Health Care Management Science最新文献_第5页

Classification of patients with chronic disease by activation level using machine learning methods. 使用机器学习方法按激活水平对慢性病患者进行分类。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-12-01 Epub Date: 2023-10-12 DOI: 10.1007/s10729-023-09653-4

Onur Demiray, Evrim D Gunes, Ercan Kulak, Emrah Dogan, Seyma Gorcin Karaketir, Serap Cifcili, Mehmet Akman, Sibel Sakarya

{"title":"Classification of patients with chronic disease by activation level using machine learning methods.","authors":"Onur Demiray, Evrim D Gunes, Ercan Kulak, Emrah Dogan, Seyma Gorcin Karaketir, Serap Cifcili, Mehmet Akman, Sibel Sakarya","doi":"10.1007/s10729-023-09653-4","DOIUrl":"10.1007/s10729-023-09653-4","url":null,"abstract":"Patient Activation Measure (PAM) measures the activation level of patients with chronic conditions and correlates well with patient adherence behavior, health outcomes, and healthcare costs. PAM is increasingly used in practice to identify patients needing more support from the care team. We define PAM levels 1 and 2 as low PAM and investigate the performance of eight machine learning methods (Logistic Regression, Lasso Regression, Ridge Regression, Random Forest, Gradient Boosted Trees, Support Vector Machines, Decision Trees, Neural Networks) to classify patients. Primary data collected from adult patients (n=431) with Diabetes Mellitus (DM) or Hypertension (HT) attending Family Health Centers in Istanbul, Turkey, is used to test the methods. [Formula: see text] of patients in the dataset have a low PAM level. Classification performance with several feature sets was analyzed to understand the relative importance of different types of information and provide insights. The most important features are found as whether the patient performs self-monitoring, smoking and exercise habits, education, and socio-economic status. The best performance was achieved with the Logistic Regression algorithm, with Area Under the Curve (AUC)=0.72 with the best performing feature set. Alternative feature sets with similar prediction performance are also presented. The prediction performance was inferior with an automated feature selection method, supporting the importance of using domain knowledge in machine learning.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"626-650"},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41199292","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

Determining optimal COVID-19 testing center locations and capacities. 确定新冠肺炎检测中心的最佳位置和容量。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-12-01 Epub Date: 2023-11-07 DOI: 10.1007/s10729-023-09656-1

Esma Akgun, Sibel A Alumur, F Safa Erenay

{"title":"Determining optimal COVID-19 testing center locations and capacities.","authors":"Esma Akgun, Sibel A Alumur, F Safa Erenay","doi":"10.1007/s10729-023-09656-1","DOIUrl":"10.1007/s10729-023-09656-1","url":null,"abstract":"We study the problem of determining the locations and capacities of COVID-19 specimen collection centers to efficiently improve accessibility to polymerase chain reaction testing during surges in testing demand. We develop a two-echelon multi-period location and capacity allocation model that determines optimal number and locations of pop-up testing centers, capacities of the existing centers as well as assignments of demand regions to these centers, and centers to labs. The objective is to minimize the total number of delayed appointments and specimens subject to budget, capacity, and turnaround time constraints, which will in turn improve the accessibility to testing. We apply our model to a case study for locating COVID-19 testing centers in the Region of Waterloo, Canada using data from the Ontario Ministry of Health, public health databases, and medical literature. We also test the performance of the model under uncertain demand and analyze its outputs under various scenarios. Our analyses provide practical insights to the public health decision-makers on the timing of capacity expansions and the locations for the new pop-up centers. According to our results, the optimal strategy is to dynamically expand the existing specimen collection center capacities and prevent bottlenecks by locating pop-up facilities. The optimal locations of pop-ups are among the densely populated areas that are in proximity to the lab and a subset of those locations are selected with the changes in demand. A comparison with a static approach promises up to 39% cost savings under high demand using the developed multi-period model.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"748-769"},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71480901","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

Correction to: Select, route and schedule: optimizing community paramedicine service delivery with mandatory visits and patient prioritization. 更正：选择、路线和时间表：通过强制就诊和患者优先顺序优化社区护理服务提供。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-12-01 DOI: 10.1007/s10729-023-09651-6

Shima Azizi, Özge Aygül, Brenton Faber, Sharon Johnson, Renata Konrad, Andrew C Trapp

引用次数: 0

Managing a multi-panel clinic with heterogeneous patients. 管理一个由不同患者组成的多小组诊所。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-12-01 Epub Date: 2023-11-06 DOI: 10.1007/s10729-023-09658-z

Hao-Wei Chen

引用次数: 0

Combining machine learning and optimization for the operational patient-bed assignment problem. 结合机器学习和优化的手术病床分配问题。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-12-01 Epub Date: 2023-11-28 DOI: 10.1007/s10729-023-09652-5

Fabian Schäfer, Manuel Walther, Dominik G Grimm, Alexander Hübner

{"title":"Combining machine learning and optimization for the operational patient-bed assignment problem.","authors":"Fabian Schäfer, Manuel Walther, Dominik G Grimm, Alexander Hübner","doi":"10.1007/s10729-023-09652-5","DOIUrl":"10.1007/s10729-023-09652-5","url":null,"abstract":"Assigning inpatients to hospital beds impacts patient satisfaction and the workload of nurses and doctors. The assignment is subject to unknown inpatient arrivals, in particular for emergency patients. Hospitals, therefore, need to deal with uncertainty on actual bed requirements and potential shortage situations as bed capacities are limited. This paper develops a model and solution approach for solving the patient bed-assignment problem that is based on a machine learning (ML) approach to forecasting emergency patients. First, it contributes by improving the anticipation of emergency patients using ML approaches, incorporating weather data, time and dates, important local and regional events, as well as current and historical occupancy levels. Drawing on real-life data from a large case hospital, we were able to improve forecasting accuracy for emergency inpatient arrivals. We achieved up to 17% better root mean square error (RMSE) when using ML methods compared to a baseline approach relying on averages for historical arrival rates. We further show that the ML methods outperform time series forecasts. Second, we develop a new hyper-heuristic for solving real-life problem instances based on the pilot method and a specialized greedy look-ahead (GLA) heuristic. When applying the hyper-heuristic in test sets we were able to increase the objective function by up to 5.3% in comparison to the benchmark approach in [40]. A benchmark with a Genetic Algorithm shows also the superiority of the hyper-heuristic. Third, the combination of ML for emergency patient admission forecasting with advanced optimization through the hyper-heuristic allowed us to obtain an improvement of up to 3.3% on a real-life problem.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"785-806"},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10709483/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138444486","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

Surgical scheduling via optimization and machine learning with long-tailed data : Health care management science, in press. 通过长尾数据优化和机器学习进行手术排期：医疗管理科学》，出版中。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-12-01 Epub Date: 2023-09-04 DOI: 10.1007/s10729-023-09649-0

Yuan Shi, Saied Mahdian, Jose Blanchet, Peter Glynn, Andrew Y Shin, David Scheinker

{"title":"Surgical scheduling via optimization and machine learning with long-tailed data : Health care management science, in press.","authors":"Yuan Shi, Saied Mahdian, Jose Blanchet, Peter Glynn, Andrew Y Shin, David Scheinker","doi":"10.1007/s10729-023-09649-0","DOIUrl":"10.1007/s10729-023-09649-0","url":null,"abstract":"Using data from cardiovascular surgery patients with long and highly variable post-surgical lengths of stay (LOS), we develop a modeling framework to reduce recovery unit congestion. We estimate the LOS and its probability distribution using machine learning models, schedule procedures on a rolling basis using a variety of optimization models, and estimate performance with simulation. The machine learning models achieved only modest LOS prediction accuracy, despite access to a very rich set of patient characteristics. Compared to the current paper-based system used in the hospital, most optimization models failed to reduce congestion without increasing wait times for surgery. A conservative stochastic optimization with sufficient sampling to capture the long tail of the LOS distribution outperformed the current manual process and other stochastic and robust optimization approaches. These results highlight the perils of using oversimplified distributional models of LOS for scheduling procedures and the importance of using optimization methods well-suited to dealing with long-tailed behavior.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"692-718"},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10147078","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

Pool testing with dilution effects and heterogeneous priors. 具有稀释效应和异质先验的集合测试

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-12-01 Epub Date: 2023-08-01 DOI: 10.1007/s10729-023-09650-7

Gustavo Quinderé Saraiva

引用次数: 0

Patient-to-nurse ratios: Balancing quality, nurse turnover, and cost. 病人与护士的比率:平衡质量、护士流动率和成本。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-12-01 Epub Date: 2023-11-29 DOI: 10.1007/s10729-023-09659-y

David D Cho, Kurt M Bretthauer, Jan Schoenfelder

{"title":"Patient-to-nurse ratios: Balancing quality, nurse turnover, and cost.","authors":"David D Cho, Kurt M Bretthauer, Jan Schoenfelder","doi":"10.1007/s10729-023-09659-y","DOIUrl":"10.1007/s10729-023-09659-y","url":null,"abstract":"We consider the problem of setting appropriate patient-to-nurse ratios in a hospital, an issue that is both complex and widely debated. There has been only limited effort to take advantage of the extensive empirical results from the medical literature to help construct analytical decision models for developing upper limits on patient-to-nurse ratios that are more patient- and nurse-oriented. For example, empirical studies have shown that each additional patient assigned per nurse in a hospital is associated with increases in mortality rates, length-of-stay, and nurse burnout. Failure to consider these effects leads to disregarded potential cost savings resulting from providing higher quality of care and fewer nurse turnovers. Thus, we present a nurse staffing model that incorporates patient length-of-stay, nurse turnover, and costs related to patient-to-nurse ratios. We present results based on data collected from three participating hospitals, the American Hospital Association (AHA), and the California Office of Statewide Health Planning and Development (OSHPD). By incorporating patient and nurse outcomes, we show that lower patient-to-nurse ratios can potentially provide hospitals with financial benefits in addition to improving the quality of care. Furthermore, our results show that higher policy patient-to-nurse ratio upper limits may not be as harmful in smaller hospitals, but lower policy patient-to-nurse ratios may be necessary for larger hospitals. These results suggest that a \"one ratio fits all\" patient-to-nurse ratio is not optimal. A preferable policy would be to allow the ratio to be hospital-dependent.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"807-826"},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138451369","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

Responding to the US opioid crisis: leveraging analytics to support decision making. 应对美国阿片类药物危机：利用分析支持决策。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2023-12-01 Epub Date: 2023-10-07 DOI: 10.1007/s10729-023-09657-0

Margaret L Brandeau

引用次数: 0

Classification of patients with chronic disease by activation level using machine learning methods. 使用机器学习方法按激活水平对慢性病患者进行分类。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-10-12 DOI: 10.2139/ssrn.4326943

Onur Demiray, E. Gunes, E. Kulak, E. Doğan, Ş. Karaketir, Serap Çi̇fçi̇li̇, M. Akman, S. Sakarya

{"title":"Classification of patients with chronic disease by activation level using machine learning methods.","authors":"Onur Demiray, E. Gunes, E. Kulak, E. Doğan, Ş. Karaketir, Serap Çi̇fçi̇li̇, M. Akman, S. Sakarya","doi":"10.2139/ssrn.4326943","DOIUrl":"https://doi.org/10.2139/ssrn.4326943","url":null,"abstract":"Patient Activation Measure (PAM) measures the activation level of patients with chronic conditions and correlates well with patient adherence behavior, health outcomes, and healthcare costs. PAM is increasingly used in practice to identify patients needing more support from the care team. We define PAM levels 1 and 2 as low PAM and investigate the performance of eight machine learning methods (Logistic Regression, Lasso Regression, Ridge Regression, Random Forest, Gradient Boosted Trees, Support Vector Machines, Decision Trees, Neural Networks) to classify patients. Primary data collected from adult patients (n=431) with Diabetes Mellitus (DM) or Hypertension (HT) attending Family Health Centers in Istanbul, Turkey, is used to test the methods. [Formula: see text] of patients in the dataset have a low PAM level. Classification performance with several feature sets was analyzed to understand the relative importance of different types of information and provide insights. The most important features are found as whether the patient performs self-monitoring, smoking and exercise habits, education, and socio-economic status. The best performance was achieved with the Logistic Regression algorithm, with Area Under the Curve (AUC)=0.72 with the best performing feature set. Alternative feature sets with similar prediction performance are also presented. The prediction performance was inferior with an automated feature selection method, supporting the importance of using domain knowledge in machine learning.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"1 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45488451","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