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Author: Jordan Shefer Peck
Publisher:
ISBN:
Category :
Languages : en
Pages : 187
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Book Description
A health care delivery chain is a series of treatment steps through which patients flow. The Emergency Department (ED)/Inpatient Unit (IU) chain is an example chain, common to many hospitals. Recent literature has suggested that predictions of IU admission, when patients enter the ED, could be used to initiate IU bed preparations before the patient has completed emergency treatment and improve flow through the chain. This dissertation explores the merit and implications of this suggestion. Using retrospective data collected at the ED of the Veterans Health Administration Boston Health Care System (VHA BHS), three methods are selected for making admission predictions: expert opinion, naive Bayes conditional probability and linear regression with a logit link function (logit-linear regression). The logit-linear regression is found to perform best. Databases of historic data are collected from four hospitals including VHA BHS. Logit-linear regression prediction models generated for each individual hospital perform well based on multiple measures. The prediction model generated for the VHA BHS hospital continues to perform well when predictive data are collected and coded prospectively by nurses. For two weeks, predictions are made on each patient that enters the VHA BHS ED. This data is then summarized and displayed on the VHA BHS internet homepage. No change was observed in key ED flow measures; however, interviews with hospital staff exposed ways in which the prediction information was valuable: planning individual patient admissions, personal scheduling, resource scheduling, resource alignment, and hospital network coordination. A discrete event simulation of the system shows that if IU staff emphasizes discharge before noon, flow measures improve as compared to a baseline scenario where discharge priority begins at 1pm. Sharing ED crowding or prediction information leads to best patient flow performance when using specific schedules dictating IU response to the information. This dissertation targets the practical and theoretical implications of using prediction to improve flow through the ED/IU health care delivery chain. It is suggested that the results will have impact on many other levels of health care delivery that share the delivery chain structure.
Author: Jordan Shefer Peck
Publisher:
ISBN:
Category :
Languages : en
Pages : 187
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Book Description
A health care delivery chain is a series of treatment steps through which patients flow. The Emergency Department (ED)/Inpatient Unit (IU) chain is an example chain, common to many hospitals. Recent literature has suggested that predictions of IU admission, when patients enter the ED, could be used to initiate IU bed preparations before the patient has completed emergency treatment and improve flow through the chain. This dissertation explores the merit and implications of this suggestion. Using retrospective data collected at the ED of the Veterans Health Administration Boston Health Care System (VHA BHS), three methods are selected for making admission predictions: expert opinion, naive Bayes conditional probability and linear regression with a logit link function (logit-linear regression). The logit-linear regression is found to perform best. Databases of historic data are collected from four hospitals including VHA BHS. Logit-linear regression prediction models generated for each individual hospital perform well based on multiple measures. The prediction model generated for the VHA BHS hospital continues to perform well when predictive data are collected and coded prospectively by nurses. For two weeks, predictions are made on each patient that enters the VHA BHS ED. This data is then summarized and displayed on the VHA BHS internet homepage. No change was observed in key ED flow measures; however, interviews with hospital staff exposed ways in which the prediction information was valuable: planning individual patient admissions, personal scheduling, resource scheduling, resource alignment, and hospital network coordination. A discrete event simulation of the system shows that if IU staff emphasizes discharge before noon, flow measures improve as compared to a baseline scenario where discharge priority begins at 1pm. Sharing ED crowding or prediction information leads to best patient flow performance when using specific schedules dictating IU response to the information. This dissertation targets the practical and theoretical implications of using prediction to improve flow through the ED/IU health care delivery chain. It is suggested that the results will have impact on many other levels of health care delivery that share the delivery chain structure.
Author: Randolph Hall
Publisher: Springer Science & Business Media
ISBN: 1461495121
Category : Business & Economics
Languages : en
Pages : 547
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Book Description
This book is dedicated to improving healthcare through reducing delays experienced by patients. With an interdisciplinary approach, this new edition, divided into five sections, begins by examining healthcare as an integrated system. Chapter 1 provides a hierarchical model of healthcare, rising from departments, to centers, regions and the “macro system.” A new chapter demonstrates how to use simulation to assess the interaction of system components to achieve performance goals, and Chapter 3 provides hands-on methods for developing process models to identify and remove bottlenecks, and for developing facility plans. Section 2 addresses crowding and the consequences of delay. Two new chapters (4 and 5) focus on delays in emergency departments, and Chapter 6 then examines medical outcomes that result from waits for surgeries. Section 3 concentrates on management of demand. Chapter 7 presents breakthrough strategies that use real-time monitoring systems for continuous improvement. Chapter 8 looks at the patient appointment system, particularly through the approach of advanced access. Chapter 9 concentrates on managing waiting lists for surgeries, and Chapter 10 examines triage outside of emergency departments, with a focus on allied health programs Section 4 offers analytical tools and models to support analysis of patient flows. Chapter 11 offers techniques for scheduling staff to match patterns in patient demand. Chapter 12 surveys the literature on simulation modeling, which is widely used for both healthcare design and process improvement. Chapter 13 is new and demonstrates the use of process mapping to represent a complex regional trauma system. Chapter 14 provides methods for forecasting demand for healthcare on a region-wide basis. Chapter 15 presents queueing theory as a method for modeling waits in healthcare, and Chapter 16 focuses on rapid delivery of medication in the event of a catastrophic event. Section 5 focuses on achieving change. Chapter 17 provides a diagnostic for assessing the state of a hospital and using the state assessment to select improvement strategies. Chapter 18 demonstrates the importance of optimizing care as patients transition from one care setting to the next. Chapter 19 is new and shows how to implement programs that improve patient satisfaction while also improving flow. Chapter 20 illustrates how to evaluate the overall portfolio of patient diagnostic groups to guide system changes, and Chapter 21 provides project management tools to guide the execution of patient flow projects.
Author:
Publisher:
ISBN:
Category : Hospitals
Languages : en
Pages : 0
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Book Description
In the hospital setting, the emergency room (ER) offers timely emergency care for patients and is considered the busiest department because of the urgency of cases. Emergency rooms have the highest number of patients overcrowding within any hospital; more than 50% of the patients admitted to the hospital come through the ER. Healthcare management is continuously trying to minimize wait time and optimize the hospital's allocated resources, but most ERs still suffer from the overcrowding crisis due to the stochastic arrival and random arrival distribution. Advanced techniques, such as machine learning algorithms, are useful for determining real life queue scenarios and patient flow (e.g., waiting time in queue and length of stay), which are considered measures of ER overcrowding. As such, we began by building a model to predict patient length of stay through predictive input factors such as patient age, mode of arrival, and patient's type of condition using three machine learning algorithms (e.g., artificial neural networks (ANN), linear regression, and logistic regression). The best model accuracy ANN resulted in an increase of 19.5% compared to the performance from previous studies. Then, the Deep Learning Model was applied for historical queueing variables to predict patient waiting time in a system alongside, or in place of, queueing theory (QT). Four optimization algorithms (SGD, Adam, RMSprop, and AdaGrade) were applied and compared to find the best model wth the lowest mean absolute error. The results showed that the SDG algorithm achieved better prediction accuracy than the traditional approach and reduced the use of assumptions. Moreover, the model decreased the error reduction by 24% when compared to prior literature. Lastly, we proposed a model to predict the patient waiting time based on the lab test results. Multi-algorithms were implemented by using real-life COVID-19 test results data recorded during the pandemic. Among the eight proposed models, the results showed that decision tree regression performed better for predicting waiting times. Based on experiments performed in the research, this dissertation provides a guideline for waiting time analysis in the queue - not only in healthcare, but also in other sectors, considering model understandability and the feature extraction process.
Author: Campion Quinn
Publisher: World Scientific
ISBN: 9811284121
Category : Medical
Languages : en
Pages : 354
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Book Description
'Artificial Intelligence in Medicine' is a comprehensive guide exploring the transformative impact of artificial intelligence (AI) in healthcare. The book delves into the foundational concepts and historical development of AI in medicine, highlighting data collection, preprocessing, and feature extraction crucial for medical applications. It showcases the benefits of AI, such as accurate diagnoses and personalized treatments, while addressing ethical and regulatory considerations.The book examines the practical aspects of AI implementation in clinical practice and emphasizes the human aspect of AI in healthcare and patient engagement. Readers can gain insights into the role of AI in clinical decision support, collaborative learning, and knowledge sharing. It concludes with a glimpse into the future of AI-driven healthcare, exploring the emerging technologies and trends in the rapidly evolving field of AI in medicine.
Author: Wang, John
Publisher: IGI Global
ISBN: 1609601378
Category : Business & Economics
Languages : en
Pages : 418
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Book Description
Our rapidly changing world has forced business practitioners, in corporation with academic researchers, to respond quickly and develop effective solution methodologies and techniques to handle new challenges in supply chain systems. Supply Chain Optimization, Management and Integration: Emerging Applications presents readers with a rich collection of ideas from researchers who are bridging the gap between the latest in information technology and supply chain management. This book includes theoretical, analytical, and empirical research, comprehensive reviews of relevant research, and case studies of effective applications in the field of SCM. The use of new technologies, methods, and techniques are emphasized by those who have worked with supply chain management across the world for those in the field of information systems.
Author: Vincent G. Duffy
Publisher: Springer
ISBN: 3030222195
Category : Computers
Languages : en
Pages : 564
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Book Description
This two-volume set LNCS 11581 and 11582 constitutes the thoroughly refereed proceedings of the 10th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, DHM 2019, which was held as part of the 21st HCI International Conference, HCII 2019, in Orlando, FL, USA, in July 2019. The total of 1275 papers and 209 posters included in the 35 HCII 2019 proceedings volumes were carefully reviewed and selected from 5029 submissions. DHM 2019 includes a total of 77 papers; they were organized in topical sections named: Part I, Human Body and Motion: Anthropometry and computer aided ergonomics; motion prediction and motion capture; work modelling and industrial applications; risk assessment and safety. Part II, Healthcare Applications: Models in healthcare; quality of life technologies; health dialogues; health games and social communities.
Author: Nardjes Bouchemal
Publisher:
ISBN: 9781787853010
Category : Artificial intelligence
Languages : en
Pages :
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Book Description
Intelligent Systems for Healthcare Management and Delivery provides relevant and advanced methodological, technological, and scientific approaches related to the application of sophisticated exploitation of AI, as well as providing insight into the technologies and intelligent applications that have received growing attention in recent years such as medical imaging, EMR systems, and drug development assistance.
Author: Cybellium
Publisher: Cybellium
ISBN: 1836791089
Category : Technology & Engineering
Languages : en
Pages : 270
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Book Description
Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com
Author: Wenjun Zhu (Ph.D.)
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Patient transitions are significant elements in healthcare delivery systems, which refer to the movement of a patient from one healthcare setting or provider to another, including discharge from hospital to home, admission from home to a hospital, or movement from one unit to another within the hospital. Patient transitions play a significant role in ensuring patient safety, care quality and operation efficiency. Unfortunately, these transitions do not always go smoothly, and ineffective transitions can lead to adverse events and higher hospital readmission rates and costs. Moreover, although patient transitions have been studied extensively, most of them are based on pilot studies or empirical data analysis. Only limited analytical work can be found, and nearly all of them focus on planning or long-term analysis. The introduction of mathematical modeling can provide a fresh look on the dynamics of patient transitions. Thus, this dissertation is dedicated to improving the efficiency and quality of patient transitions in healthcare delivery systems: from transitions of care between different units, to readmission from home to hospital, and to medication prescription upon admission. Specifically, mathematical models and data analytical tools are utilized to provide a systematic approach, and practical cases in healthcare facilities are introduced to illustrate the applicability of the methods. First, by focusing on transitions of care between multiple units within a hospital, we introduce a Markov chain model to study the transient behavior of patient transfers from a hospital emergency department (ED) to in-patient units. Such transfers are referred to as handoffs and the process is modeled by a stochastic process with unavailability of service, which characterizes the constraints in bed capacity, staff shortage, and coordination issues, etc. To overcome the dimensionality curse, an approximation method is introduced to reduce the computation complexity substantially and numerical studies are carried out to evaluate the accuracy of the method. Next, focusing on readmission from home to hospital, a transition flow model is introduced to study fall-related ED revisits for elderly patients with diabetes. Diabetic patients are stratified into five clinically relevant classes, and the complex transition process is decomposed into five independent sub-process corresponding to the classes as there is no cross-class transition in the process based on the data collected. To reduce revisits, sensitivity analysis is introduced to identify the most critical factors whose changes can lead to the largest reduction in revisits. The applicability of the model is illustrated through a case study at University of Wisconsin (UW) Hospital ED. The study in next chapter is on medication prescription right after transitions into intensive care units (ICUs). Correlation-based network analysis (CNA) is utilized to investigate drug-induced acute kidney injury (AKI) by mining the medication administration records upon patient's admission into ICU of Mayo Clinic, focusing on the identification of drug-drug interactions. Patient-level factors have been identified as potential risk factors that can facilitate or impede safe patient transitions, thus, patient level covariate such as glomerular filtration rate (GFR) is considered to identify the differences among risk groups. In summary, the work developed in this dissertation provides mathematical models and data analytical tools to assess and improve patient transitions, and ultimately contributes to delivery of efficient and high-quality care services in healthcare delivery systems.
Author: Veena Mayya
Publisher: Veena Mayya
ISBN: 9788196431549
Category :
Languages : en
Pages : 0
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Book Description
Healthcare analytics is a branch of data science that examines underlying patterns in healthcare data in order to identify ways in which clinical care can be improved - in terms of patient care, cost optimization, and hospital management. Towards this end, Clinical Decision Support Systems (CDSS) have received extensive research attention over the years. CDSS are intended to influence clinical decision making during patient care. CDSS can be defined as "a link between health observations and health-related knowledge that influences treatment choices by clinicians for improved healthcare delivery".A CDSS is intended to aid physicians and other health care professionals with clinical decision-making tasks based on automated analysis of patient data and other sources of information. CDSS is an evolving system with the potential for wide applicability to improve patient outcomes and healthcare resource utilization. Recent breakthroughs in healthcare analytics have seen an emerging trend in the application of artificial intelligence approaches to assist essential applications such as disease prediction, disease code assignment, disease phenotyping, and disease-related lesion segmentation. Despite the significant benefits offered by CDSSs, there are several issues that need to be overcome to achieve their full potential. There is substantial scope for improvement in terms of patient data modelling methodologies and prediction models, particularly for unstructured clinical data. This thesis discusses several approaches for developing decision support systems towards patient-centric predictive analytics on large multimodal healthcare data. Clinical data in the form of unstructured text, which is rich in patientspecific information sources, has largely remained unexplored and could be potentially used to facilitate effective CDSS development. Effective code assignment for patient clinical records in a hospital plays a significant role in the process of standardizing medical records, mainly for streamlining clinical care delivery, billing, and managing insurance claims. The current practice employed is manual coding, usually carried out by trained medical coders, making the process subjective, error-prone, inexact, and time-consuming.
