Health Care Management Science最新文献_第4页

Bi-objective location-allocation model of interventions in high drug consumption areas incorporating X topic modeling. 结合X主题模型的高药物消费地区干预措施双目标定位-分配模型。

IF 2.8 3区医学

Health Care Management Science Pub Date : 2026-05-08 DOI: 10.1007/s10729-025-09753-3

Kevin Palomino, Carmen Berdugo, Jorge Acuña, José L Zayas-Castro

{"title":"Bi-objective location-allocation model of interventions in high drug consumption areas incorporating X topic modeling.","authors":"Kevin Palomino, Carmen Berdugo, Jorge Acuña, José L Zayas-Castro","doi":"10.1007/s10729-025-09753-3","DOIUrl":"10.1007/s10729-025-09753-3","url":null,"abstract":"The Comprehensive Policy for the Prevention and Care of Psychoactive Substance Use in Colombia aims to improve the care provided to people, families, and communities at risk or struggling with psychoactive substance use through prevention and mitigation programs. The effectiveness of these programs depends on both population participation and access to intervention centers. This study proposes a bi-objective integer programming model within a location-allocation framework to support policy decisions under budget constraints. To estimate drug-related risk, we integrate sentiment analysis from social media data (X, formerly Twitter) as a key input into the optimization model. Specifically, negative sentiment derived from posts is used to inform the spatial distribution of risk between locations. The model simultaneously minimizes population-level risk and distance to services, while ensuring equitable coverage based on multidimensional poverty and rurality. The proposed approach was applied to real-world data from Atlántico. The results demonstrated that the bi-objective model achieved an average coverage of 24.67% of the population within a 40 km radius, effectively balancing service accessibility between high-risk urban areas and underserved rural zones. Compared to a population-based heuristic, which achieved only 8.85% coverage and excluded 22 of 23 locations, the proposed model significantly improved equity in service distribution. Furthermore, complementary topic modeling using Latent Dirichlet Allocation (LDA) revealed key themes in public discourse, including drug cartels, addiction risks, and social impacts, providing valuable information to support tailored communication and community engagement strategies for new intervention centers.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 2","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13156152/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147837110","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}

引用次数: 0

An integrated deep learning framework for effective management of surgical instruments tables based on videos. 基于视频的手术器械台有效管理的集成深度学习框架。

IF 2.8 3区医学

Health Care Management Science Pub Date : 2026-04-13 DOI: 10.1007/s10729-026-09761-x

Yong Ma, Tao Tao, Xuying Ma, Haoxin Li, Zhitao Cheng, Maoji Kang, Yan Zhuang, Linru Zhou, Fan Li, Song Su, Jiali Wu, Yong Tang

{"title":"An integrated deep learning framework for effective management of surgical instruments tables based on videos.","authors":"Yong Ma, Tao Tao, Xuying Ma, Haoxin Li, Zhitao Cheng, Maoji Kang, Yan Zhuang, Linru Zhou, Fan Li, Song Su, Jiali Wu, Yong Tang","doi":"10.1007/s10729-026-09761-x","DOIUrl":"10.1007/s10729-026-09761-x","url":null,"abstract":"Efficient management of surgical instruments is a critical component of operating room safety and workflow reliability. In routine clinical practice, inconsistent instrument placement and reliance on manual counting impose a substantial cognitive and operational burden on surgical teams, increasing the risk of preventable intraoperative errors. To address these challenges, we propose a vision-based deep learning framework to support standardized management of surgical instrument tables through real-time monitoring of instrument presence and usage. The framework integrates dedicated modules for instrument table segmentation, instrument recognition, and hand activity tracking, enabling continuous analysis of intraoperative video streams. In comparative evaluations, the proposed segmentation module achieved a mean accuracy of 94.95% and a mean intersection-over-union of 90.04%, while the instrument recognition module reached a mean accuracy of 98.5% with a precision of 97.59%, outperforming existing approaches under the same experimental conditions. Based on this framework, an initial system prototype was developed and evaluated in a simplified simulated environment involving four representative and routinely used surgical instruments (surgical scissors, forceps, occlusive dressings, and a kidney dish). The results demonstrate the technical feasibility of using computer vision-based systems to support standardized instrument placement and real-time counting in the operating room. Rather than replacing clinical judgment, the proposed approach is intended to function as an assistive tool that complements existing workflows. By reducing manual workload and supporting more reliable instrument management, this framework has the potential to enhance operating room safety and efficiency, with broader implications for risk reduction and operational performance in surgical care settings.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 2","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147672216","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}

引用次数: 0

A stochastic programming approach for dynamic allocation of bed capacity and assignment of patients to collaborating hospitals during pandemic outbreaks. 流行病爆发期间病床容量动态分配和病人分配到合作医院的随机规划方法。

IF 2.8 3区医学

Health Care Management Science Pub Date : 2026-03-30 DOI: 10.1007/s10729-025-09747-1

Stef Baas, Sander Dijkstra, Richard J Boucherie, Anne Zander

{"title":"A stochastic programming approach for dynamic allocation of bed capacity and assignment of patients to collaborating hospitals during pandemic outbreaks.","authors":"Stef Baas, Sander Dijkstra, Richard J Boucherie, Anne Zander","doi":"10.1007/s10729-025-09747-1","DOIUrl":"10.1007/s10729-025-09747-1","url":null,"abstract":"Sustaining regular and infectious care during an infectious outbreak requires adequate management support for patient capacity allocation. During the COVID-19 pandemic, hospitals faced severe challenges, including uncertainty surrounding the number of infectious patients needing hospitalization and too little regional cooperation. This led to inefficient usage of healthcare capacity. To better prepare for future pandemics, we have developed a decision support system for central regional decision-making on opening and closing hospital rooms for infectious patients and assigning new infectious patients to hospitals. Since relabeling rooms takes some lead time, we develop a stochastic lookahead approach using stochastic programming with sample average approximation based on scenarios of the number of occupied infectious beds and infectious patients needing hospitalization. The lookahead approach models the impact of current decisions on future costs, such as costs for bed shortages, unused beds for infectious patients, and opening and closing rooms. These decisions affect the quality of care by ensuring capacity for either infectious or regular care patients. Our simulation study of a COVID-19 scenario in the Netherlands demonstrates that the stochastic lookahead approach outperforms a deterministic approach as well as other heuristic decision rules, such as hospitals acting individually and implementing a pandemic unit where one hospital is designated to take all regional infectious patients until full. Our approach is very flexible and capable of tuning model parameters to account for the characteristics of future, yet unknown, pandemics, and supports sustaining regular care by minimizing the strain of infectious care on the available regular care capacity.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 2","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13035704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147573144","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}

引用次数: 0

An upper bound to the benefits of implementing positive assortative matching in pooled testing. 在池测试中实现正分类匹配的好处的上界。

IF 2.8 3区医学

Health Care Management Science Pub Date : 2026-03-26 DOI: 10.1007/s10729-026-09759-5

Gustavo Quinderé Saraiva

{"title":"An upper bound to the benefits of implementing positive assortative matching in pooled testing.","authors":"Gustavo Quinderé Saraiva","doi":"10.1007/s10729-026-09759-5","DOIUrl":"10.1007/s10729-026-09759-5","url":null,"abstract":"Dorfman pooled testing combines individual specimens (e.g., blood samples) into one test; if the pooled sample tests positive for infection, each specimen is tested separately. Under small prevalence levels, this method is known to reduce the expected number of tests required to screen a population, as individual tests only occur when a pooled test detects an infection. When conducting Dorfman testing, studies often recommend implementing positive assortative matching, i.e., pooling together samples with a similar risk of infection, as this tends to minimize the expected number of tests, the expected number of false negatives, and the expected number of false positives. However, because the logistics of collecting data and assorting samples from lowest to highest probability of infection can be costly, one may ask if implementing this procedure is indeed cost-effective. This article provides easy-to-compute upper bounds to the benefits of implementing Dorfman testing with positive assortative matching instead of matching samples randomly. Testers can then compare these upper bounds with the costs of estimating the probabilities of infection from each sample and then matching together those with similar risk of infection, to aid their decision on whether or not to implement this method.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 2","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147520918","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}

引用次数: 0

Urgent samples in clinical laboratories: stochastic batching to minimize patient turnaround time. 临床实验室的紧急样品：随机批处理，以尽量减少患者周转时间。

IF 2.8 3区医学

Health Care Management Science Pub Date : 2026-03-25 DOI: 10.1007/s10729-026-09756-8

Antonin Novak, Andrzej Gnatowski, Premysl Sucha

{"title":"Urgent samples in clinical laboratories: stochastic batching to minimize patient turnaround time.","authors":"Antonin Novak, Andrzej Gnatowski, Premysl Sucha","doi":"10.1007/s10729-026-09756-8","DOIUrl":"10.1007/s10729-026-09756-8","url":null,"abstract":"This paper addresses the problem of batching laboratory samples in hospital laboratories where samples of different priorities are received continuously with uncertain transportation times. The focus is on optimizing the control strategy for loading a centrifuge to minimize patient turnaround time (TAT). While focusing on samples of patients in life-threatening situations (i.e., vital samples), we propose several online and offline methods, including a stochastic mixed-integer quadratic programming model integrated within a discrete-event system simulation. This paper aims to enhance patient care by providing timely laboratory results through improved batching strategies. The case study, which uses real data from a university hospital, demonstrates that incorporating distributional knowledge of transport times into our decision policy can reduce the median patient TAT of vital samples by 4.9 minutes and the 0.95 quantile by 9.7 minutes, but has no significant effect on low-priority samples. In addition, we show that this is essentially an optimal result by comparison with the upper bound obtained by a perfect-knowledge offline algorithm.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 2","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13018023/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147511705","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}

引用次数: 0

How much is adequate staffing for a nursing home? Forecasting daily service need of its case mix. 一个养老院需要多少人手？预测其病例组合的日常服务需求。

IF 2.8 3区医学

Health Care Management Science Pub Date : 2026-03-24 DOI: 10.1007/s10729-025-09740-8

Shujin Jiang, Mingyang Li, Nan Kong

{"title":"How much is adequate staffing for a nursing home? Forecasting daily service need of its case mix.","authors":"Shujin Jiang, Mingyang Li, Nan Kong","doi":"10.1007/s10729-025-09740-8","DOIUrl":"10.1007/s10729-025-09740-8","url":null,"abstract":"Staffing adequately in an economical manner is vital to nursing homes (NHs) in the United States. NHs strive for providing resident-centered differentiated service to their changing and diverse residents and service cases. In this paper, we present a novel Bayesian forecasting method to predict acuity category-specific resident volume and caregiver type-specific staff time on a daily basis for NHs. We utilize the Minimum Data Set (MDS) alongside the Resource Utilization Group (RUG) guidelines according to residents' acuity and the Patient Driven Payment Model (PDPM) specifications on recommended staff time in response to their staffing need, to generate two time series on daily NH service need, i.e., resident volume of each acuity group and staff time of each caregiver type in the entire facility, respectively. Given that the two multi-dimensional time-series above are nonstationary with potential correlations between dimensions, we propose prediction models with time-varying latent states to capture the dynamic patterns in the data and incorporate shared information between different categories. Specifically, we introduce a generalized mixture model to predict the discrete-valued resident volume and a generalized linear model to predict the continuous-valued staff time. Further, we propose a unified Bayesian estimation framework that allows convenient sequential updating and embed it in a forecasting procedure with rolling window to better capturing the nonstationarity inherent in the data. We demonstrate the superiority of the proposed model-based Bayesian forecasting method by comparing its performance against benchmark methods, using data from representative NHs.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 2","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13013337/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147511607","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}

引用次数: 0

Data-driven identification of outpatient-suitable procedures: a machine learning approach. 数据驱动的门诊适宜程序识别：机器学习方法。

IF 2.8 3区医学

Health Care Management Science Pub Date : 2026-03-23 DOI: 10.1007/s10729-026-09758-6

Robert Messerle, Jonas Schreyögg

{"title":"Data-driven identification of outpatient-suitable procedures: a machine learning approach.","authors":"Robert Messerle, Jonas Schreyögg","doi":"10.1007/s10729-026-09758-6","DOIUrl":"10.1007/s10729-026-09758-6","url":null,"abstract":"Policymakers worldwide are encouraging a shift from inpatient to outpatient care to improve the efficiency of health systems. One of the first steps in such efforts typically involves allowing providers, often hospitals, to perform a designated list of procedures on an outpatient basis. However, determining which procedures are suitable for the hospital outpatient setting remains challenging and has traditionally relied on expert judgment and established practices. Our study advances this approach by employing supervised machine learning techniques to identify patterns in physician and expert decisions. We present a comprehensive classification of hospital procedures as either inpatient- or outpatient-suitable and use some methods of explainable AI methods to identify the main factors influencing these assessments. Our model achieves high accuracy (92%) and a robust area under the receiver operating characteristic curve (> 95%), assigning outpatient suitability scores to the entire German procedure catalog. These scores can easily be used with aggregate hospital data from different countries. To validate our approach, we applied the model to a surgical procedures shortlist from the OECD. Our findings provide decision-makers with a data-driven framework for developing targeted strategies to incentivize the provision of outpatient care.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 2","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13009083/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147503715","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}

引用次数: 0

Optimization of diagnostic kits usage based on symptomatic presentation: an application of military decision making in public health. 基于症状表现的诊断试剂盒使用优化：军事决策在公共卫生中的应用

IF 2.8 3区医学

Health Care Management Science Pub Date : 2026-03-16 DOI: 10.1007/s10729-026-09757-7

Fumihiko Nakamura, Naoshi Tsuchida, Kazunori Matsuda, Takafumi Saikawa, Takashi Okumura

{"title":"Optimization of diagnostic kits usage based on symptomatic presentation: an application of military decision making in public health.","authors":"Fumihiko Nakamura, Naoshi Tsuchida, Kazunori Matsuda, Takafumi Saikawa, Takashi Okumura","doi":"10.1007/s10729-026-09757-7","DOIUrl":"10.1007/s10729-026-09757-7","url":null,"abstract":"In situations like pandemics or epidemics where patient surges occur, medical institutions face shortages of resources, such as beds, medications, and testing resources. Operations research has explored efficient resource allocation in this problem domain, but the scarcity of laboratory testing resources has received little attention. In such a situation, clinicians have conserved testing resources by diagnosing overtly symptomatic patients without testing. Although this heuristic approach has statistical ground in regard to pre-test probability and test accuracy, this has not been proven optimal in the literature. This paper applies military operations research’s firing-control theory to formalize this problem to maximize the overall value of the testing resources. We then propose a strategy that optimizes resource performance using the index of suspicion (IoS), a measure of infection likelihood given symptom manifestation. Numerical simulations indicated a sub-optimal strategy for deciding the testing of individual patients based on IoS, maximizing the use of limited resources. This approach provides a robust theoretical foundation to the heuristic strategy, while ensuring a practical method for resource allocation in healthcare settings, especially under conditions of limited testing capabilities, such as during pandemics and seasonal flu epidemics.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 2","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147467804","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}

引用次数: 0

Enhancing pandemic surveillance and testing: a simulation modeling study utilizing german multicenter data with federated machine learning. 加强流行病监测和测试：利用德国多中心数据和联邦机器学习的模拟建模研究。

IF 2.8 3区医学

Health Care Management Science Pub Date : 2026-03-14 DOI: 10.1007/s10729-025-09752-4

Stefan Kempter, Jens O Brunner, Frank Hanses, Christoph Spinner, Lutz T Zabel, Christoph Römmele, Stefan Borgmann, Jörg Janne Vehreschild, Christina C Bartenschlager

{"title":"Enhancing pandemic surveillance and testing: a simulation modeling study utilizing german multicenter data with federated machine learning.","authors":"Stefan Kempter, Jens O Brunner, Frank Hanses, Christoph Spinner, Lutz T Zabel, Christoph Römmele, Stefan Borgmann, Jörg Janne Vehreschild, Christina C Bartenschlager","doi":"10.1007/s10729-025-09752-4","DOIUrl":"10.1007/s10729-025-09752-4","url":null,"abstract":"The COVID-19 pandemic has starkly exposed queryPlease check author names and affiliation if presented correctly.vulnerabilities in the management of surveillance and testing. Significant challenges associated with physical tests, i.e., PCR and antigen tests, include their high cost, resource-intensive nature, turnaround time, and sensitivity. Although the literature has underscored the potential of Machine Learning-based methods for the digital diagnosis of COVID-19, developing high-performing models crucially depends on extensive datasets exceeding the amount available in one healthcare institution. Federated Machine Learning offers a solution to that dilemma. The aim of this research is to evaluate the potential impact of Federated Learning-based digital COVID-19 diagnosis on the trajectory of a pandemic. Therefore, we design a multidimensional evaluation framework, consisting of a simulation study utilizing real-world lab parameters from multiple hospitals and a newly developed performance indicator, named Testing Evaluation for Pandemics. We find that Federated Learning can significantly support the decision-making process of diagnosing COVID-19 at the beginning of a pandemic while saving scarce resources. However, a warm-up phase is needed until constant performance similar to physical tests is reached. In addition, lab parameters have a high prediction power for the diagnosis and are well suited because of patient welfare reasons.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12988995/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147456772","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}

引用次数: 0

Enhancing laboratory testing capacity: strategies for pandemic preparedness. 加强实验室检测能力：大流行防范战略。

IF 2.8 3区医学

Health Care Management Science Pub Date : 2026-03-12 DOI: 10.1007/s10729-026-09755-9

Erica Gralla, Nadia Lahrichi, Fannie Côté, Jade El Hage, Arunkumar Govindakarnavar, Victor J Del Rio Vilas

{"title":"Enhancing laboratory testing capacity: strategies for pandemic preparedness.","authors":"Erica Gralla, Nadia Lahrichi, Fannie Côté, Jade El Hage, Arunkumar Govindakarnavar, Victor J Del Rio Vilas","doi":"10.1007/s10729-026-09755-9","DOIUrl":"10.1007/s10729-026-09755-9","url":null,"abstract":"The COVID-19 pandemic revealed a critical weakness in the public health infrastructure for pandemic response: insufficient testing capacity to control the spread of disease. Overwhelmed by surging demand, testing laboratories could not keep up, leading to long turnaround times for results. This paper builds on previous research that identified constraints on laboratory capacity, to propose strategies for enhancing testing capacity with minimal investment. Three key strategies are explored: pooling specimens, flexible allocation of liquid handling machines, and flexible staff scheduling. The strategies are analyzed to determine their benefits and applicability in relevant COVID-19 testing contexts: the latter in a manual operation (such as in Nepal) and the others in an automated operation (such as in a U.S. university lab). All three strategies can effectively increase laboratory capacity, but require targeted investment: for instance, pooling can paradoxically increase turnaround times without sufficient liquid handling and qPCR capacity; we identify two investment options to mitigate this risk. More broadly, our findings underscore that the “devil is in the details”: performance is highly sensitive to specific operational factors such as staff schedules and machine configuration. Tools and guidelines for implementing improvement strategies are provided, including optimization models to determine efficient schedules and guidelines for resource allocation. The paper demonstrates that there are several potential paths to increase preparedness of testing facilities for current and future epidemics.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147443492","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}

引用次数: 0