IIE transactions on healthcare systems engineering最新文献

{"title":"Resource allocation decision making in the military health system","authors":"Nathaniel D. Bastian, L. Fulton, V. Shah, Tahir Ekin","doi":"10.1080/19488300.2014.904456","DOIUrl":"https://doi.org/10.1080/19488300.2014.904456","url":null,"abstract":"The necessity to efficiently balance and re-allocate system resources among hospitals in a hospital network is paramount, especially as health systems experience increasing demand and costs for health services. In this paper, we proffer a resource allocation-based optimization model that adjusts resources (system inputs) automatically, which provides decision makers (such as health care managers and policy-makers) with a decision-support tool for re-allocating resources in large health systems that are centrally controlled and funded, such as the Military Health System. In these systems, inputs are fixed at certain levels and may only be adjusted within medical treatment facilities, while outputs must be maintained. We provide a mathematical formulation and example solutions from a case study using real-world data from sixteen U.S. Army hospitals. We also find utility in the use of multi-start evolutionary algorithms to store multiple optimal solutions for consideration by decision makers.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"4 1","pages":"80 - 87"},"PeriodicalIF":0.0,"publicationDate":"2014-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2014.904456","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60564324","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 value of remote monitoring systems for treatment of chronic disease","authors":"Ashlea Bennett Milburn, M. Hewitt, Paul M. Griffin, M. Savelsbergh","doi":"10.1080/19488300.2014.901995","DOIUrl":"https://doi.org/10.1080/19488300.2014.901995","url":null,"abstract":"Caring for patients with chronic illnesses is costly—75% of U.S. healthcare spending can be attributed to treating chronic conditions (CDC, 2009a,b). Several components contribute to the cost of treating chronic disease. There are the direct costs associated with treating the disease, and those associated with complications that arise as a result of the disease. There are also indirect costs associated with loss of productivity and quality of life. Technological advances in remote monitoring systems (RMS) may provide a more cost-effective and less labor-intensive way to manage chronic disease by focusing on preventive measures and continuous monitoring instead of emergency care and hospital admissions. In this paper, we develop a model that estimates the total potential savings associated with broad introduction of RMS, and considers how capacity constraints and fairness concerns should impact RMS allocation to target populations. To illustrate the value and insight the model may provide, we conduct a small computational study that focuses on direct costs that would be real costs to a healthcare provider or payer for a subset of the most common chronic diseases: diabetes, heart failure, and hypertension. The computational study shows that, under reasonable assumptions, broad introduction of RMS will lead to substantial cost savings for target populations. The study provides proof of concept that the model could serve as a useful tool for policy makers, as it allows a decision maker to modify cost, risk, and capacity parameters to determine reasonable policies for the allocation of and reimbursement for RMS.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"4 1","pages":"65 - 79"},"PeriodicalIF":0.0,"publicationDate":"2014-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2014.901995","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60564674","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 dynamic multi-attribute utility theory–based decision support system for patient prioritization in the emergency department","authors":"David Claudio, G. Kremer, Wilfredo Bravo-Llerena, A. Freivalds","doi":"10.1080/19488300.2013.879356","DOIUrl":"https://doi.org/10.1080/19488300.2013.879356","url":null,"abstract":"The triage process may result in long waiting periods during which vital indicators of patients with apparently less urgent problems are not monitored after the initial triage. The integration of technology and decision theory has the potential to assist nurses in recognizing priorities by collecting data on the changing clinical information of patients and methodically organizing it. This study investigates the potential for integrating technology and multi-attribute utility theory (MAUT) to develop a dynamic decision support system (DSS) for patient prioritization in Emergency Department (ED) settings. An enhancement to the conventional MAUT model was made to incorporate changes in vital signs over time. A pilot study was conducted with data from 12 nurses and 47 patients. The dynamic MAUT model was assessed with a physician who made prioritization decisions independent of the model. A statistical analysis shows no significant difference between the recommendation proposed by the model and the decisions made by the physician. The results from the analysis give evidence for the potential benefits of combining technology with decision theory to aid nurses in prioritizing ED patients. These results can be used to further develop a DSS for dynamic patient prioritization in ED settings.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"53 1","pages":"1 - 15"},"PeriodicalIF":0.0,"publicationDate":"2014-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2013.879356","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60563796","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":"Time series forecasting in an outpatient cancer clinic using common-day clustering","authors":"David Claudio, Andrew Miller, Anali Huggins","doi":"10.1080/19488300.2013.879459","DOIUrl":"https://doi.org/10.1080/19488300.2013.879459","url":null,"abstract":"The use of forecasting methods in healthcare settings can lead to operational improvements and improved patient care. However, many outpatient care facilities do not engage in demand forecasting and those that do often use rudimentary methods without exploring the best technique to forecast their patient demand. This research study examines the application of time series forecasting techniques to daily patient volume levels at an outpatient cancer treatment clinic. The work focuses on the optimal methods for accurate day-ahead forecasting in this healthcare setting with particular attention given to the differing forecast performance characteristics between traditional calendar sequencing and common-day clustering of the time series data. Through the construction of various forecasting models across multiple patient treatment duration categories, it is found that modifying a time series to a common-day clustered sequence can provide a statistically significant improvement in the accuracy of a forecast.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"7 1","pages":"16 - 26"},"PeriodicalIF":0.0,"publicationDate":"2014-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2013.879459","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60563852","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":"Simulation of influenza propagation: Model development, parameter estimation, and mitigation strategies","authors":"S. Andradóttir, Wenchi Chiu, D. Goldsman, M. Lee","doi":"10.1080/19488300.2014.880093","DOIUrl":"https://doi.org/10.1080/19488300.2014.880093","url":null,"abstract":"Simulation models for disease propagation have been widely used over the last several years. Such models allow one to study and evaluate the potential impacts of various government intervention policies. However, due to the lack of common guidelines, researchers have built simulation models separately and often in isolation, resulting in the repeated re-invention of the wheel. This paper provides a broad review of disease propagation simulation models. We discuss methods for generating susceptible populations, the choice of influenza transmission parameters, and various mitigation strategies. Our aim is to provide the information needed for researchers, practitioners, and decision makers to build simulation models for influenza propagation in particular (and disease propagation in general), and to use these models to better understand diseases, analyze people's behaviors, and identify appropriate intervention strategies.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"4 1","pages":"27 - 48"},"PeriodicalIF":0.0,"publicationDate":"2014-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2014.880093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60563920","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":"Managing operating room efficiency and responsiveness for emergency and elective surgeries—A literature survey","authors":"Yann B. Ferrand, M. Magazine, U. Rao","doi":"10.1080/19488300.2014.881440","DOIUrl":"https://doi.org/10.1080/19488300.2014.881440","url":null,"abstract":"This paper provides a review and classification of the state of research on the question of how a hospital can best utilize its operating rooms (ORs) to balance efficiency and responsiveness when performing surgeries on scheduled electives and high-priority emergencies. We first provide an overview of the specific research questions and conclusions in the literature, as well as a synthesis of the different types of approaches. Then we classify these approaches by methodology and performance measures considered. We also extend the review to other application domains that face a similar question, and highlight similarities and differences to identify potential learning points that apply to the surgery setting. We anticipate this survey highlights the need for future quantitative research that improves the balance of efficiency and responsiveness in the OR.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"4 1","pages":"49 - 64"},"PeriodicalIF":0.0,"publicationDate":"2014-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2014.881440","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60564456","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 mathematical programming approach to the fractionation problem in chemoradiotherapy","authors":"E. Salari, J. Unkelbach, T. Bortfeld","doi":"10.1080/19488300.2015.1017673","DOIUrl":"https://doi.org/10.1080/19488300.2015.1017673","url":null,"abstract":"In concurrent chemoradiotherapy, chemotherapeutic agents are administered during the course of radiotherapy to enhance the primary tumor control. However, this treatment often comes at the expense of increased risk of normal-tissue complications. The additional biological damage is mainly attributed to two mechanisms of action, which are the independent cytotoxic activity of chemotherapeutic agents and their interactive cooperation with radiation. The goal of this study is to develop a mathematical framework to obtain drug and radiation administration schedules that maximize the therapeutic gain for concurrent chemoradiotherapy. In particular, we analyze the impact of incorporating these two mechanisms into the radiation fractionation problem. Considering each mechanism individually, we first derive closed-form expressions for the optimal radiation fractionation regimen and the corresponding drug administration schedule. We next study the case in which both mechanisms are simultaneously present and develop a dynamic programming framework to determine optimal treatment regimens. Results show that those chemotherapeutic agents that interact with radiation may change optimal radiation fractionation regimens. Moreover, administration of chemotherapeutic agents possessing both mechanisms may give rise to optimal non-stationary fractionation schemes.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"62 22 1","pages":"55 - 73"},"PeriodicalIF":0.0,"publicationDate":"2013-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2015.1017673","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60567107","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":"Monthly clinic assignments for internal medicine housestaff","authors":"J. Bard, Z. Shu, Luci K. Leykum","doi":"10.1080/19488300.2013.857370","DOIUrl":"https://doi.org/10.1080/19488300.2013.857370","url":null,"abstract":"This article presents a new model for constructing monthly clinic schedules for interns and residents (i.e., housestaff) training in Internal Medicine. Clinical experiences during the three years of residency occur in inpatient and outpatient settings, and on generalist and specialist clinical services. These experiences include spending time in a primary care setting caring for an assigned group of patients over time. Housestaff rotate through different clinical experiences monthly, with their primary care clinic time overlaid longitudinally on these other clinical services. The exact amount of primary care time spent varies between clinical rotations. In fact, it is the variable clinic hour requirements that drive the scheduling process, and is what distinguishes our problem from most personnel scheduling problems. Typically, staff schedules are driven by shift or hourly demand and are designed to minimize some measure of cost. The objective in our work is to both maximize clinic utilization and minimize the number of violations of a prioritized set of goals while ensuring that certain clinic-level and individual constraints are satisfied. The corresponding problem is formulated as an integer goal program in which several of the hard constraints are temporarily allowed to be violated to avoid infeasibility. To find solutions, a three-phase methodology is proposed. In the first phase (pre-processing step), clinic assignments for a subset of the housestaff are either fixed or excluded each month in light of restrictions imposed by their current rotation. In the second phase, tentative solutions are obtained with a commercial solver. In the final phase (post-processing step), all violations of the relaxed hard constraints are removed and an attempt is made to lexicographically reduce violations of the major goals. The effectiveness of the methodology is demonstrated by analyzing eight monthly rosters provided by the Internal Medicine Residency Program at the University of Texas Health Science Center in San Antonio. On average, we found that up to 7.62% more clinic sessions could be assigned each month using our methodology, and that the corresponding rosters admitted an average of 37% fewer violations for 9 out of the 11 soft constraints than did the actual schedules worked.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"3 1","pages":"207 - 239"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2013.857370","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60564123","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":"Improving healthcare warehouse operations through 5S","authors":"S. Venkateswaran, I. Nahmens, L. Ikuma","doi":"10.1080/19488300.2013.857371","DOIUrl":"https://doi.org/10.1080/19488300.2013.857371","url":null,"abstract":"Typically, Lean strategies in healthcare aim at improving patient throughput, reducing medication errors, redesigning work flow, improving patient safety, and reducing cycle time. Documented studies to improve healthcare's warehouse operations are not common in the literature. Managing types of medical supplies has always been a priority due to demand uncertainties and the risk of shortages that would profoundly affect patient safety. This study showcases two implementation approaches of the Lean tool 5S (Hybrid and Traditional) conducted in three different hospitals’ central warehouses at Ochsner Health System. These warehouses store similar medical products with over 1,000 types of supplies (e.g., syringes, gloves, primary IV) that supply different departments within hospitals and clinics. The objective was to compare the impact of implementing Hybrid 5S (integrated with inventory management techniques and process improvement tools) with Traditional 5S to improve healthcare warehouse operations. Both approaches resulted in increased inventory turnover (30% increase from Hybrid 5S and 4.0% and 43% increase from the two Traditional 5S). The Hybrid 5S approach had additional improvements including 15.7% space saved and the least non-conformities to the 5S ideals. Hence, by incorporating industrial engineering techniques such as inventory management, results from Lean tools can be enhanced.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"3 1","pages":"240 - 253"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2013.857371","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60563835","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":"Guidelines for scheduling in primary care under different patient types and stochastic nurse and provider service times","authors":"H. Oh, A. Muriel, H. Balasubramanian, K. Atkinson, Thomas Ptaszkiewicz","doi":"10.1080/19488300.2013.858379","DOIUrl":"https://doi.org/10.1080/19488300.2013.858379","url":null,"abstract":"Scheduling in primary care is challenging because of the diversity of patient cases (acute versus chronic), mix of appointments (pre-scheduled versus same-day), and uncertain time spent with providers and non-provider staff (nurses/medical assistants). In this paper, we present an empirically driven stochastic integer programming model that schedules and sequences patient appointments during a work day session. The objective is to minimize a weighted measure of provider idle time and patient wait time. Key model features include: an empirically based classification scheme to accommodate different chronic and acute conditions seen in a primary care practice; adequate coordination of patient time with a nurse and a provider; and strategies for introducing slack in the schedule to counter the effects of variability in service time with providers and nurses. In our computational experiments we characterize, for each patient type in our classification, where empty slots should be positioned in the schedule to reduce waiting time. Our results also demonstrate that the optimal start times for a variety of patient-centered heuristic sequences consistently follow a pattern that results in easy to implement guidelines. Moreover, these heuristic sequences and appointment times perform significantly better than the practice's schedule. Finally, we also compare schedules suggested by our two-service-stage model (nurse and provider) with those that only consider the provider stage and find that the performance of the provider-only model is 21% worse than that of the two-service-stage model.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"3 1","pages":"263 - 279"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2013.858379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60564109","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}