Download or read book Stochastic Modeling And Analytics In Healthcare Delivery Systems written by Jingshan Li and published by World Scientific. This book was released on 2017-09-22 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, there has been an increased interest in the field of healthcare delivery systems. Scientists and practitioners are constantly searching for ways to improve the safety, quality and efficiency of these systems in order to achieve better patient outcome.This book focuses on the research and best practices in healthcare engineering and technology assessment. With contributions from researchers in the fields of healthcare system stochastic modeling, simulation, optimization and management, this is a valuable read.

Download or read book Data Analytics and Stochastic Models for Informed Decision Making in Healthcare written by Coralys M. Colón Morales and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantitative methods make use of complex mathematical or statistical models to identify patterns in data, predict behaviors and support decision-making. These methods have been broadly applied in many fields. However, the healthcare industry is still ripe with opportunity. Cutting-edge quantitative analysis has only recently emerged in within healthcare. The focus of this dissertation is to continue bridging the gap between quantitative methods and the healthcare industry. Specifically, the work focuses on individual decision-making in the form of selecting a health insurance plan, and operational decision-making in the form of patient appointment scheduling. The uncertainty surrounding these decisions make them complex ones. By applying data analytics and stochastic modeling, the research presented here addresses the processes of decision-making under uncertainty within these settings.

Download or read book Recent Trends in Signal and Image Processing written by Siddhartha Bhattacharyya and published by Springer Nature. This book was released on 2021-04-01 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers selected papers presented at the Third International Symposium on Signal and Image Processing (ISSIP 2020), organized by the Department of Information Technology, RCC Institute of Information Technology, Kolkata, during March 18–19, 2020. It presents fascinating, state-of-the-art research findings in the field of signal and image processing. It includes conference papers covering a wide range of signal processing applications involving filtering, encoding, classification, segmentation, clustering, feature extraction, denoising, watermarking, object recognition, reconstruction and fractal analysis. It addresses various types of signals, such as image, video, speech, non-speech audio, handwritten text, geometric diagram, ECG and EMG signals; MRI, PET and CT scan images; THz signals; solar wind speed signals (SWS); and photoplethysmogram (PPG) signals, and demonstrates how new paradigms of intelligent computing, like quantum computing, can be applied to process and analyze signals precisely and effectively.

Download or read book Stochastic Models for Capacity Planning in Healthcare Delivery written by Asli Özen and published by . This book was released on 2014 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: U.S. healthcare system has become far too complex and costly to sustain and operations research has much to contribute in improving health systems by addressing a large spectrum of problems. We study capacity planning in healthcare while considering the case-mix of patients, using stochastic modeling in different application areas: primary care, inpatient bed allocation and (spine) surgery scheduling. This body of work was developed over four years of collaborative research with hospitals and healthcare providers. The main objective of our research in primary care is to optimize the patient mix of primary care physicians in a group practice to maximize patient-clinician continuity and access. To model case-mix, we use the number of simultaneous chronic conditions (comorbidities) a patient has as a predictor of the number of appointment requests. We later extend the optimization framework and use queuing theory to develop methodologies to quantify and evaluate access to care and continuity of care for patient visits with different urgencies. From an inpatient care perspective, we develop an empirically calibrated simulation model to represent a time-varying multi-server queuing network model with multiple patient classes. Our main focus has been on quantifying the impact of discharge profiles to alleviate inpatient bed congestions. The main objective of our research in surgical care is to create better patient access and improve revenue as a result of increased surgical capacity with more efficient schedules and an improved patient mix, using a multi-stage mixed integer optimization.

Download or read book Stochastic Modeling and Decision Making in Two Healthcare Applications written by Pengyi Shi and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Delivering health care services in an efficient and effective way has become a great challenge for many countries due to the aging population worldwide, rising health expenses, and increasingly complex healthcare delivery systems. It is widely recognized that models and analytical tools can aid decision-making at various levels of the healthcare delivery process, especially when decisions have to be made under uncertainty. This thesis employs stochastic models to improve decision-making under uncertainty in two specific healthcare settings: inpatient flow management and infectious disease modeling. In Part I of this thesis, we study patient flow from the emergency department (ED) to hospital inpatient wards. This line of research aims to develop insights into effective inpatient flow management to reduce the waiting time for admission to inpatient wards from the ED. Delayed admission to inpatient wards, also known as ED boarding, has been identified as a key contributor to ED overcrowding and is a big challenge for many hospitals. Part I consists of three main chapters. In Chapter 2 we present an extensive empirical study of the inpatient department at our collaborating hospital. Motivated by this empirical study, in Chapter 3 we develop a high fidelity stochastic processing network model to capture inpatient flow with a focus on the transfer process from the ED to the wards. In Chapter 4 we devise a new analytical framework, two-time-scale analysis, to predict time-dependent performance measures for some simplified versions of our proposed model. We explore both exact Markov chain analysis and diffusion approximations. Part I of the thesis makes contributions in three dimensions. First, we identify several novel features that need to be built into our proposed stochastic network model. With these features, our model is able to capture inpatient flow dynamics at hourly resolution and reproduce the empirical time-dependent performance measures, whereas traditional time-varying queueing models fail to do so. These features include unconventional non-i.i.d. (independently and identically distributed) service times, an overflow mechanism, and allocation delays. Second, our two-time-scale framework overcomes a number of challenges faced by existing analytical methods in analyzing models with these novel features. These challenges include time-varying arrivals and extremely long service times. Third, analyzing the developed stochastic network model generates a set of useful managerial insights, which allow hospital managers to (i) identify strategies to reduce the waiting time and (ii) evaluate the trade-off between the benefit of reducing ED congestion and the cost from implementing certain policies. In particular, we identify early discharge policies that can eliminate the excessively long waiting times for patients requesting beds in the morning. In Part II of the thesis, we model the spread of influenza pandemics with a focus on identifying factors that may lead to multiple waves of outbreak. This line of research aims to provide insights and guidelines to public health officials in pandemic preparedness and response. In Chapter 6 we evaluate the impact of seasonality and viral mutation on the course of an influenza pandemic. In Chapter 7 we evaluate the impact of changes in social mixing patterns, particularly mass gatherings and holiday traveling, on the disease spread. In Chapters 6 and 7 we develop agent-based simulation models to capture disease spread across both time and space, where each agent represents an individual with certain socio-demographic characteristics and mixing patterns. The important contribution of our models is that the viral transmission characteristics and social contact patterns, which determine the scale and velocity of the disease spread, are no longer static. Simulating the developed models, we study the effect of the starting season of a pandemic, timing and degree of viral mutation, and duration and scale of mass gatherings and holiday traveling on the disease spread. We identify possible scenarios under which multiple outbreaks can occur during an influenza pandemic. Our study can help public health officials and other decision-makers predict the entire course of an influenza pandemic based on emerging viral characteristics at the initial stage, determine what data to collect, foresee potential multiple waves of attack, and better prepare response plans and intervention strategies, such as postponing or cancelling public gathering events.

Download or read book Applications of Stochastic Modeling and Data Analytics Techniques in Healthcare Decision Making written by Ozden Onur Dalgic and published by . This book was released on 2017 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: We present approaches utilizing aspects of data analytics and stochastic modeling techniques and applied to various areas in healthcare. In general, the thesis has composed of three major components. Firtsly, we propose a comparison analysis between two of the very well-known infectious disease modeling techniques to derive effective vaccine allocation strategies. This study, has emerged from the fact that individuals are prioritized based on their risk profiles when allocating limited vaccine stocks during an influenza pandemic. Computationally expensive but realistic agent-based simulations and fast but stylized compartmental models are typically used to derive effective vaccine allocation strategies. A detailed comparison of these two approaches, however, is often omitted. We derive age-specific vaccine allocation strategies to mitigate a pandemic influenza outbreak in Seattle by applying derivative-free optimization to an agent-based simulation and also to a compartmental model. We compare the strategies derived by these two approaches under various infection aggressiveness and vaccine coverage scenarios. We observe that both approaches primarily vaccinate school children, however they may allocate the remaining vaccines in different ways. The vaccine allocation strategies derived by using the agent-based simulation are associated with up to 70% decrease in total cost and 34% reduction in the number of infections compared to the strategies derived by the compartmental model. Nevertheless, the latter approach may still be competitive for very low and/or very high infection aggressiveness. Our results provide insights about the possible differences between the vaccine allocation strategies derived by using agent-based simulations and those derived by using compartmental models. Secondly, we introduce a novel and holistic scheme to capture the gradual amyotrophic lateral sclerosis progression based on the critical events referred as tollgates. Amyotrophic lateral sclerosis is neuro-degenerative and terminal disease. Patients with amyotrophic lateral sclerosis lose control of voluntary movements over time due to continuous degeneration of motor neurons. Using a comprehensive longitudinal dataset from Mayo Clinic's ALS Clinic in Rochester, MN, we characterize the progression through tollgates at the body segment (e.g., arm, leg, speech, swallowing, breathing) and patient levels over time. We describe how the progression based on the followed tollgate pathways varies among patients and ultimately, how this type of progression characterization may be utilized for further studies. Kaplan-Meier analysis are conducted to derive the probability of passing each tollgate over time. We observe that, in each body segment, the majority of the patients have their abilities affected or worse (Level1) at the first visit. Especially, the proportion of patients at higher tollgate levels is larger for arm and leg segments compared to others. For each segment, we derive the over-time progression pathways of patients in terms of the reached tollgates. Tollgates towards later visits show a great diversity among patients who were at the same tollgate level at the first clinic visit. The proposed tollgate mechanism well captures the variability among patients and the history plays a role on when patients reach tollgates. We suggest that further and comprehensive studies should be conducted to observe the whole effect of the history in the future progression. Thirdly, based on the fact that many available databases may not have detailed medical records to derive the necessary data, we propose a classification-based approach to estimate the tollgate data using ALSFRS-R scores which are available in most databases. We observed that tollgates are significantly associated with the ALSFRS-R scores. Multiclass classification techniques are commonly used in such problem; however, traditional classification techniques are not applicable to the problem of finding the tollgates due to the constraint of that a patients' tollgates under a specific segment for multiple visit should be non-decreasing over time. Therefore, we propose two approaches to achieve a multi-class estimation in a non-decreasing manner given a classification method. While the first approach fixes the class estimates of observation in a sequential manner, the second approach utilizes a mixed integer programming model to estimate all the classes of a patients' observations. We used five different multi-class classification techniques to be employed by both of the above implementations. Thus, we investigate the performance of classification model employed under both approaches for each body segment.

Download or read book Healthcare Analytics written by Hui Yang and published by John Wiley & Sons. This book was released on 2016-10-13 with total page 632 pages. Available in PDF, EPUB and Kindle. Book excerpt: Features of statistical and operational research methods and tools being used to improve the healthcare industry With a focus on cutting-edge approaches to the quickly growing field of healthcare, Healthcare Analytics: From Data to Knowledge to Healthcare Improvement provides an integrated and comprehensive treatment on recent research advancements in data-driven healthcare analytics in an effort to provide more personalized and smarter healthcare services. Emphasizing data and healthcare analytics from an operational management and statistical perspective, the book details how analytical methods and tools can be utilized to enhance healthcare quality and operational efficiency. Organized into two main sections, Part I features biomedical and health informatics and specifically addresses the analytics of genomic and proteomic data; physiological signals from patient-monitoring systems; data uncertainty in clinical laboratory tests; predictive modeling; disease modeling for sepsis; and the design of cyber infrastructures for early prediction of epidemic events. Part II focuses on healthcare delivery systems, including system advances for transforming clinic workflow and patient care; macro analysis of patient flow distribution; intensive care units; primary care; demand and resource allocation; mathematical models for predicting patient readmission and postoperative outcome; physician–patient interactions; insurance claims; and the role of social media in healthcare. Healthcare Analytics: From Data to Knowledge to Healthcare Improvement also features: • Contributions from well-known international experts who shed light on new approaches in this growing area • Discussions on contemporary methods and techniques to address the handling of rich and large-scale healthcare data as well as the overall optimization of healthcare system operations • Numerous real-world examples and case studies that emphasize the vast potential of statistical and operational research tools and techniques to address the big data environment within the healthcare industry • Plentiful applications that showcase analytical methods and tools tailored for successful healthcare systems modeling and improvement The book is an ideal reference for academics and practitioners in operations research, management science, applied mathematics, statistics, business, industrial and systems engineering, healthcare systems, and economics. Healthcare Analytics: From Data to Knowledge to Healthcare Improvement is also appropriate for graduate-level courses typically offered within operations research, industrial engineering, business, and public health departments.

Download or read book Healthcare Analytics written by Hui Yang and published by . This book was released on 2016 with total page 602 pages. Available in PDF, EPUB and Kindle. Book excerpt: Features of statistical and operational research methods and tools being used to improve the healthcare industry With a focus on cutting-edge approaches to the quickly growing field of healthcare, Healthcare Analytics: From Data to Knowledge to Healthcare Improvement provides an integrated and comprehensive treatment on recent research advancements in data-driven healthcare analytics in an effort to provide more personalized and smarter healthcare services. Emphasizing data and healthcare analytics from an operational management and statistical perspective, the book details how analytical methods and tools can be utilized to enhance healthcare quality and operational efficiency. Organized into two main sections, Part I features biomedical and health informatics and specifically addresses the analytics of genomic and proteomic data; physiological signals from patient-monitoring systems; data uncertainty in clinical laboratory tests; predictive modeling; disease modeling for sepsis; and the design of cyber infrastructures for early prediction of epidemic events. Part II focuses on healthcare delivery systems, including system advances for transforming clinic workflow and patient care; macro analysis of patient flow distribution; intensive care units; primary care; demand and resource allocation; mathematical models for predicting patient readmission and postoperative outcome; physician-patient interactions; insurance claims; and the role of social media in healthcare. Healthcare Analytics: From Data to Knowledge to Healthcare Improvement also features: - Contributions from well-known international experts who shed light on new approaches in this growing area - Discussions on contemporary methods and techniques to address the handling of rich and large-scale healthcare data as well as the overall optimization of healthcare system operations - Numerous real-world examples and case studies that emphasize the vast potential of statistical and operational research tools and techniques to address the big data environment within the healthcare industry - Plentiful applications that showcase analytical methods and tools tailored for successful healthcare systems modeling and improvement The book is an ideal reference for academics and practitioners in operations research, management science, applied mathematics, statistics, business, industrial and systems engineering, healthcare systems, and economics. Healthcare Analytics: From Data to Knowledge to Healthcare Improvement is also appropriate for graduate-level courses typically offered within operations research, industrial engineering, business, and public health departments. HUI YANG, PhD, is Associate Professor in the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at The Pennsylvania State University. His research interests include sensor-based modeling and analysis of complex systems for process monitoring/control; system diagnostics/ prognostics; quality improvement; and performance optimization with special focus on nonlinear stochastic dynamics and the resulting chaotic, recurrence, self-organizing behaviors. EVA K. LEE, PhD, is Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology, Director of the Center for Operations Research in Medicine and HealthCare, and Distinguished Scholar in Health System, Health Systems Institute at both Emory University School of Medicine and Georgia Institute of Technology. Her research interests include health-risk prediction; early disease prediction and diagnosis; optimal treatment strategies and drug delivery; healthcare outcome analysis and treatment prediction; public health and medical preparedness; large-scale healthcare/medical decision analysis and quality improvement; clinical translational.

Download or read book Handbook of Healthcare Analytics written by Tinglong Dai and published by John Wiley & Sons. This book was released on 2018-10-16 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: How can analytics scholars and healthcare professionals access the most exciting and important healthcare topics and tools for the 21st century? Editors Tinglong Dai and Sridhar Tayur, aided by a team of internationally acclaimed experts, have curated this timely volume to help newcomers and seasoned researchers alike to rapidly comprehend a diverse set of thrusts and tools in this rapidly growing cross-disciplinary field. The Handbook covers a wide range of macro-, meso- and micro-level thrusts—such as market design, competing interests, global health, personalized medicine, residential care and concierge medicine, among others—and structures what has been a highly fragmented research area into a coherent scientific discipline. The handbook also provides an easy-to-comprehend introduction to five essential research tools—Markov decision process, game theory and information economics, queueing games, econometric methods, and data science—by illustrating their uses and applicability on examples from diverse healthcare settings, thus connecting tools with thrusts. The primary audience of the Handbook includes analytics scholars interested in healthcare and healthcare practitioners interested in analytics. This Handbook: Instills analytics scholars with a way of thinking that incorporates behavioral, incentive, and policy considerations in various healthcare settings. This change in perspective—a shift in gaze away from narrow, local and one-off operational improvement efforts that do not replicate, scale or remain sustainable—can lead to new knowledge and innovative solutions that healthcare has been seeking so desperately. Facilitates collaboration between healthcare experts and analytics scholar to frame and tackle their pressing concerns through appropriate modern mathematical tools designed for this very purpose. The handbook is designed to be accessible to the independent reader, and it may be used in a variety of settings, from a short lecture series on specific topics to a semester-long course.

Download or read book A Smart and Connected Healthcare Delivery Process written by Sujee Lee and published by . This book was released on 2020 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: Healthcare delivery is facing a paradigm change to embrace rapid development in information technology, data analytics, artificial intelligence, as well as numerous medical devices and treatments to achieve smart and interconnected care. In a smart and interconnected healthcare system, integration of data analytics, system modeling, optimal decision-making, and care intervention is necessary and important. Based on the collected data, including the patient's demographic information, disease history, physical exam, and diagnostic test, the smart and patient-specific intervention decision will be formed, and proper care practice will be delivered. Through this, all activities of prevention, diagnosis, treatment, clinical visits, and home care are all connected together. This dissertation is dedicated to providing analytical frameworks for such a smart and connected healthcare delivery process to address issues related to classification, prediction, intervention, and care service by integrating machine learning, optimization, and system modeling techniques. Specifically, (1) through data collection and preprocessing, predictive models are developed to stratify patients, determine the patient's status, or predict risks by means of machine learning algorithms. (2) Based on patient identification, through modeling the post-discharge care process, intervention plans and policies are evaluated and optimal decisions are proposed. (3) Finally, to implement the intervention and treatment plan, care delivery policies are studied to improve care quality. Through these steps, an integrated and comprehensive framework can be established to connect data analytics, intervention planning, and care services in a closed loop. In order to show the significance and applicability of such frameworks for the smart and connected healthcare system, this dissertation introduces analytical frameworks applied on readmission risk management for COPD (Chronic Obstructive Pulmonary Disease) patients and opioid prescription optimization for TJR (Total Joint Replacement) patients. In order to provide models of continuous care delivery, a workflow policy development study for primary care physicians is also introduced. Specifically, for reducing COPD readmissions, two different sub-frameworks integrating machine learning models and operations research methods are developed. The first sub-framework classifies COPD patients into the high or low risk of readmissions, then based on the risk group, an intervention resource allocation is determined through linear programming and graphical analysis. In the second sub-framework, a training procedure for a causal Bayesian network is proposed and the resulting causal network describing relationships between factors and readmission is integrated into a Markov decision process to provide a dynamic intervention planning. For opioid prescription optimization, a novel approach for semi-supervised learning is proposed and the resulting classification model predicts patients' expected opioid consumption levels. Then, a stochastic program is introduced to decide how many opioids should be prescribed to each class of patients to reduce opioid leftovers and thereby curtail the opioid crisis. Finally, as primary care is in charge of continuous care delivery regardless of patients' underlying diseases and conditions, workflow models for primary care physicians are developed by utilizing stochastic process modeling techniques. In summary, the work developed in this dissertation provides novel frameworks enabling smart and interconnected care to treat patients in need and resolve issues in the U.S. healthcare system.

Download or read book Using Predictive Analytics to Improve Healthcare Outcomes written by John W. Nelson and published by John Wiley & Sons. This book was released on 2021-07-21 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using Predictive Analytics to Improve Healthcare Outcomes Winner of the American Journal of Nursing (AJN) Informatics Book of the Year Award 2021! Discover a comprehensive overview, from established leaders in the field, of how to use predictive analytics and other analytic methods for healthcare quality improvement. Using Predictive Analytics to Improve Healthcare Outcomes delivers a 16-step process to use predictive analytics to improve operations in the complex industry of healthcare. The book includes numerous case studies that make use of predictive analytics and other mathematical methodologies to save money and improve patient outcomes. The book is organized as a “how-to” manual, showing how to use existing theory and tools to achieve desired positive outcomes. You will learn how your organization can use predictive analytics to identify the most impactful operational interventions before changing operations. This includes: A thorough introduction to data, caring theory, Relationship-Based Care®, the Caring Behaviors Assurance System©, and healthcare operations, including how to build a measurement model and improve organizational outcomes. An exploration of analytics in action, including comprehensive case studies on patient falls, palliative care, infection reduction, reducing rates of readmission for heart failure, and more—all resulting in action plans allowing clinicians to make changes that have been proven in advance to result in positive outcomes. Discussions of how to refine quality improvement initiatives, including the use of “comfort” as a construct to illustrate the importance of solid theory and good measurement in adequate pain management. An examination of international organizations using analytics to improve operations within cultural context. Using Predictive Analytics to Improve Healthcare Outcomes is perfect for executives, researchers, and quality improvement staff at healthcare organizations, as well as educators teaching mathematics, data science, or quality improvement. Employ this valuable resource that walks you through the steps of managing and optimizing outcomes in your clinical care operations.

Download or read book Applications of Operations Research to Health Care Delivery Systems written by Brant E. Fries and published by Springer Science & Business Media. This book was released on 2013-03-13 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt: L-';icoI:.io:::.; 0: (1llL~·aL'. On~; n:~\.!iI:-c:: te"hniC), '

Download or read book Measurement and Analysis in Transforming Healthcare Delivery written by Peter J. Fabri and published by Springer. This book was released on 2016-07-20 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides the important concepts necessary for a physician to participate in a reengineering process, develop decision-making skills based on probability and logic rather than “rules,” and to measure and analyze meaningful outcomes of care delivery. This approach has been developed over ten years in a medical student-based program and has been enthusiastically embraced by medical students without backgrounds in engineering or statistics. More specifically, this text will introduce physicians to relevant and available computer software, combined with an in depth knowledge of measurement, variation, and uncertainty. It provides a basis for the transformation of data into information, information into knowledge, and knowledge into wisdom. The first quarter of the book will address understanding and visualizing data, using statistical and graphic analysis. The next quarter addresses the fundamentals of applied statistics, and the application of conditional probability to clinical decision making. The next quarter addresses the four “cornerstones” of modern analytics: regression, classification, association analysis, and clustering. The final section addresses the identification of outliers and their importance in understanding, the assessment of cause and effect and the limitations associated with retrospective data analysis. This toolbox will prepare the interested physician to actively engage in the identification of problem areas, the design of process-based solutions, and the continuous assessment of outcomes of clinical practice. Armed with this toolbox, the reader will be “prepared to make a difference” in the rapidly changing world of healthcare delivery. Measurement and Analysis in Transforming Healthcare Delivery is an excellent resource for general practitioners, health administrators, and all medical professionals interacting with healthcare delivery. /div

Download or read book Modeling and Analysis of Patient Transitions in Healthcare Delivery Systems written by Wenjun Zhu (Ph.D.) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Supply Chain Engineering and Logistics Handbook written by Erick C. Jones and published by CRC Press. This book was released on 2019-11-12 with total page 734 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook begins with the history of Supply Chain (SC) Engineering, it goes on to explain how the SC is connected today, and rounds out with future trends. The overall merit of the book is that it introduces a framework similar to sundial that allows an organization to determine where their company may fall on the SC Technology Scale. The book will describe those who are using more historic technologies, companies that are using current collaboration tools for connecting their SC to other global SCs, and the SCs that are moving more towards cutting edge technologies. This book will be a handbook for practitioners, a teaching resource for academics, and a guide for military contractors. Some figures in the eBook will be in color. Presents a decision model for choosing the best Supply Chain Engineering (SCE) strategies for Service and Manufacturing Operations with respect to Industrial Engineering and Operations Research techniques Offers an economic comparison model for evaluating SCE strategies for manufacturing outsourcing as opposed to keeping operations in-house Demonstrates how to integrate automation techniques such as RFID into planning and distribution operations Provides case studies of SC inventory reductions using automation from AIT and RFID research Covers planning and scheduling, as well as transportation and SC theory and problems

Download or read book Modeling and Analysis of Patient Transitions in Healthcare Delivery Systems written by Wenjun Zhu (Ph.D.) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Knowledge Modelling and Big Data Analytics in Healthcare written by Mayuri Mehta and published by CRC Press. This book was released on 2021-12-08 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge Modelling and Big Data Analytics in Healthcare: Advances and Applications focuses on automated analytical techniques for healthcare applications used to extract knowledge from a vast amount of data. It brings together a variety of different aspects of the healthcare system and aids in the decision-making processes for healthcare professionals. The editors connect four contemporary areas of research rarely brought together in one book: artificial intelligence, big data analytics, knowledge modelling, and healthcare. They present state-of-the-art research from the healthcare sector, including research on medical imaging, healthcare analysis, and the applications of artificial intelligence in drug discovery. This book is intended for data scientists, academicians, and industry professionals in the healthcare sector.