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Data Analytics And Stochastic Models For Informed Decision Making In Healthcare
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Book Synopsis Data Analytics and Stochastic Models for Informed Decision Making in Healthcare by : Coralys M. Colón Morales
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.
Book Synopsis Applications of Stochastic Modeling and Data Analytics Techniques in Healthcare Decision Making by : Ozden Onur Dalgic
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.
Book Synopsis Stochastic Modeling And Analytics In Healthcare Delivery Systems by : Jingshan Li
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.
Book Synopsis Decision Analytics and Optimization in Disease Prevention and Treatment by : Nan Kong
Download or read book Decision Analytics and Optimization in Disease Prevention and Treatment written by Nan Kong and published by John Wiley & Sons. This book was released on 2018-02-02 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: A systematic review of the most current decision models and techniques for disease prevention and treatment Decision Analytics and Optimization in Disease Prevention and Treatment offers a comprehensive resource of the most current decision models and techniques for disease prevention and treatment. With contributions from leading experts in the field, this important resource presents information on the optimization of chronic disease prevention, infectious disease control and prevention, and disease treatment and treatment technology. Designed to be accessible, in each chapter the text presents one decision problem with the related methodology to showcase the vast applicability of operations research tools and techniques in advancing medical decision making. This vital resource features the most recent and effective approaches to the quickly growing field of healthcare decision analytics, which involves cost-effectiveness analysis, stochastic modeling, and computer simulation. Throughout the book, the contributors discuss clinical applications of modeling and optimization techniques to assist medical decision making within complex environments. Accessible and authoritative, Decision Analytics and Optimization in Disease Prevention and Treatment: Presents summaries of the state-of-the-art research that has successfully utilized both decision analytics and optimization tools within healthcare operations research Highlights the optimization of chronic disease prevention, infectious disease control and prevention, and disease treatment and treatment technology Includes contributions by well-known experts from operations researchers to clinical researchers, and from data scientists to public health administrators Offers clarification on common misunderstandings and misnomers while shedding light on new approaches in this growing area Designed for use by academics, practitioners, and researchers, Decision Analytics and Optimization in Disease Prevention and Treatment offers a comprehensive resource for accessing the power of decision analytics and optimization tools within healthcare operations research.
Book Synopsis Advanced Prognostic Predictive Modelling in Healthcare Data Analytics by : Sudipta Roy
Download or read book Advanced Prognostic Predictive Modelling in Healthcare Data Analytics written by Sudipta Roy and published by Springer Nature. This book was released on 2021-04-22 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses major technical advancements and research findings in the field of prognostic modelling in healthcare image and data analysis. The use of prognostic modelling as predictive models to solve complex problems of data mining and analysis in health care is the feature of this book. The book examines the recent technologies and studies that reached the practical level and becoming available in preclinical and clinical practices in computational intelligence. The main areas of interest covered in this book are highest quality, original work that contributes to the basic science of processing, analysing and utilizing all aspects of advanced computational prognostic modelling in healthcare image and data analysis.
Book Synopsis Data Analytics and Stochastic Optimization Models for Decision Support in Chronic Disease Operations Management by : Mohammad Hessam Olya
Download or read book Data Analytics and Stochastic Optimization Models for Decision Support in Chronic Disease Operations Management written by Mohammad Hessam Olya and published by . This book was released on 2019 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: The results of this study show that feature representation and training related instances jointly increase the performance of patient workload prediction. Moreover, we have addressed two critical issues in team-based healthcare strategic and tactical planning. The first issue is to determine the optimal number of providers for multiple facilities and eligible patients for pay-to-travel incentives where the demand and location of patients are unknown. The second issue is to minimize the number of different healthcare teams and balance their workload within every single facility. We have developed a stochastic workforce and workload optimization model under various scenarios to address this issue. The result of prescriptive analysis suggests considering the randomness rather than replacing the stochastic variables by their expected value significantly contributes in reducing the overall cost of healthcare and practically enhancing access to care.
Book Synopsis Healthcare Data Analytics by : Chandan K. Reddy
Download or read book Healthcare Data Analytics written by Chandan K. Reddy and published by CRC Press. This book was released on 2015-06-23 with total page 756 pages. Available in PDF, EPUB and Kindle. Book excerpt: At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear understanding of the analytical techniques currently available
Book Synopsis Decision Making in Healthcare Systems by : Tofigh Allahviranloo
Download or read book Decision Making in Healthcare Systems written by Tofigh Allahviranloo and published by Springer Nature. This book was released on 2023-12-31 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book chooses the topic which is due to the editors' experience in modeling projects in healthcare systems. Also, the transfer of experiences is the reason why mathematical modeling and decision making in the field of health are not given much attention. To this end, the new aspect of this book is the lack of reference needed to carry out projects in the field of health for researchers whose main expertise is not modeling. Students of health, mathematics, management, and industrial engineering fields are in the direct readership with this book. Different projects in the field of healthcare systems can use the topics presented in different chapters mentioned in this book.
Book Synopsis EBOOK: Analytical Models for Decision-Making by : Colin Sanderson
Download or read book EBOOK: Analytical Models for Decision-Making written by Colin Sanderson and published by McGraw-Hill Education (UK). This book was released on 2006-03-16 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Health care systems are complex and, as a result, it is often unclear what the effects of changes in policy or service provision might be. At the same time, resources for health care tend to be in short supply, which means that public health practitioners have to make difficult decisions. This book describes the quantitative and qualitative methods that can help decision-makers to structure and clarify difficult problems and to explore the implications of pursuing different options. The accompanying CD ROM provides the opportunity to try out some of the proposed solutions. The book examines: Models and decision-making in health care Methods for clarifying complex decisions Models for service planning and resource allocation Modelling for evaluating changes in systems Series Editors: Rosalind Plowman and Nicki Thorogood.
Book Synopsis Stochastic Modeling and Decision Making in Two Healthcare Applications by : Pengyi Shi
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.
Book Synopsis Stochastic Models and Data Driven Simulations for Healthcare Operations by : Ross Michael Anderson
Download or read book Stochastic Models and Data Driven Simulations for Healthcare Operations written by Ross Michael Anderson and published by . This book was released on 2014 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis considers problems in two areas in the healthcare operations: Kidney Paired Donation (KPD) and scheduling medical residents in hospitals. In both areas, we explore the implications of policy change through high fidelity simulations. We then build stochastic models to provide strategic insight into how policy decisions affect the operations of these healthcare systems. KPD programs enable patients with living but incompatible donors (referred to as patient-donor pairs) to exchange kidneys with other such pairs in a centrally organized clearing house. Exchanges involving two or more pairs are performed by arranging the pairs in a cycle, where the donor from each pair gives to the patient from the next pair. Alternatively, a so called altruistic donor can be used to initiate a chain of transplants through many pairs, ending on a patient without a willing donor. In recent years, the use of chains has become pervasive in KPD, with chains now accounting for the majority of KPD transplants performed in the United States. A major focus of our work is to understand why long chains have become the dominant method of exchange in KPD, and how to best integrate their use into exchange programs. In particular, we are interested in policies that KPD programs use to determine which exchanges to perform, which we refer to as matching policies. First, we devise a new algorithm using integer programming to maximize the number of transplants performed on a fixed pool of patients, demonstrating that matching policies which must solve this problem are implementable. Second, we evaluate the long run implications of various matching policies, both through high fidelity simulations and analytic models. Most importantly, we find that: (1) using long chains results in more transplants and reduced waiting time, and (2) the policy of maximizing the number of transplants performed each day is as good as any batching policy. Our theoretical results are based on introducing a novel model of a dynamically evolving random graph. The analysis of this model uses classical techniques from Erdos-Renyi random graph theory as well as tools from queueing theory including Lyapunov functions and Little's Law. In the second half of this thesis, we consider the problem of how hospitals should design schedules for their medical residents. These schedules must have capacity to treat all incoming patients, provide quality care, and comply with regulations restricting shift lengths. In 2011, the Accreditation Council for Graduate Medical Education (ACGME) instituted a new set of regulations on duty hours that restrict shift lengths for medical residents. We consider two operational questions for hospitals in light of these new regulations: will there be sufficient staff to admit all incoming patients, and how will the continuity of patient care be affected, particularly in a first day of a patients hospital stay, when such continuity is critical? To address these questions, we built a discrete event simulation tool using historical data from a major academic hospital, and compared several policies relying on both long and short shifts. The simulation tool was used to inform staffing level decisions at the hospital, which was transitioning away from long shifts. Use of the tool led to the following strategic insights. We found that schedules based on shorter more frequent shifts actually led to a larger admitting capacity. At the same time, such schedules generally reduce the continuity of care by most metrics when the departments operate at normal loads. However, in departments which operate at the critical capacity regime, we found that even the continuity of care improved in some metrics for schedules based on shorter shifts, due to a reduction in the use of overtime doctors. We develop an analytically tractable queueing model to capture these insights. The analysis of this model requires analyzing the steady-state behavior of the fluid limit of a queueing system, and proving a so called "interchange of limits" result.
Book Synopsis Data Mining and Analytics in Healthcare Management by : David L. Olson
Download or read book Data Mining and Analytics in Healthcare Management written by David L. Olson and published by Springer Nature. This book was released on 2023-04-20 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents data mining methods in the field of healthcare management in a practical way. Healthcare quality and disease prevention are essential in today’s world. Healthcare management faces a number of challenges, e.g. reducing patient growth through disease prevention, stopping or slowing disease progression, and reducing healthcare costs while improving quality of care. The book provides an overview of current healthcare management problems and highlights how analytics and knowledge management have been used to better cope with them. It then demonstrates how to use descriptive and predictive analytics tools to help address these challenges. In closing, it presents applications of software solutions in the context of healthcare management. Given its scope, the book will appeal to a broad readership, from researchers and students in the operations research and management field to practitioners such as data analysts and decision-makers who work in the healthcare sector.
Book Synopsis Knowledge Modelling and Big Data Analytics in Healthcare by : Mayuri Mehta
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.
Book Synopsis Healthcare Decision Making and Stochastic Model Predictive Control by : Martin Arno Sehr
Download or read book Healthcare Decision Making and Stochastic Model Predictive Control written by Martin Arno Sehr and published by . This book was released on 2017 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model Predictive Control has become a prevailing technique in practice by virtue of its natural inclusion of constraint enforcement in sub-optimal feedback design through repeated solution of finite-horizon, open-loop control problems. However, many approaches are lacking in proper accommodation of output feedback using imperfect measurements, as is normally required in practice. The conventional workaround for this disconnect between control theory and practice is the use of certainty equivalent control laws, which subsume best available state estimates in place of the system state in order to salvage methods available for state-feedback Model Predictive Control. This dissertation explores Stochastic Model Predictive Control in the general, nonlinear output-feedback setting. Starting the receding horizon development from Stochastic Optimal Control, we attain inherent accommodation of imperfect measurement data through propagation of the conditional state density, the information state. This setup further results in the control signals being of dual, probing nature: the control balances the typically antagonistic requirements of regulation and exploration. However, these conflicting tasks inherent to Stochastic Optimal Control also embody the associated computational intractability. While properties such as optimal probing and numerical performance bounds on the infinite time-horizon require solution of Stochastic Optimal Control problems, obtaining these solutions is typically not possible in practice due to the exorbitant computational demands. We suggest two methods for tractable Stochastic Model Predictive Control. Firstly, we propose approximation of the information state update by a Particle Filter, which may be merged naturally with scenario optimization to generate control laws. While computationally tractable, this method does not maintain duality without additional measures. Alternatively, the nonlinear output-feedback problem can be approximated--or even cast--as a Partially Observable Markov Decision Process, a special class of systems for which Stochastic Optimal Control is numerically tractable for reasonable problem size, enabling dual optimal control with provable infinite-horizon properties. Throughout this dissertation, we examine two classes of examples from healthcare: individualized appointment scheduling, a problem not requiring duality; medical treatment decision making, where dual control decisions are often required to balance optimally when to order diagnostic tests and when to apply medical intervention.
Book Synopsis Practical Predictive Analytics and Decisioning Systems for Medicine by : Gary D. Miner
Download or read book Practical Predictive Analytics and Decisioning Systems for Medicine written by Gary D. Miner and published by Academic Press. This book was released on 2014-09-27 with total page 1111 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advent of electronic medical records years ago and the increasing capabilities of computers, our healthcare systems are sitting on growing mountains of data. Not only does the data grow from patient volume but the type of data we store is also growing exponentially. Practical Predictive Analytics and Decisioning Systems for Medicine provides research tools to analyze these large amounts of data and addresses some of the most pressing issues and challenges where data integrity is compromised: patient safety, patient communication, and patient information. Through the use of predictive analytic models and applications, this book is an invaluable resource to predict more accurate outcomes to help improve quality care in the healthcare and medical industries in the most cost–efficient manner.Practical Predictive Analytics and Decisioning Systems for Medicine provides the basics of predictive analytics for those new to the area and focuses on general philosophy and activities in the healthcare and medical system. It explains why predictive models are important, and how they can be applied to the predictive analysis process in order to solve real industry problems. Researchers need this valuable resource to improve data analysis skills and make more accurate and cost-effective decisions. Includes models and applications of predictive analytics why they are important and how they can be used in healthcare and medical research Provides real world step-by-step tutorials to help beginners understand how the predictive analytic processes works and to successfully do the computations Demonstrates methods to help sort through data to make better observations and allow you to make better predictions
Book Synopsis Big Data Analytics for Healthcare by : Pantea Keikhosrokiani
Download or read book Big Data Analytics for Healthcare written by Pantea Keikhosrokiani and published by Academic Press. This book was released on 2022-05-19 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics and Medical Information Systems presents the valuable use of artificial intelligence and big data analytics in healthcare and medical sciences. It focuses on theories, methods and approaches in which data analytic techniques can be used to examine medical data to provide a meaningful pattern for classification, diagnosis, treatment, and prediction of diseases. The book discusses topics such as theories and concepts of the field, and how big medical data mining techniques and applications can be applied to classification, diagnosis, treatment, and prediction of diseases. In addition, it covers social, behavioral, and medical fake news analytics to prevent medical misinformation and myths. It is a valuable resource for graduate students, researchers and members of biomedical field who are interested in learning more about analytic tools to support their work. Presents theories, methods and approaches in which data analytic techniques are used for medical data Brings practical information on how to use big data for classification, diagnosis, treatment, and prediction of diseases Discusses social, behavioral, and medical fake news analytics for medical information systems
