Health Care Management Science最新文献

{"title":"Cluster analysis to identify targeted interventions for highly multimorbid patients in a one-million population in England.","authors":"Richard M Wood, Peter M Thomson, Sam T Creavin","doi":"10.1007/s10729-026-09785-3","DOIUrl":"10.1007/s10729-026-09785-3","url":null,"abstract":"<p><p>Whole population segmentation can be a valuable asset to help understand the general distribution of health needs and healthcare utilisation within a population. In the one million resident health system in and around Bristol (UK), existing work has revealed a five-segment model of the adult population in which, with worsening health, segments halve in size and double in per-person spend. The top segment contains approximately 3% of the population but consumes over one-fifth of healthcare spend. With poor health outcomes and large costs, this high-multimorbidity cohort presents an opportunity to improve the 'value' extracted from available healthcare resources. Clinical management interventions considered for application to this segment should, however, appreciate its high heterogeneity with respect to health need, healthcare usage, and demographic and social characteristics. To support the identification of appropriately tailored interventions, we further partition this segment using cluster analysis (specifically, hierarchical density-based clustering following dimensionality reduction). This yields six clusters which, based on their defining features, are named: Older without dementia (n = 1,243); Cancer (n = 1,055); Younger and complex (n = 2,199); Respiratory (n = 4,743); Cardiovascular (n = 6,140); Dementia (n = 4,579). Following clinical review, each cluster is provided with a value-based objective, to improve outcomes and/or reduce costs. Additionally, a targeted set of possible clinical management interventions is suggested. This paper helps to address a deficit within the current literature of clinically contextualised clustering studies of highly multimorbid individuals.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148684411","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":"Predicting asthma-related hospitalizations in underserved and rural populations in Algeria.","authors":"Amin Bouamar, Ghalem Belalem, Boumedyen Belaid, Dalila Benatta, Abdelmadjid Snouber","doi":"10.1007/s10729-026-09784-4","DOIUrl":"10.1007/s10729-026-09784-4","url":null,"abstract":"<p><p>We developed a comprehensive system architecture that incorporates a predictive module for assessing hospitalization risk due to asthma exacerbations. This module is specifically designed to promote equitable access to healthcare services for underserved populations, particularly those residing in rural and socio-economically disadvantaged areas. The predictive model, based on LightGBM, demonstrated superior performance, achieving high evaluation metrics, particularly in settings with synthetic data, and maintained a minimal false negative rate, which is a critical requirement in clinical decision-making. The proposed architecture further integrates a synthetic data generator to establish a dual-layered security mechanism in conjunction with federated learning, thereby reinforcing data privacy and protection against inversion attacks. Additionally, we proposed a personalized aggregation strategy, tailored to prioritize model updates from clients with lower false negative rates. This approach aims to enhance the global model's predictive reliability, ensuring both quality of care and equitable access to accurate and timely medical interventions for underserved populations.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148678488","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 screening selection rates for post-traumatic stress disorder and the impact of social stigma.","authors":"Gian-Gabriel P Garcia, Navid Ghaffarzadegan, Mohammad S Jalali","doi":"10.1007/s10729-026-09769-3","DOIUrl":"10.1007/s10729-026-09769-3","url":null,"abstract":"<p><p>Recent interest in optimizing diagnostic thresholds for Post-traumatic Stress Disorder (PTSD) screening tools is motivated by the need to improve diagnostic accuracy and facilitate early treatment of PTSD within high-risk populations. However, existing approaches focus on diagnostic accuracy without considering the costs of screening and subsequent treatment, nor the impact of social stigma - which can undermine their effectiveness. We develop a modeling framework that captures the complex interplay between the screening selection rate, screening and treatment outcomes and costs, and the role of social stigma. Critically, our model incorporates feedback loops capturing how social stigma evolves in a vicious cycle wherein it inhibits effective screening and treatment, increasing the prevalence of severe cases of PTSD and future stigma. Our analytical study characterizes the model's structural properties, including optimal screening selection rates and the long-term dynamics of stigma. Our numerical study, motivated by PTSD screening in the U.S. military, compares cost and stigma dynamics across various screening selection rates. Our results highlight the need to select screening thresholds that can balance the two objectives of lowering costs and social stigma in order to break the vicious cycle of stigma surrounding PTSD.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148663636","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":"Reach: Recognising episodes of acute complexity in health: a predictive older patient prioritisation machine learning model.","authors":"Abtin Ijadi Maghsoodi, Valery Pavlov, Paul Rouse, Cameron Graham Walker, Matthew Parsons","doi":"10.1007/s10729-026-09783-5","DOIUrl":"10.1007/s10729-026-09783-5","url":null,"abstract":"<p><p>As the numbers of older people (65 +) rise globally, the pressure on acute hospitals to provide efficient and effective care while addressing resource inequities increases. In this study we introduce Recognising Episodes of Acute Complexity in Health (REACH), a novel Automatic Machine Learning (AutoML)-based predictive model that prioritises older patients and assigns them to complex or non-complex care pathways using routinely collected electronic health record (EHR) and interRAI data. Existing triage and patient-flow approaches are rarely designed for equity-sensitive deployment in settings where underserved populations experience systematically different access and outcomes; in our context, this gap motivated the development of REACH. Designed to be flexible and adaptable, REACH was developed and stress-tested in New Zealand, where Māori, Pasifika and other underserved older populations face inequitable access and outcomes, and is intended as a transferable approach for other health systems with similar equity and capacity challenges. The model estimates individual complexity probabilities and incorporates resource and capacity constraints to support decisions about patient prioritisation, bed allocation and placement into older-person-specific pathways. Applied to seven years of hospital data, REACH demonstrates improved identification of complex geriatric cases and more efficient use of specialised ward capacity compared with traditional FIFO and triage-based assignment. Using New Zealand data as a real-world case study, we demonstrate that equity-aware, capacity-constrained pathway assignment can be operationalised with routine EHR/interRAI inputs, with a design that can be retrained and recalibrated for other jurisdictions that use comparable assessment standards. The application of the model demonstrates its potential to improve patient prioritisation and placement processes while addressing challenges in healthcare access for underserved populations, highlighting the importance of data-driven decision support systems in older persons' care. The findings hold significant implications for healthcare practitioners, managers, policymakers and researchers seeking to design equitable and efficient models of acute care for older people.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13421209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148619762","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":"An optimization-embedded simulation approach for quantifying the operational impacts of right-sizing prenatal care.","authors":"Leena Ghrayeb, Amy Cohn, Ruiwei Jiang, Alex Peahl","doi":"10.1007/s10729-026-09782-6","DOIUrl":"10.1007/s10729-026-09782-6","url":null,"abstract":"<p><p>The United States spends approximately $111 billion annually on maternity care but has the worst maternal mortality rate among peer high-income nations. Recent studies suggest that outdated prenatal care guidelines may be a cause for this. In response to the growing need for modernized prenatal care standards, national prenatal care stakeholders are moving away from the traditional \"one-size-fits-all\" prenatal care pathways and have proposed new \"tailored\" appointment pathways. To study the operational impacts of adopting this new paradigm, we propose a discrete-event simulation model, which captures patient-related heterogeneity, with a mixed-integer linear programming model embedded within it, to schedule patients on a weekly basis. The objectives are to minimize patient delays, patient rescheduling, and overbooking. We use this model to quantify the operational impacts of adopting the new tailored care paradigm and to draw insights about varying scheduling policies. We apply our model to a case study of a single prenatal care clinic within a large academic health center. Our results suggest that tailoring care significantly reduces delays, rescheduling, and overbooking. This additional flexibility may allow a clinic to accommodate more patients, or to better adapt to social risk factors and/or unexpected complications that require additional care. We also find that scheduling appointments one at a time, rather than by trimester or the entirety of the pathway, yields schedules with minimal delays and overbooking.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13407701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148591311","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":"Improving treatment outcomes in Ghana with agent-based model for diabetes patients' self-management behaviours.","authors":"Eunice Twumwaa Tagoe, Justice Nonvignon, Robert Van Der Meer, Itamar Megiddo","doi":"10.1007/s10729-026-09772-8","DOIUrl":"10.1007/s10729-026-09772-8","url":null,"abstract":"<p><p>Despite universal health coverage policies, diabetes management in resource-constrained settings remains challenging due to complex interactions between patient behaviours and systemic barriers. This study combines health behaviour theories with healthcare system constraints in an agent-based model to evaluate the influence of policy scenarios on type 2 diabetes patients' self-management behaviours, blood glucose control, medication adherence, and hospital admissions within Ghana's public health system. Using pattern-oriented modelling with multiple validation levels, the model integrates empirical evidence from clinical data, service provider expertise, and behavioural research to simulate how patients' daily decisions about taking medicines, following lifestyle recommendations, and seeking healthcare interact with system-level factors like medicine availability and insurance coverage. We evaluate three public health scenarios: expanding insurance coverage, increasing medicine availability, and implementing a sugar-sweetened beverage tax. Our analysis indicates that a sustained 10% annual increase in insurance coverage over five years yields substantially greater improvements in blood glucose control and reduces hospital admissions compared to alternative scenarios. By integrating behavioural theories with system constraints to simulate patient decision-making, this modelling approach reveals how affordability and availability barriers interact to influence self-management behaviours and treatment outcomes in underserved populations. Our findings have implications for the operation of Ghana's national insurance scheme and diabetes service planning and delivery within the public health sector.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13401566/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148591405","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":"Cross-training of nurses during a global pandemic: a two-stage stochastic programming approach.","authors":"Hendrik Winzer, Jens Bengtsson","doi":"10.1007/s10729-026-09781-7","DOIUrl":"10.1007/s10729-026-09781-7","url":null,"abstract":"<p><p>During a global pandemic, hospitals face challenges of uncertain patient influx and increased risk of absenteeism among medical personnel, both of which adversely impact patient safety. To address these challenges, we propose a two-stage stochastic program for nurse staffing that incorporates uncertainties in patient demand and absenteeism. Our model supports two critical staffing decisions to optimize nurse allocation. The first is a tactical decision regarding the number of nurses to be cross-trained, and the second is an operational decision concerning the number of temporary nurses that need to be hired. Applied to data from a Norwegian tertiary public hospital during the first wave of the COVID-19 pandemic, our model identifies a bottleneck in intensive care unit nurse availability. Sensitivity analyses reveal that the effect of increasing the penalty for untreated patients is much larger than the effect of changes in cross-training parameters, such as the number of trainees per mentoring nurse or cross-training cost. Moreover, we highlight that cross-training helps to reduce bottlenecks and improves future service levels. Thus, cross-training remains advantageous overall, despite temporarily reducing nurse availability during the cross-training period. Despite the study's limited scope on a single patient pathway and its focus solely on nurses, it provides valuable insights into nurse staffing strategies for practitioners. To the best of our knowledge, this is the first study to model cross-training as a tactical staffing decision and its implications for workforce availability during the cross-training period, while also accounting for an increased risk of absenteeism.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13385144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148520011","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":"Strategic management of vaccine distribution during pandemics under uncertainty.","authors":"Mahsa Mohammadi, Mohsen Roytvand Ghiasvand, Donya Rahmani, Babak Mohamadpour Tosarkani","doi":"10.1007/s10729-026-09774-6","DOIUrl":"10.1007/s10729-026-09774-6","url":null,"abstract":"<p><p>The pandemic has reshaped how supply chain networks (SCNs) are designed, particularly in the context of vaccine distribution. This study introduces a new solution methodology to address the challenges of managing large-scale vaccine supply chains under uncertainty, focusing on the challenge of distributing vaccines within tight time constraints. It introduces a Robust Model (RM) for vaccine SCN design and proposes a novel heuristic algorithm, derived from the Lagrangian Relaxation Algorithm (LRA), to efficiently handle large-scale scenarios. Results from sensitivity analysis and validation confirm that this heuristic significantly improves the algorithm's ability to solve complex cases within reasonable time and accuracy. Additionally, the framework simplifies the consideration of vaccine immunogenicity during outbreaks such as the COVID-19, and it accounts for unpredictable factors such as fluctuating demand, cost variability, and potential vaccine wastage. The model supports decision-makers in effectively distributing vaccines during epidemics. A case study in the Greater Toronto Area (GTA) illustrates the model's practicality, demonstrating how it can enhance immunization efforts, reduce hospitalizations, and mitigate the impact of health crises. This research offers a comprehensive solution for large-scale vaccine SCN design in uncertain environments, with proven relevance in real-world scenarios such as the GTA.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148519938","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":"Data-driven smart freight fleet management amid epidemic disruptions: A framework and case study.","authors":"Carlos D Paternina-Arboleda, Diana G Ramirez-Rios, Karenina N Zaballa, Jairo R Montoya-Torres","doi":"10.1007/s10729-026-09779-1","DOIUrl":"10.1007/s10729-026-09779-1","url":null,"abstract":"<p><p>This article introduces a comprehensive data-driven framework for intelligent freight fleet management that effectively addresses the challenges presented by epidemic disruptions. By harnessing the power of data analytics and decision-making methodologies, the framework adeptly addresses diverse aspects of fleet operations, including route planning, driver allocation strategies, and imperative health risk management protocols. The viability of this framework is demonstrated through a constructed simulated case study that uses real geospatial data, which effectively showcases its real-world application and efficacy. The outcomes of our study illuminate significant enhancements in operational efficiency, marked reduction in operational risks, and a notable elevation in driver safety protocols. Thus, our proposed framework serves as a guidepost, offering profound insights into the seamless introduction of data-driven paradigms and smart technologies, empowering organizations to fortify their resilience and aptly respond to the intricacies posed by epidemic disruptions with adaptability and agility.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148470047","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":"Improving hard-to-place kidney allocation: A machine learning approach to center ranking.","authors":"Sean Berry, Berk Görgülü, Sait Tunç, Mucahit Cevik, Matthew J Ellis","doi":"10.1007/s10729-026-09777-3","DOIUrl":"10.1007/s10729-026-09777-3","url":null,"abstract":"<p><p>Kidney transplantation is the preferred treatment for end-stage renal disease, yet donor scarcity and inefficiencies in allocation systems create major bottlenecks, resulting in prolonged wait times and alarming mortality rates. Despite the severe shortage of donor kidneys, timely and effective interventions to prevent non-utilization of life-saving organs remain limited. Expedited out-of-sequence placement of hard-to-place kidneys to centers with a high likelihood of acceptance has been recommended in the literature as a strategy to improve placement success. However, in practice, this process remains nonstandardized and relies heavily on the subjective judgment of decision-makers. We propose a data-driven, machine learning-based ranking policy for out-of-sequence allocation of hard-to-place kidneys that prioritizes transplant centers using predicted center-level acceptance probabilities. Using national deceased-donor and kidney-offer data, we construct a unique offer-level dataset with donor- and center-specific features. We also employ machine learning interpretability tools to provide insight into the factors influencing kidney allocation decisions. Our analysis demonstrates that the proposed policy can reduce the average number of centers considered before placement by fourfold for all kidneys and tenfold for the subset of hard-to-place kidneys. These results highlight the potential of the proposed framework to improve the efficiency of expedited placement and support more timely utilization of hard-to-place kidneys.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148445876","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}