schedule generation algorithm to account for the dependency in tray demand, which has not been addressed in literature thus far. Using a case study from our partnering hospital, we show that without compromising quality, the tray inventory can safely be reduced by 28%.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 1","pages":"7"},"PeriodicalIF":2.8,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12920321/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146226526","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":"Healthcare applications of 0-1 neural networks in prescriptive problems with observational data.","authors":"Vrishabh Patil, Kara K Hoppe, Yonatan Mintz","doi":"10.1007/s10729-025-09751-5","DOIUrl":"10.1007/s10729-025-09751-5","url":null,"abstract":"<p><p>A key challenge in medical decision making is learning treatment policies for patients with limited observational data. This challenge is particularly evident in personalized healthcare decision-making, where models need to take into account the intricate relationships between patient characteristics, treatment options, and health outcomes. To address this, we introduce prescriptive neural networks (PNNs), shallow 0-1 neural networks trained with mixed integer programming that can be used with counterfactual estimation to optimize policies in medium data settings. These models offer greater interpretability than deep neural networks and can encode more complex policies than common models such as decision trees. We show that PNNs can outperform existing methods in both synthetic data experiments and in a case study of assigning treatments for postpartum hypertension. In particular, PNNs are shown to produce policies that could reduce peak blood pressure by 5.47 mm Hg (p=0.02) over existing clinical practice, and by 2 mm Hg (p=0.01) over the next best prescriptive modeling technique. Moreover PNNs were more likely than all other models to correctly identify clinically significant features while existing models relied on potentially dangerous features such as patient insurance information and race that could lead to bias in treatment.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 1","pages":"6"},"PeriodicalIF":2.8,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12920400/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146226593","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 survey on optimization and machine learning-based fair decision making in healthcare.","authors":"Zequn Chen, Wesley J Marrero","doi":"10.1007/s10729-025-09749-z","DOIUrl":"10.1007/s10729-025-09749-z","url":null,"abstract":"<p><p>Unintended biases introduced by optimization and machine learning models (a core area of artificial intelligence) are of great interest to medical researchers and professionals. Bias in healthcare decisions can cause patients from vulnerable populations (e.g., racially minoritized, low-income, or living in rural areas) to have lower access to resources and inferior outcomes, exacerbating societal unfairness. In this paper, we present a systematic review of the literature regarding fair decision making in healthcare until April 2024. We screened 801 unique references, identifying 114 articles within the scope. In our review, we examine fair decision-making methodologies in healthcare by systematically identifying and categorizing biases within both data and models. Additionally, we present a range of fairness metrics drawn from different use cases and classify bias mitigation strategies into pre-processing, in-processing, and post-processing techniques. We provide a broad conceptual overview and practical illustrations of each approach. Moreover, we examine emerging bias mitigation technologies that, though not yet applied in healthcare, show substantial promise for future integration. Our review aims to increase awareness of fairness in healthcare decision making and facilitate the selection of appropriate approaches under varying scenarios.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 1","pages":"5"},"PeriodicalIF":2.8,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146165083","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":"Interpretable machine learning for personalized breast cancer screening recommendations.","authors":"Sean Berry, Berk Görgülü, Sait Tunc, Mucahit Cevik","doi":"10.1007/s10729-025-09746-2","DOIUrl":"10.1007/s10729-025-09746-2","url":null,"abstract":"<p><p>Breast cancer is the most common non-skin cancer and the second leading cause of cancer death in U.S. women. Early detection and timely intervention are thus critical in reducing breast cancer-related deaths. Existing literature for the design of personalized mammography screening is mainly concerned with modeling the problem as a partially observable Markov decision process, which are computationally difficult to solve. In this study, we propose a machine learning-based approach for identifying the personalized screening recommendations using medical history and associated risk factors for individual patients. We find that machine learning models could provide a high degree of accuracy at drastically reduced computational complexity. Furthermore, once trained to sufficient accuracy, we ascertain explainable insights into machine learning model decisions. These insights yield a set of actionable decision rules that healthcare providers could use to support informed patient screening decisions. Overall, our study showcases the potential of machine learning in providing accurate and actionable recommendations for breast cancer screening.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 1","pages":"4"},"PeriodicalIF":2.8,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146118811","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":"Did COVID-19 worsen the disparities among mental health patients at risk of exhibiting aggression in Ontario, Canada?","authors":"Somayeh Ghazalbash, Vedat Verter","doi":"10.1007/s10729-025-09745-3","DOIUrl":"10.1007/s10729-025-09745-3","url":null,"abstract":"<p><p>The COVID-19 pandemic has strained global health systems, exacerbating health disparities, especially among vulnerable groups. It has also worsened mental health, leading to increased rates of depression and anxiety. We study the impact of the COVID-19 pandemic on the prevalence of mental health episodes involving violence in Ontario, the largest province of Canada. We compare the dangerousness of mental health patients who needed hospitalization before and during/after the pandemic across different socio-demographic groups and geographic regions. This enables us to identify the vulnerable populations in this domain as well as the key factors associated with disparities among patients at risk of exhibiting aggression. We conducted a retrospective study from March 2017 to March 2023. The study involved 340,000+ observations from patients aged 15 and above admitted to mental health inpatient hospital wards in Ontario, Canada. We evaluated violent behavior using three mental health indicators, including the risk of harming others, hospital admissions due to threats or danger to others, and history of police intervention for violent behavior within the last 30 days. We also examined associated disparities across several social determinants of health through a combination of absolute rate analysis, logistic regression, stratified autoregressive integrated moving average models, and Oaxaca-Blinder decomposition. Our findings indicated a pre-existing and noteworthy increase in violent behavior among patients with mental health conditions after the onset of the pandemic. Males, young and middle-aged adults, unmarried individuals, and low-income demographics suffered from the widening gap. The disparities were most evident in urban areas, and less educated groups showed higher levels of violent behavior. Policy announcements, such as school closures, had a substantial impact on mental health disparities, resulting in lasting effects on mental well-being. The COVID-19 pandemic has worsened mental health disparities related to violence, necessitating targeted interventions and policies to improve mental health outcomes and reduce violence-related health inequities.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 1","pages":"3"},"PeriodicalIF":2.8,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146105400","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":"Impact of collaboration network on care costs: an integrated healthcare analysis.","authors":"Ji Wu, Jianna Wang, Doris Chenguang Wu, Xian Cheng","doi":"10.1007/s10729-025-09742-6","DOIUrl":"10.1007/s10729-025-09742-6","url":null,"abstract":"<p><p>While prior research on inpatient care costs has primarily focused on patient- and clinical-level factors, limited empirical attention has been given to how physician collaboration shapes cost outcomes. Few studies have examined this relationship using social network analysis at the micro level. This study investigates how collaboration networks influence care costs, the mechanisms through which these effects occur, and the moderating role of attending physicians' workload. The structure of collaboration networks determines how efficiently information is shared and decisions are made, which in turn influences healthcare costs. Physicians' centrality within the network impacts their ability to access information and facilitate knowledge transfer, with higher centrality promoting better collaboration, reducing redundancies, and improving decision-making. Using digital trace data from a hospital in China, we employed social network analysis to identify collaborative networks and fitted a log-linear model to examine the association between these networks and healthcare costs. The results demonstrate that degree and closeness centrality of the attending physicians are negatively correlated with hospitalization cost. In contrast, betweenness centrality was found positively correlated with hospitalization cost. Additionally, we find that centrality metrics help reduce diagnostic and treatment costs by enhancing information exchange and clinical decision-making. Furthermore, the workload of attending physicians significantly impacted the relationship between collaboration network centrality and care costs. Specifically, the combined effect of an attending physician's degree and workload has an additional negative impact on hospitalization costs. The interaction between betweenness centrality and workload was found to be positively correlated with hospitalization costs. As the healthcare industry continues to evolve towards more collaborative and integrated models, these findings contribute to guiding effective and cost-efficient healthcare delivery.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 1","pages":"2"},"PeriodicalIF":2.8,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146085575","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":"Hospital service focus vs. breadth: Impact on hospital outcomes and the moderating role of hospital size.","authors":"Matthew J Castel, Timothy C Dunne","doi":"10.1007/s10729-025-09741-7","DOIUrl":"10.1007/s10729-025-09741-7","url":null,"abstract":"<p><p>There is an ever-increasing need for hospitals in the United States to improve upon their performance. In particular, it is necessary for hospitals to decrease their costs while improving patient satisfaction. Intuitively, hospitals adopt different strategies to accomplish those goals. Researchers have examined how hospitals that use a focus strategy (i.e., specialization) seek ways to improve performance by increased efficiencies and coordination among resources. Other studies examine the impact of increased hospital services (i.e. breadth) as a means to benefit from economies of scope. This study expands upon those literatures by submitting that focus and breadth do not have to be opposing strategies but can be implemented simultaneously; i.e. breadth of services with specialized focus on a few. The current study also examines how hospital size moderates the relationship between those two hospital strategies and performance. Specifically, this study applies an organizational information processing theory lens to predict that hospital focus and service breadth will impact patient satisfaction and cost per discharge, and how those relationships will be moderated by hospital size. Using a pooled cross-section, a regression analysis shows that hospital focus generally improves patient satisfaction while lowering cost; however, the impact on patient satisfaction is diminished for large hospitals. Additionally, service breadth tends to decrease patient satisfaction and lowers cost per discharge; however, the decrease in patient satisfaction is partially mitigated for large hospitals.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 1","pages":"1"},"PeriodicalIF":2.8,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12852193/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146062443","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}