IISE Transactions on Healthcare Systems Engineering最新文献_第2页

Uncertainty-driven modality selection for data-efficient prediction of Alzheimer’s disease 不确定性驱动的模式选择用于阿尔茨海默病的数据有效预测

IISE Transactions on Healthcare Systems Engineering Pub Date : 2023-06-27 DOI: 10.1080/24725579.2023.2227197

Zhi-Wei Zheng, Yindan Su, Kewei Chen, D. Weidman, Teresa Wu, ShihChung B. Lo, F. Lure, Jing Li

引用次数: 0

The Application of Fuzzy Delphi Method for Evaluating Biopsychosocial Factors for Prioritization of Patients 应用模糊德尔菲法评价生物社会心理因素对患者进行优先排序

IISE Transactions on Healthcare Systems Engineering Pub Date : 2023-05-22 DOI: 10.1080/24725579.2023.2215247

H. Rana, Dr Muhammad Umer, Uzma Hassan, Umer Asgher, Faheem Qaiser Jamal, Afshan Naseem, N. Ehsan

引用次数: 2

A sociotechnical framework for integration of telehealth into clinical workflow 将远程医疗纳入临床工作流程的社会技术框架

IISE Transactions on Healthcare Systems Engineering Pub Date : 2023-05-08 DOI: 10.1080/24725579.2023.2211083

Samuel Bonet Olivencia, F. Sasangohar

{"title":"A sociotechnical framework for integration of telehealth into clinical workflow","authors":"Samuel Bonet Olivencia, F. Sasangohar","doi":"10.1080/24725579.2023.2211083","DOIUrl":"https://doi.org/10.1080/24725579.2023.2211083","url":null,"abstract":"Abstract Telehealth has received attention in recent years for improving access to healthcare and for supporting integrated care for chronic diseases. Considering that telehealth integration into clinical workflow can alter healthcare providers’ practice patterns, impacting efficiency, quality of care, and patient safety, it is timely to identify and account for system-level variables and considerations to improve the efficiency of telehealth integrations in healthcare settings. Despite the growth of telehealth, and isolated efforts to identify such considerations, a comprehensive conceptual framework for telehealth clinical integration is largely absent. To address this gap, this research effort applied a mixed methods approach to develop a sociotechnical framework to serve as a roadmap for clinics, hospitals, and other healthcare settings regarding the components that must be considered when developing and implementing a telehealth system. The developed framework, System Adoption and Integration of New Telehealth Systems (SAINTS), is grounded in literature and insights from three telehealth case studies in healthcare settings, is influenced by well-grounded sociotechnical models with application in complex healthcare systems, incorporates model-based systems engineering language for the development of structural models, and has been structured considering three temporal stages: system preparation, patient enrollment, and system implementation.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"13 1","pages":"248 - 259"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44981993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Identifying heart disease risk factors from electronic health records using an ensemble of deep learning method 利用深度学习集成方法从电子健康记录中识别心脏病风险因素

IISE Transactions on Healthcare Systems Engineering Pub Date : 2023-04-21 DOI: 10.1080/24725579.2023.2205665

Li Luo, Yue Wang, D. Mo

{"title":"Identifying heart disease risk factors from electronic health records using an ensemble of deep learning method","authors":"Li Luo, Yue Wang, D. Mo","doi":"10.1080/24725579.2023.2205665","DOIUrl":"https://doi.org/10.1080/24725579.2023.2205665","url":null,"abstract":"Abstract Heart disease is a leading cause of death worldwide. For decades, cardiologists have attempted to identify heart-disease risk factors to facilitate its prediction, prevention, and treatment. In recent years, electronic health records (EHRs) have become a valuable source for detecting these risk factors (e.g. smoking, obesity, and diabetes). However, challenges persist as EHRs include clinical notes in free-form and unstructured text, making it tedious for cardiologists to retrieve relevant information. To resolve this problem, we devised a deep-learning-based ensemble approach to automatically identify heart-disease risk factors from EHRs. This proposed approach can efficiently extract semantic information from EHRs and automate risk-factor identification with high performance. In particular, this approach does not require any external domain knowledge about the disease because a powerful Bidirectional Encoder Representations from Transformers (BERT) method is implemented to encode the discriminative features of clinical notes. The extracted features are then fed to conditional random fields (CRF) to identify all possible risk-factor indicators. Experimental results show that, in a scenario where no external knowledge is available, the proposed approach achieves state-of-the-art performance.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"13 1","pages":"237 - 247"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42109302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A long short-term memory model for forecasting the surgical case volumes at a hospital 预测医院外科病例量的长短期记忆模型

IISE Transactions on Healthcare Systems Engineering Pub Date : 2023-04-20 DOI: 10.1080/24725579.2023.2205180

H. Bui, Sandra D. Ekşiog˜lu, Adria A. Villafranca, Joseph A. Sanford, K. Sexton

{"title":"A long short-term memory model for forecasting the surgical case volumes at a hospital","authors":"H. Bui, Sandra D. Ekşiog˜lu, Adria A. Villafranca, Joseph A. Sanford, K. Sexton","doi":"10.1080/24725579.2023.2205180","DOIUrl":"https://doi.org/10.1080/24725579.2023.2205180","url":null,"abstract":"Abstract Surgical procedures are the primary source of expenditures and revenues for hospitals. Accurate forecasts of the volume of surgical cases enable hospitals to efficiently deliver high-quality care to patients. We propose an algorithm to forecast the expected volume of surgical procedures using multivariate time-series data. This algorithm uses feature engineering techniques to determine factors that affect the volume of surgical cases, such as the number of available providers, federal holidays, weather conditions, etc. These features are incorporated in a long short-term memory (LSTM) network to predict the number of surgical procedures in the upcoming week. The hyperparameters of this model are tuned via grid search and Bayesian optimization techniques. We develop and verify the model using historical data of daily case volume from 2014 to 2020 at an academic hospital in North America. The proposed model is validated using data from 2021. The results show that the proposed model can make accurate predictions six weeks in advance, and the average = 0.855, RMSE = 2.017, MAE = 1.104. These results demonstrate the benefits of incorporating additional features to improve the model’s predictive power for time series forecasting.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"13 1","pages":"226 - 236"},"PeriodicalIF":0.0,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41706163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physician scheduling for emergency telemedicine across multiple facilities 医生安排跨多个设施的紧急远程医疗

IISE Transactions on Healthcare Systems Engineering Pub Date : 2023-04-13 DOI: 10.1080/24725579.2023.2201481

O. Olanrewaju, M. Erkoc

{"title":"Physician scheduling for emergency telemedicine across multiple facilities","authors":"O. Olanrewaju, M. Erkoc","doi":"10.1080/24725579.2023.2201481","DOIUrl":"https://doi.org/10.1080/24725579.2023.2201481","url":null,"abstract":"Abstract Telemedicine has emerged as an effective means to connect health service providers with patients remotely. In the context of emergency care, telemedicine typically involves a telemedicine service hub (TSH) that matches remote physicians with patients in emergency wards. Effective operation of such systems requires careful and continuous coordination between physicians and local providers and as such, physician staffing and scheduling is a major managerial challenge that a TSH has to overcome. In this context, our paper studies a setting, where the TSH must respond to an arriving emergency case by assigning a physician within a considerably short time window. An emergency case at a facility can be matched only with a physician who is credentialed at that facility. Since care is urgent, queuing patients is not an option when there is no available on-shift physician. In such cases, the system must invoke the off-shift physicians, which is referred to as “blast.” The telemedicine company tries to avoid this option due to high costs. We propose a novel integer programming model for generating physician schedules with optimal mix of credentials and coverage across multiple hospitals. The proposed model aims to minimize total costs under a chance constraint that limits the blast probabilities and other tactical constraints that are unique to this setting. Two fast acting heuristic-based solution approaches are developed for real-life size problems and their computational performances were demonstrated via numerical analyses.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"13 1","pages":"182 - 197"},"PeriodicalIF":0.0,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48930253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Manpower scheduling of hospital call center: a multi-objective multi-stage optimization approach 医院呼叫中心人力资源调度的多目标多阶段优化方法

IISE Transactions on Healthcare Systems Engineering Pub Date : 2023-04-13 DOI: 10.1080/24725579.2023.2202424

Nazila Bazrafshan, Mohammadsadegh Mikaeili, Sarah S. Lam, Joshua Bosire

{"title":"Manpower scheduling of hospital call center: a multi-objective multi-stage optimization approach","authors":"Nazila Bazrafshan, Mohammadsadegh Mikaeili, Sarah S. Lam, Joshua Bosire","doi":"10.1080/24725579.2023.2202424","DOIUrl":"https://doi.org/10.1080/24725579.2023.2202424","url":null,"abstract":"Abstract This research investigates the solution approach for scheduling staff in a call center to determine the appropriate staff schedules in order to minimize workforce costs while meeting the target level of service quality. In this regard, a multi-objective multi-stage integer mathematical model is developed to determine the optimal schedules for the staff. The first objective aims to minimize the maximum understaffing, and the second objective minimizes the weighted sum of understaffing and overstaffing to create a balance between these two conflicting objectives. The first stage of the model is devoted to full-time (FT) and part-time (PT) staff scheduling, and the second stage of the model determines the schedules for the Per Diem staff (referred to as PRNs in this study). This model considers several constraints and aspects of the problem that include employees’ days off, lunch breaks, preferences, etc. The lexicographic method and weighted sum approach are used to solve the models. Results show that the optimal schedules obtained from the models outperform the current practice of the call center and significantly reduce the maximum/total sum of overstaffing and understaffing over the planning horizon.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"13 1","pages":"205 - 214"},"PeriodicalIF":0.0,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43856547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Change Management: A Framework for Adaptation of the Change Management Model 变革管理：适应变革管理模式的框架

IISE Transactions on Healthcare Systems Engineering Pub Date : 2023-04-11 DOI: 10.1080/24725579.2023.2201959

J. Rawson, Melissa A. Davis

引用次数: 0

Likelihood ratio-based CUSUM charts for real-time monitoring the quality of service in a network of queues 基于似然比的CUSUM图表用于实时监控队列网络中的服务质量

IISE Transactions on Healthcare Systems Engineering Pub Date : 2023-04-06 DOI: 10.1080/24725579.2023.2181470

Yanqing Kuang, Devashish Das, M. Sir, K. Pasupathy

{"title":"Likelihood ratio-based CUSUM charts for real-time monitoring the quality of service in a network of queues","authors":"Yanqing Kuang, Devashish Das, M. Sir, K. Pasupathy","doi":"10.1080/24725579.2023.2181470","DOIUrl":"https://doi.org/10.1080/24725579.2023.2181470","url":null,"abstract":"Abstract Queuing networks (QNs) are widely used stochastic models for service systems include healthcare systems, transportation systems, and computer networks. While existing literature has extensively focused on modeling and optimizing resource allocation in QNs, very little research has been done on developing systematic statistical monitoring methods for QNs. This paper proposes cumulative sum (CUSUM) control charts that monitor the queuing information collected in real-time from the QN. We compare the proposed methods with existing statistical monitoring methods to demonstrate their ability to quickly detect a change in the service rate of one or more queues at the nodes in the QN. Simulation results show that the proposed CUSUM charts are more effective than existing statistical monitoring methods. The motivation for this research comes from the need to monitor the performance of a hospital emergency department (ED) with the goal of monitoring delays experienced by patients visiting the ED. A case study using the data from the ED of a large academic medical center shows that proposed methods are a promising tool for monitoring the timeliness of care provided to patients visiting the ED.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48500862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Patients’ and care partners’ perspectives on the design of a vascular connection for a mobile dialysis device 患者和护理伙伴对移动透析设备血管连接设计的看法

IISE Transactions on Healthcare Systems Engineering Pub Date : 2023-03-21 DOI: 10.1080/24725579.2023.2192533

Auður Anna Jónsdóttir, Siena Firestone, L. Kessler, Ji-Eun Kim

引用次数: 0