78,727 research outputs found

    Improving Patient Flow Through Axiomatic Design of Hospital Emergency Departments

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    Organised by: Cranfield UniversityIn response to crowding in hospital emergency departments (ED), efforts have been made to increase patient flow through the Fast Track (FT). The use of FT, however, has not always been accompanied by an increase in the overall patient flow, sometimes leaving the FT underutilized. We find that this is mainly caused by the current practice of assigning patients to FT based only on the Emergency Severity Index. One index for two functional requirements results in a coupling between prioritizing of patients and encouraging the fast flow of them. By introducing a new index for patient flow, we could uncouple this design problem and significantly decrease the overall patient waiting time (~50%) compared to that of the existing use of FT.Mori Seiki – The Machine Tool Compan

    An Advanced Home ElderCare Service

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    With the increase of welfare cost all over the developed world, there is a need to resort to new technologies that could help reduce this enormous cost and provide some quality eldercare services. This paper presents a middleware-level solution that integrates monitoring and emergency detection solutions with networking solutions. The proposed system enables efficient integration between a variety of sensors and actuators deployed at home for emergency detection and provides a framework for creating and managing rescue teams willing to assist elders in case of emergency situations. A prototype of the proposed system was designed and implemented. Results were obtained from both computer simulations and a real-network testbed. These results show that the proposed system can help overcome some of the current problems and help reduce the enormous cost of eldercare service

    Design of experiments for non-manufacturing processes : benefits, challenges and some examples

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    Design of Experiments (DoE) is a powerful technique for process optimization that has been widely deployed in almost all types of manufacturing processes and is used extensively in product and process design and development. There have not been as many efforts to apply powerful quality improvement techniques such as DoE to improve non-manufacturing processes. Factor levels often involve changing the way people work and so have to be handled carefully. It is even more important to get everyone working as a team. This paper explores the benefits and challenges in the application of DoE in non-manufacturing contexts. The viewpoints regarding the benefits and challenges of DoE in the non-manufacturing arena are gathered from a number of leading academics and practitioners in the field. The paper also makes an attempt to demystify the fact that DoE is not just applicable to manufacturing industries; rather it is equally applicable to non-manufacturing processes within manufacturing companies. The last part of the paper illustrates some case examples showing the power of the technique in non-manufacturing environments

    Meeting the four-hour deadline in an A&E department

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    This is the print version of the Article. The official published version can be obtained from the link below - Copyright @ 2011 EmeraldPurpose – Accident and emergency (A&E) departments experience a secondary peak in patient length of stay (LoS) at around four hours, caused by the coping strategies used to meet the operational standards imposed by government. The aim of this paper is to build a discrete-event simulation model that captures the coping strategies and more accurately reflects the processes that occur within an A&E department. Design/methodology/approach – A discrete-event simulation (DES) model was used to capture the A&E process at a UK hospital and record the LoS for each patient. Input data on 4,150 arrivals over three one-week periods and staffing levels was obtained from hospital records, while output data were compared with the corresponding records. Expert opinion was used to generate the pathways and model the decision-making processes. Findings – The authors were able to replicate accurately the LoS distribution for the hospital. The model was then applied to a second configuration that had been trialled there; again, the results also reflected the experiences of the hospital. Practical implications – This demonstrates that the coping strategies, such as re-prioritising patients based on current length of time in the department, employed in A&E departments have an impact on LoS of patients and therefore need to be considered when building predictive models if confidence in the results is to be justified. Originality/value – As far as the authors are aware this is the first time that these coping strategies have been included within a simulation model, and therefore the first time that the peak around the four hours has been analysed so accurately using a model

    Antennas and Propagation of Implanted RFIDs for Pervasive Healthcare Applications

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    © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.This post-acceptance version of the paper is essentially complete, but may differ from the official copy of record, which can be found at the following web location (subscription required to access full paper): http://dx.doi.org/10.1109/JPROC.2010.205101

    Reducing Wait Time Prediction In Hospital Emergency Room: Lean Analysis Using a Random Forest Model

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    Most of the patients visiting emergency departments face long waiting times due to overcrowding which is a major concern across the hospital in the United States. Emergency Department (ED) overcrowding is a common phenomenon across hospitals, which leads to issues for the hospital management, such as increased patient s dissatisfaction and an increase in the number of patients choosing to terminate their ED visit without being attended to by a medical healthcare professional. Patients who have to Leave Without Being Seen (LWBS) by doctors often leads to loss of revenue to hospitals encouraging healthcare professionals to analyze ways to improve operational efficiency and reduce the operational expenses of an emergency department. To keep patients informed of the conditions in the emergency room, recently hospitals have started publishing wait times online. Posted wait times help patients to choose the ED which is least overcrowded thus benefiting patients with shortest waiting time and allowing hospitals to allocate and plan resources appropriately. This requires an accurate and efficient method to model the experienced waiting time for patients visiting an emergency medical services unit. In this thesis, the author seeks to estimate the waiting time for low acuity patients within an ED setting; using regularized regression methods such as Lasso, Ridge, Elastic Net, SCAD and MCP; along with tree-based regression (Random Forest). For accurately capturing the dynamic state of emergency rooms, queues of patients at various stage of ED is used as candidate predictor variables along with time patient s arrival time to account for diurnal variation. Best waiting time prediction model is selected based on the analysis of historical data from the hospital. Tree-based regression model predicts wait time of low acuity patients in ED with more accuracy when compared with regularized regression, conventional rolling average, and quantile regression methods. Finally, most influential predictors for predictability of patient wait time are identified for the best performing model

    An analysis and simulation of an emergency department with aims towards improving network flow and efficiency of care

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    Includes bibliographical references.The purpose of this project is to help improve E.R. operations, by minimizing patient wait times and utilizing resources fully and properly. One of the biggest issues in an ER is effectively serving all patients quickly without over burdening the human resources of the system. To do so staff must be utilized correctly, patients prioritized, and processes optimized and balanced within the system. However, many hospitals have very little data on actual times within the system or a true understanding of where delays occur. The project will seek to both determine what processes in the hospital are most detrimental to patient wait times along with giving the hospital recommendations on how to improve wait times and resource utilization. Ultimately the goal is for a system that will allow them to better serve their patients.B.S. (Bachelor of Science

    A Look at the Private Option in Arkansas

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    In September 2013, Arkansas became the first state in the nation to receive approval from the federal government for a Section 1115 demonstration waiver to require most adults who are newly eligible for coverage through the Affordable Care Act's Medicaid expansion to enroll in Marketplace plans. The initiative, often referred to as the "private option," has allowed Arkansas to cover close to 220,000 Medicaid beneficiaries with commercial provider networks and strengthen its Marketplace. An additional 25,000 medically frail adults are covered through the state's fee-for-service system, bringing to 245,000 the number of newly eligible adults covered in Arkansas as of June 30, 2015. As a result of this coverage, Arkansas has been able to drive down its uninsured rate and reduce uncompensated care costs. The future of the private option is the source of extensive discussion within Arkansas, and it continues to be watched closely by policymakers within the state and around the country. Drawing on a dozen interviews with state officials, providers, insurance carriers, and advocates, as well as early data on coverage, reduced uncompensated care costs, and other topics, this issue brief provides an initial look at implementation

    The STAR MAPS-based PiXeL detector

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    The PiXeL detector (PXL) for the Heavy Flavor Tracker (HFT) of the STAR experiment at RHIC is the first application of the state-of-the-art thin Monolithic Active Pixel Sensors (MAPS) technology in a collider environment. Custom built pixel sensors, their readout electronics and the detector mechanical structure are described in detail. Selected detector design aspects and production steps are presented. The detector operations during the three years of data taking (2014-2016) and the overall performance exceeding the design specifications are discussed in the conclusive sections of this paper
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