Modelling In Healthcare

A model is a simplified representation of a real-world situation used to help answer a specific question. This book provides a look into the world of modelling with the focus on modelling in healthcare.--[from preface]

How many patients will require admission to my hospital in two days? How widespread will influenza be in my community in two weeks? What will the changing demographics of our community do to affect demand for medical services in our region in two years? These and similar questions are the province of Modelling in Healthcare. This new volume, presented by the Complex Systems Modelling Group at Simon Fraser University in Canada, uses plain language, sophisticated mathematics and vivid examples to guide and instruct. Sage advice on the benefits and limitations of the modeling process and model pr.

This book demonstrates how to successfully manage and lead healthcare institutions by employing the logic of business model innovation to gain competitive advantages. Since clerk-like routines in professional organizations tend to overlook patient and service-centered healthcare solutions, it challenges the view that competition and collaboration in the healthcare sector should not only incorporate single-end services, therapies or diagnosis related groups. Moreover, the authors focus on holistic business models, which place greater emphasis on customer needs and put customers and patients first. The holistic business models approach addresses topics such as business operations, competitiveness, strategic business objectives, opportunities and threats, critical success factors and key performance indicators.The contributions cover various aspects of service business innovation such as reconfiguring the hospital business model in healthcare delivery, essential characteristics of service business model innovation in healthcare, guided business modeling and analysis for business professionals, patient-driven service delivery models in healthcare, and continuous and co-creative business model creation. All of the contributions introduce business models and strategies, process innovations, and toolkits that can be applied at the managerial level, ensuring the book will be of interest to healthcare professionals, hospital managers and consultants, as well as scholars, whose focus is on improving value-generating and competitive business architectures in the healthcare sector.

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.

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.

This text is listed on the Course of Reading for SOA Fellowship study in the Group & Health specialty track. Healthcare Risk Adjustment and Predictive Modeling provides a comprehensive guide to healthcare actuaries and other professionals interested in healthcare data analytics, risk adjustment and predictive modeling. The book first introduces the topic with discussions of health risk, available data, clinical identification algorithms for diagnostic grouping and the use of grouper models. The second part of the book presents the concept of data mining and some of the common approaches used by modelers. The third and final section covers a number of predictive modeling and risk adjustment case-studies, with examples from Medicaid, Medicare, disability, depression diagnosis and provider reimbursement, as well as the use of predictive modeling and risk adjustment outside the U.S. For readers who wish to experiment with their own models, the book also provides access to a test dataset.

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.

In financially constrained health systems across the world, increasing emphasis is being placed on the ability to demonstrate that health care interventions are not only effective, but also cost-effective. This book deals with decision modelling techniques that can be used to estimate the value for money of various interventions including medical devices, surgical procedures, diagnostic technologies, and pharmaceuticals. Particular emphasis is placed on the importance of the appropriate representation of uncertainty in the evaluative process and the implication this uncertainty has for decision making and the need for future research. This highly practical guide takes the reader through the key principles and approaches of modelling techniques. It begins with the basics of constructing different forms of the model, the population of the model with input parameter estimates, analysis of the results, and progression to the holistic view of models as a valuable tool for informing future research exercises. Case studies and exercises are supported with online templates and solutions. This book will help analysts understand the contribution of decision-analytic modelling to the evaluation of health care programmes. ABOUT THE SERIES: Economic evaluation of health interventions is a growing specialist field, and this series of practical handbooks will tackle, in-depth, topics superficially addressed in more general health economics books. Each volume will include illustrative material, case histories and worked examples to encourage the reader to apply the methods discussed, with supporting material provided online. This series is aimed at health economists in academia, the pharmaceutical industry and the health sector, those on advanced health economics courses, and health researchers in associated fields.