IIE transactions on healthcare systems engineering最新文献

{"title":"Impact statement: The evaluation of operating room scheduling settings","authors":"Min Zhang","doi":"10.1080/19488300.2016.1242967","DOIUrl":"https://doi.org/10.1080/19488300.2016.1242967","url":null,"abstract":"Improving the productivity of operating rooms (ORs) is one of the key and challenging problems in hospital operations, since ORs have been the greatest source of both revenues and costs (Ghazalbash et al., 2012). One solution is to look into the OR scheduling optimization. Extensive optimization strategies are proposed in literature attempting to improve the efficiency of ORs, and simulation models are used to validate, test and evaluate the performance of the scheduling strategies before they get to the clinical practice. The next article, by Wang et al., presents a discrete event simulation model to evaluate the performance of OR scheduling strategies under distributed OR scheduling (DORS) settings. It first fills the gap to demonstrate the","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"6 1","pages":"235 - 235"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2016.1242967","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60570163","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":"Impact statement: From the nurse manager's perspective","authors":"Laurel M. Chiaramonte","doi":"10.1080/19488300.2016.1245053","DOIUrl":"https://doi.org/10.1080/19488300.2016.1245053","url":null,"abstract":"As a nurse manager, I spend countless hours developing a 24/7 schedule. Some employees prefer to spend their weekends at home while others appreciate the overtime. Additionally, nurses often have a...","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"6 1","pages":"212 - 212"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2016.1245053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60570189","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":"Call for papers: Special Issue for Health Informatics, IIE Transactions on Healthcare Systems Engineering","authors":"","doi":"10.1080/19488300.2016.1239477","DOIUrl":"https://doi.org/10.1080/19488300.2016.1239477","url":null,"abstract":"Health informatics is a multidisciplinary field that uses health information technology to improve healthcare on its effectiveness, efficiency, timeliness, safety and quality. Health informatics bridges information technology, computer science, statistics, data analytics, management science, systems engineering, social science and other fields to improve the healthcare workflow, enable better clinical processes, support decision making, identify patients risk, and provide information to enhance patient safety and quality. Over the past few years, the acceleration of Electronic Health Record (EHR) adoption brought by “meaningful use” incentive programs from the Centers for Medicare and Medicaid Services, and the rapid progress on the infrastructures and methodologies on data analytics, drive the rapid growth of healthcare informatics. In the industrial and system engineering and operations research community, Health informatics is becoming more and more popular, and there are increasing number of IE/OR faculty members becoming specialized in healthcare related research and number of research publications are increasing very rapidly. In order to provide a publication venue to promote and present the emerging research results in the area of health informatics and to investigate the impacts and applications of healthcare informatics in healthcare industry, IIE transactions on Healthcare systems engineering is organizing a special issue for researchers, clinicians, scientists and engineers presenting their innovative work on the topic of healthcare informatics. Prospective authors are encouraged to submit their completed or ongoing original work related to the health informatics field in this special issue. The topics include but are not limited to: EHR, HIS and other clinical information systems Healthcareworkflowandprocessmodeling and simulation Disease modeling Artificial Intelligence in healthcare Big data analytics and machine learning in healthcare Healthcare data acquisition, transmission, management and visualization Medical imaging/biomedical signal processing and analytics Data driven health logistics and supply chain management mHealth Innovations Data driven healthcare operation analysis Quality and Reliability Engineering method for patient quality and safety Health economics Health Care Management and Policy Cognitive computing for healthcare delivery and disease management Meaningful use and legislative issues Other topics related to healthcare informatics Authors can submit their manuscripts via the ScholarOne Manuscripts (Choose for special issue): https://mc.manuscriptcentral.com/uhse","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"6 1","pages":"260 - 261"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2016.1239477","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60570158","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":"A literature review of preventable hospital readmissions: Preceding the Readmissions Reduction Act","authors":"H. Wan, Lingsong Zhang, Steve Witz, K. Musselman, Fang Yi, C. Mullen, J. Benneyan, J. Zayas-Castro, Florentino Rico, Laila Cure, D. Martínez","doi":"10.1080/19488300.2016.1226210","DOIUrl":"https://doi.org/10.1080/19488300.2016.1226210","url":null,"abstract":"ABSTRACT Preventable readmissions are a large and growing concern throughout healthcare in the United States, representing as many as 20% of all hospitalizations (30-day post-discharge) and an estimated $17 to $26 billion in unnecessary costs annually. National quality initiatives and Medicare reimbursement financial incentives have stimulated significant efforts by healthcare organizations to reduce readmissions via a number of approaches and interventions. Given the severity and complexity of this problem, this article explores the literature describing descriptive and predictive readmission studies as well as proposed interventions that used a systems engineering approach before the 2011 Medicare program to stimulate reduction of readmissions. A total of 112 publications from 1988 to 2011 were identified and grouped into three general categories: descriptive analyses, intervention studies, and predictive analyses. While a significant amount of work has been conducted in each of these areas, very few systems engineering, industrial engineering, and operations research studies have focused directly on the hospital readmission issue. This article, therefore, concludes with a discussion of potential areas in which industrial engineers could make meaningful contributions to this significant issue.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"6 1","pages":"193 - 211"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2016.1226210","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60570106","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 assessment for hypertension and hypertension complications incidences using a Bayesian network","authors":"Junghye Lee, Wonji Lee, I. Park, Hun‐Sung Kim, Hyeseon Lee, C. Jun","doi":"10.1080/19488300.2016.1232767","DOIUrl":"https://doi.org/10.1080/19488300.2016.1232767","url":null,"abstract":"ABSTRACT The Bayesian network is a useful method for modeling healthcare issues since it can graphically represent causal relationships among variables and provide probabilistic information. We apply this method to conduct hypertension and hypertension complications incidence analyses using the National Health Insurance Corporation (NHIC) sample cohort database from 2002 to 2010, which contains more than a million prescribers' information, including socio-demographic information, health check-up records, and other information related to medical treatments and medical expenses in South Korea. We select significant factors that affect hypertension and its complications incidence using Cox regression, and perform Bayesian network analysis with respect to those factors. We investigate the causality for hypertension and its complications incidence, and then calculate the conditional probabilities about nodes of interest. In addition, we evaluate performance to predict the incidence of hypertension and its complications. We conclude that the Bayesian network method has several notable advantages. Firstly, it can demonstrate which factors affect hypertension and its complications incidence and how they are related to each other. Secondly, it can calculate conditional probability; thus, we can perform qualitative and quantitative analyses at the same time.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"6 1","pages":"246 - 259"},"PeriodicalIF":0.0,"publicationDate":"2016-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2016.1232767","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60570152","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":"Rerostering of nurses with intelligent agents and iterated local search","authors":"Michael Chiaramonte, David Caswell","doi":"10.1080/19488300.2016.1226211","DOIUrl":"https://doi.org/10.1080/19488300.2016.1226211","url":null,"abstract":"ABSTRACT The nurse rerostering problem is a special case rostering problem. Rerostering occurs when a disruption to a current nurse roster requires its reconstruction. This article presents a modified agent-based nurse rostering system that solves both the nurse rostering and rerostering problem. Similar to existing nurse rerostering methods, this agent system minimizes the differences between the initial roster and the reconstructed roster through the use of negotiations and iterated local search. This system differs from existing solutions because it seeks to reconstruct the roster so that it also minimizes the negative impact on nurse preferences. The agent-based system, called Competitive Nurse Rostering and Rerostering (CNRR), was tested on three sets of 30 random experiments which included over 200 schedule disruptions. CNRR found solutions for over 90% of all disruptions and over 98% of fixable disruptions. The generated solutions prevented any reductions to nurse preference satisfaction in over half our experimental runs. In 28% of our experiments, there was a significant nurse preference utility loss of five points or more. Five points of utility loss equates to an average of an 11% reduction in a nurse's preference satisfaction.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"6 1","pages":"213 - 222"},"PeriodicalIF":0.0,"publicationDate":"2016-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2016.1226211","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60570108","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":"A discrete event simulation evaluation of distributed operating room scheduling","authors":"Shuo Wang, V. Roshanaei, D. Aleman, D. Urbach","doi":"10.1080/19488300.2016.1226994","DOIUrl":"https://doi.org/10.1080/19488300.2016.1226994","url":null,"abstract":"ABSTRACT Operating room (OR) scheduling is a challenging combinatorial problem and hence most optimization-based OR scheduling research makes simplifying assumptions for tractability, including deterministic surgical durations, absence of dynamic emergency arrivals, and the existence of sufficient downstream resources. In this study, we use discrete event simulation to assess the performance of deterministically optimized OR schedules in a network of collaborating hospitals with shared resources, called distributed OR scheduling (DORS), in the face of uncertain surgical durations, emergency arrivals, and limited downstream resources. We quantify the individual and combined disruptive impact of these stochastic factors on the DORS schedule, using real data obtained from the University Health Network (UHN) in Toronto, Canada. We show that the schedule constructed by DORS results in higher OR utilization and lower average surgery cost compared to the simulated current UHN schedule.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"6 1","pages":"236 - 245"},"PeriodicalIF":0.0,"publicationDate":"2016-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2016.1226994","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60570116","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":"Studying nurse workload and patient waiting time in a hematology-oncology clinic with discrete event simulation","authors":"C. Baril, V. Gascon, Jonathan Miller, C. Bounhol","doi":"10.1080/19488300.2016.1226212","DOIUrl":"https://doi.org/10.1080/19488300.2016.1226212","url":null,"abstract":"ABSTRACT Our study is performed in a hematology-oncology clinic in Québec. This clinic experienced a 20% increase for hematology treatments and a 131% increase for oncology treatments. Clinic managers and personnel felt that this increase led to higher patient waiting time and personnel workload. Clinic managers decided to examine the possibility of adding resources to alleviate nurse workload. Patient trajectories and lead times, appointment scheduling and nurse workload are analyzed with a discrete-event simulation model. It is shown that patient waiting time is not too long. A nurse overload problem is observed with a nurse occupancy rate of 86.98% in the morning and 64.48% in the afternoon. New schedule appointments taking into account nurse capacity are proposed. These result in a decrease of the difference in nurse occupancy rates in the morning and in the afternoon.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"6 1","pages":"223 - 234"},"PeriodicalIF":0.0,"publicationDate":"2016-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2016.1226212","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60570113","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":"Impact statement: From the physician perspective","authors":"T. Duong","doi":"10.1080/19488300.2016.1199545","DOIUrl":"https://doi.org/10.1080/19488300.2016.1199545","url":null,"abstract":"In a context of resource scarcity, healthcare delivery efficiency and efficacy are considered as key objectives to combine the challenges of both cost and quality. From this perspective, care coordination or an integrated caremodel centered on the patient are pointed to as major issues for care providers and patient empowerment in the care process. From the medical literature, healthcare delivery transformation has to come from crosstalk between engineers, care providers, managers, sociologist, industrials and patients, with some authors suggesting to involve both patients and care providers as co-designers of healthcare services to achieve these objectives (Lee et al., 2015; Robert et al., 2015). Taking the example of outpatient chemotherapy planning, Lamé et al., the next article in this issue, illustrates the complexity of the care process, the interdependencies of organizational structure, and the multiplicity of stakeholders involved to perform the process of chemotherapy delivery. This article points out the difficulty of combining and organizing the activities of two departments to increase chemotherapy delivery efficiency while integrating all of the actors’ expectations and constraints in the overall process.While patient satisfaction or waiting time are relevant indicators to assess service quality, they are","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"96 1","pages":"126 - 126"},"PeriodicalIF":0.0,"publicationDate":"2016-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2016.1199545","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60570096","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":"Impact statement: “Operations research applications in hospital operations”","authors":"Sa-Hyun Cho","doi":"10.1080/19488300.2016.1210379","DOIUrl":"https://doi.org/10.1080/19488300.2016.1210379","url":null,"abstract":"","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"116 1","pages":"174 - 174"},"PeriodicalIF":0.0,"publicationDate":"2016-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2016.1210379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60570103","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}