IISE Transactions on Healthcare Systems Engineering最新文献

{"title":"Louvain clustering integration within density-based graph classification (Louvain dbGC) in Schizophrenia","authors":"Mai Abdulla, M. Khasawneh","doi":"10.1080/24725579.2021.1933268","DOIUrl":"https://doi.org/10.1080/24725579.2021.1933268","url":null,"abstract":"Abstract Several brain disorders are characterized by their silent manifestations that do not display clinical symptoms and are usually diagnosed at advanced stages in which the brain disease may be irreversible. Common strategies to diagnose some brain disorders depend on self-reported symptoms and observed behavior during an extended period of time, and there are no quantitative tests to diagnose mental disorders. Mental disorders are the leading cause of disability in the US and are typically characterized by behavioral changes without clear signs of the structural changes often seen in brain diseases, such as those caused by tumors. With new diagnosis methods, more people are being diagnosed with mental disorders, and some research suggests the importance of early detection to improve patients’ prognoses in restoring the functionality of the brain. Therefore, the goal of this study is to identify biomarkers and underlying biological substrates that will lead to early diagnosis and improved treatment for schizophrenic patients. We combined clustering techniques and density-based graph classification to better predict abnormal functional networks in schizophrenics. The Louvain dbGC combines local and global graph measures with the mesoscale organization of brain networks. To evaluate the effectiveness of the Louvain dbGC, multiple feature selection and classification algorithms were applied. Comparison with state-of-the-art methods of (1) Seed-based Analysis, (2) Independent Component Analysis, and (3) WUD Graph analysis is conducted. The Louvain dbGC better classified and separated Schizophrenics from Healthy Controls with 99.3% accuracy, 98.80% sensitivity, and 100% specificity. The Louvain dbGC can be extended to other mental disorders to detect and monitor therapeutic interventions of such diseases.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"12 1","pages":"20 - 35"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24725579.2021.1933268","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46627566","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}

{"title":"COVID-19: a pandemic challenging healthcare systems","authors":"Lidong Wang, C. Alexander","doi":"10.1080/24725579.2021.1933269","DOIUrl":"https://doi.org/10.1080/24725579.2021.1933269","url":null,"abstract":"Abstract Coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is a global pandemic. Many factors about this disease are still unknown, for example, the questionable original host of SARS-CoV-2. The complex global healthcare systems have been critically challenged by the COVID-19 pandemic. Because mitigating the risk of COVID-19 is a systems engineering issue; a multidisciplinary, coordinated, and cross-sectoral approach based on One Health is required as the health of animals, environment, and humans are closely interconnected. SARS and the Middle East Respiratory Syndrome (MERS) are introduced as a comparison with COVID-19 in this paper; the similarities, differences, and novelties of COVID-19 are also presented. Specific topics of SARS-CoV-2 and COVID-19 include symptoms and manifestations; similarities and differences compared with SARS and MERS; the origin of SARS-CoV-2; the infection and spread of SARS-CoV-2; the testing and diagnosis of COVID-19; therapeutics and treatments; and lessons, information-based management, and positive emotions for fighting against COVID-19. Advances and challenges of mitigating COVID-19 with therapeutics, testing, management, etc. are also introduced. The ability of SARS-CoV-2 to cross the species barrier and infect animals, and the applications of telemedicine, Internet of Things (IoT), Big Data analytics, and deep learning and artificial intelligence (AI) in fighting against COVID-19 are discussed.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"11 1","pages":"271 - 292"},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24725579.2021.1933269","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43624924","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}

{"title":"Risk-averse multi-stage stochastic programming to optimizing vaccine allocation and treatment logistics for effective epidemic response","authors":"Xuecheng Yin, I. E. Büyüktahtakin","doi":"10.1080/24725579.2021.1938298","DOIUrl":"https://doi.org/10.1080/24725579.2021.1938298","url":null,"abstract":"Abstract Existing compartmental-logistics models in epidemics control are limited in terms of optimizing the allocation of vaccines and treatment resources under a risk-averse objective. In this paper, we present a data-driven, mean-risk, multi-stage, stochastic epidemics-vaccination-logistics model that evaluates various disease growth scenarios under the Conditional Value-at-Risk (CVaR) risk measure to optimize the distribution of treatment centers, resources, and vaccines, while minimizing the total expected number of infections, deaths, and close contacts of infected people under a limited budget. We integrate a new ring vaccination compartment into a Susceptible-Infected-Treated-Recovered-Funeral-Burial epidemics-logistics model. Our formulation involves uncertainty both in the vaccine supply and the disease transmission rate. Here, we also consider the risk of experiencing scenarios that lead to adverse outcomes in terms of the number of infected and dead people due to the epidemic. Combining the risk-neutral objective with a risk measure allows for a tradeoff between the weighted expected impact of the outbreak and the expected risks associated with experiencing extremely disastrous scenarios. We incorporate human mobility into the model and develop a new method to estimate the migration rate between each region when data on migration rates is not available. We apply our multi-stage stochastic mixed-integer programming model to the case of controlling the 2018–2020 Ebola Virus Disease (EVD) in the Democratic Republic of the Congo (DRC) using real data. Our results show that increasing the risk-aversion by emphasizing potentially disastrous outbreak scenarios reduces the expected risk related to adverse scenarios at the price of the increased expected number of infections and deaths over all possible scenarios. We also find that isolating and treating infected individuals are the most efficient ways to slow the transmission of the disease, while vaccination is supplementary to primary interventions on reducing the number of infections. Furthermore, our analysis indicates that vaccine acceptance rates affect the optimal vaccine allocation only at the initial stages of the vaccine rollout under a tight vaccine supply.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"12 1","pages":"52 - 74"},"PeriodicalIF":0.0,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24725579.2021.1938298","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42773606","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}

{"title":"Transient queueing analysis for emergency hospital management","authors":"G. Curry, H. Moya, M. Erraguntla, A. Banerjee","doi":"10.1080/24725579.2021.1933655","DOIUrl":"https://doi.org/10.1080/24725579.2021.1933655","url":null,"abstract":"Abstract Strategic and tactical capacity planning are critical decisions faced by hospitals. While these problems have received significant attention, current queueing-based approaches do not address realistic healthcare constraints such as blocking, transient arrivals, transient capacity assignments, and surge capacities. A queueing methodology is developed to extend the analysis of these constructs. The methodology developed is generic for hospitals responding to demand surges during epidemics and pandemics such as the recent COVID-19, and in other application areas in manufacturing, supply chain management, and logistics. The medical staff and patient chairs in the emergency room, beds in the operating theater, ICU, and medical/surgical care units are used in patient treatment at a hospital. They can be considered as servers in a system, where capacity and operational policies affect performance measures such as patient throughput. The methodology develops the probabilities from which system performance measures can be estimated for a serial queueing network with blocking. Transient analysis is employed, due to the time varying nature of the patient arrival patterns. The methodology has the capability to analyze different interventions such as increasing and decreasing capacities, and ambulance diversion. In order to handle typical hospital sized problems that result in thousands of ordinary differential equations defining the system probabilities, a transient version of Kanban queueing network decomposition is developed along with procedures for dealing with the discontinuities that arise at capacity changes. Verification/validation is presented along with several scenarios that illustrate the potential application of this methodology in emergency hospital management.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"12 1","pages":"36 - 51"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24725579.2021.1933655","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42289666","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}

{"title":"Comparative workflow modeling across sites: Results for nursing home prescribing","authors":"E. Ramly, Michelle W. Tong, S. Bondar, James H Ford Ii, D. Nace, C. Crnich","doi":"10.1080/24725579.2020.1829209","DOIUrl":"https://doi.org/10.1080/24725579.2020.1829209","url":null,"abstract":"Abstract Workflows associated with health care delivery vary between settings, and understanding similarities and dissimilarities can inform context-sensitive practice change. Clinical workflows are complex, dynamic, and context-dependent, and comparing workflow across multiple settings can support tailored implementation of practice-change interventions. We propose a methodology for comparative workflow modeling and evaluate its use through application to antibiotic prescribing in six nursing homes in two states in the United States. After collecting multi-site workflow data and developing a cross-site workflow model from nursing home field visits, we constructed a comparison matrix of workflow task occurrences and variations and used it to perform cross-site comparisons across all six NHs.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"42 5","pages":"293 - 304"},"PeriodicalIF":0.0,"publicationDate":"2021-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24725579.2020.1829209","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41245618","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}

{"title":"Intelligent teletriage and personalized routing to manage patient access in a neurosurgery clinic","authors":"Derya Kilinc, E. Gel, A. Demirtaş","doi":"10.1080/24725579.2021.1921081","DOIUrl":"https://doi.org/10.1080/24725579.2021.1921081","url":null,"abstract":"Abstract We consider the intake process of new low back pain (LBP) patients at a neurosurgery clinic to manage patient demand for improving access delays through personalized routing strategies rather than increasing care capacity. Using clinical notes from the first appointments with providers, we devise a decision-tree based intelligent teletriage tool that can be used by non-medically trained agents to predict the surgical class of a patient calling in to request an appointment. The intelligent teletriage tool is based on a classifier that uses surgical-nonsurgical labels that we have generated using a structured algorithm and features that are easy to obtain directly from the patient during the course of a phone conversation. We establish that the accuracy of the teletriage tool is in the order of 80% using 10-fold cross validation and out-of-sample testing on real-life data sets. We then present three priority-based routing strategies that are neutral with respect to care capacity, and show that when used in combination with the intelligent triage tool, these can result in 90% reduction in access delays for the higher priority surgical patients who should be seen urgently. We use detailed simulations of the appointment scheduling workflow to demonstrate our results. We comment on the managerial implications of our work and the potential for the use of needs-based personalized routing strategies with intelligent teletriage to reduce access delays, improve patient outcomes and provider satisfaction.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"11 1","pages":"224 - 239"},"PeriodicalIF":0.0,"publicationDate":"2021-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24725579.2021.1921081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46874657","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}

{"title":"Reducing search times and entropy in hospital emergency departments with real-time location systems","authors":"L. Pendrill, Andreas Espinoza, Johan Wadman, Fredrik Nilsask, Jens Wretborn, U. Ekelund, U. Pahlm","doi":"10.1080/24725579.2021.1881660","DOIUrl":"https://doi.org/10.1080/24725579.2021.1881660","url":null,"abstract":"Abstract Although the consequences of hospital ED crowding have been studied extensively, the causes of crowding are still not well understood. Throughput factors in ED crowding models are difficult to study in a controlled fashion in a dynamic environment where healthcare demand changes rapidly, and physical and human resources suddenly become limited. Opportunities for automated, simultaneous, and low-cost observation of the location and movement of multiple units, patients and staff have recently arisen with the introduction of small, non-intrusive real-time location systems (RTLS). One such RTLS deployment reported here has initiated renewed consideration of quality and industrial statistics as applied to healthcare operations management. Novel metrics for essential constructs of throughput factors in ED crowding such as efficiency and effectiveness are proposed. In particular, causality is explained in terms of understanding of each construct, modeled in terms of entropy, information, and order. Experimental demonstration is given of how labor reduction and the probability of patients, personnel and equipment meeting in terms of less uncertainty can be explained. These novel metrics are expected to facilitate monitoring of how an ED reacts to different levels of crowding, provide insight into crowding dynamics, help evaluate interventions to decrease crowding, and ultimately improve care.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"11 1","pages":"305 - 315"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24725579.2021.1881660","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44315999","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}

{"title":"Image decomposition-based sparse extreme pixel-level feature detection model with application to medical images","authors":"Geet Lahoti, Jialei Chen, Xiaowei Yue, Hao Yan, C. Ranjan, Zhenjiang Qian, Chuck Zhang, Ben Wang","doi":"10.1080/24725579.2021.1910599","DOIUrl":"https://doi.org/10.1080/24725579.2021.1910599","url":null,"abstract":"Abstract Pixel-level feature detection from images is an essential but challenging task encountered in domains such as detecting defects in manufacturing systems and detecting tumors in medical imaging. Often, the real image contains multiple feature types. The types with higher pixel intensities are termed as positive (extreme) features and the ones with lower pixel intensities as negative (extreme) features. For example, when planning a medical treatment, it is important to identify, (a) calcification (a pathological feature which can result in a post-surgical complications) as positive features, and (b) soft tissues (organ morphology, knowledge of which can support pre-surgical planning) as negative features, from a preoperative computed tomography image of the human heart. However, this is not an easy task because (a) conventional segmentation techniques require manual intervention and post-processing, and (b) existing automatic approaches do not distinguish positive features from negative. In this work, we propose a novel, automatic image decomposition-based sparse extreme pixel-level feature detection model to decompose an image into mean and extreme features. To estimate model parameters, a high-dimensional least squares regression with regularization and constraints is utilized. An efficient algorithm based on the alternating direction method of multipliers and the proximal gradient method is developed to solve the large-scale optimization problem. The effectiveness of the proposed model is demonstrated using synthetic tests and a real-world case study, where the model exhibits superior performance over existing methods.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"11 1","pages":"338 - 354"},"PeriodicalIF":0.0,"publicationDate":"2021-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24725579.2021.1910599","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45533750","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}

{"title":"Understanding care transition notifications for chronically ill patients","authors":"S. Kianfar, A. Hundt, P. Hoonakker, Doreen Salek, J. Tomcavage, Abigail R. Wooldridge, Jim Walker, P. Carayon","doi":"10.1080/24725579.2021.1912217","DOIUrl":"https://doi.org/10.1080/24725579.2021.1912217","url":null,"abstract":"Abstract Chronically ill patients may be at risk of re-hospitalization or even death if their care transitions are poorly coordinated. Transitions of care create challenges for care coordination, such as insufficient or inefficient information exchange, i.e. communication, between different care settings. This paper focuses on communication that occurs during transitions of care for chronically ill patients, specifically those with heart failure (HF) and chronic obstructive pulmonary disease (COPD). Using data from 60 interviews with healthcare professionals (care managers, nurses, physicians, social workers, administrative assistants) involved in care transitions, we identified a total of 93 communication events in which healthcare professionals notified each other about four types of patient transitions: hospital admission, hospital discharge, intra-hospital transfer and emergency department (ED) visit. Results show that healthcare professionals use a variety of media (most frequently telephone, CM software, face-to-face) to notify one another about patient transition and communicate additional information. The choice of communication medium depends on the availability of the medium to the sender and the receiver, the purpose and urgency of the message. For example, care management software is used to simply notify one another about patient transition, while telephone is used to provide additional important, time-sensitive information about the patient. We believe a central health IT with appropriate capabilities (synchronous, asynchronous, status indicator, auto-generated notifications) can make communication during care transition more efficient and potentially help reduce re-hospitalization or death among chronically ill patients.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"11 1","pages":"355 - 363"},"PeriodicalIF":0.0,"publicationDate":"2021-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24725579.2021.1912217","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45909786","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}

{"title":"The effect of adherence on antihypertensive therapy plans in patients with diabetes","authors":"Saeideh Mirghorbani, S. Melouk, J. Mittenthal","doi":"10.1080/24725579.2021.1879321","DOIUrl":"https://doi.org/10.1080/24725579.2021.1879321","url":null,"abstract":"Abstract Patient adherence to a medication plan may significantly impact the benefits received by therapy. Thus, considering patient adherence to antihypertensive medication therapy in patients with diabetes, we investigate the impacts of adherence levels on patient health outcomes using a finite horizon, discounted Markov decision process. Health states are based on varying systolic blood pressure levels, cardiovascular complications, adherence levels, and the patient’s current hypertension medications. We model patient transitions through these health states by combining various models from the literature. The model maximizes the expected quality-adjusted life years (QALY). Experimentation on varying levels of patient adherence to medication plans emphasizes the importance of adherence to medication plans with respect to a quality of life metric. Furthermore, sensitivity analysis of model factors finds that smoking as an internal factor, and model parameters as external factors highly influence health outcomes. Furthermore, when patients maintain a higher than average adherence level, they will be able to noticeably increase their expected QALYs.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"11 1","pages":"95 - 112"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24725579.2021.1879321","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47605908","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}