Data-Driven Healthcare
Data-Driven Healthcare

Author: Laura B. Madsen

Publisher: John Wiley & Sons

Published: 2014-09-23

Total Pages: 224

ISBN-13: 1118973895

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Healthcare is changing, and data is the catalyst Data is taking over in a powerful way, and it's revolutionizing the healthcare industry. You have more data available than ever before, and applying the right analytics can spur growth. Benefits extend to patients, providers, and board members, and the technology can make centralized patient management a reality. Despite the potential for growth, many in the industry and government are questioning the value of data in health care, wondering if it's worth the investment. Data-Driven Healthcare: How Analytics and BI are Transforming the Industry tackles the issue and proves why BI is not only worth it, but necessary for industry advancement. Healthcare BI guru Laura Madsen challenges the notion that data have little value in healthcare, and shows how BI can ease regulatory reporting pressures and streamline the entire system as it evolves. Madsen illustrates how a data-driven organization is created, and how it can transform the industry. Learn why BI is a boon to providers Create powerful infographics to communicate data more effectively Find out how Big Data has transformed other industries, and how it applies to healthcare Data-Driven Healthcare: How Analytics and BI are Transforming the Industry provides tables, checklists, and forms that allow you to take immediate action in implementing BI in your organization. You can't afford to be behind the curve. The industry is moving on, with or without you. Data-Driven Healthcare: How Analytics and BI are Transforming the Industry is your guide to utilizing data to advance your operation in an industry where data-fueled growth will be the new norm.

Data Driven Approaches for Healthcare
Data Driven Approaches for Healthcare

Author: Chengliang Yang

Publisher: CRC Press

Published: 2019-10-01

Total Pages: 119

ISBN-13: 1000700038

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Health care utilization routinely generates vast amounts of data from sources ranging from electronic medical records, insurance claims, vital signs, and patient-reported outcomes. Predicting health outcomes using data modeling approaches is an emerging field that can reveal important insights into disproportionate spending patterns. This book presents data driven methods, especially machine learning, for understanding and approaching the high utilizers problem, using the example of a large public insurance program. It describes important goals for data driven approaches from different aspects of the high utilizer problem, and identifies challenges uniquely posed by this problem. Key Features: Introduces basic elements of health care data, especially for administrative claims data, including disease code, procedure codes, and drug codes Provides tailored supervised and unsupervised machine learning approaches for understanding and predicting the high utilizers Presents descriptive data driven methods for the high utilizer population Identifies a best-fitting linear and tree-based regression model to account for patients’ acute and chronic condition loads and demographic characteristics

MoneyBall Medicine
MoneyBall Medicine

Author: Harry Glorikian

Publisher: Taylor & Francis

Published: 2017-11-20

Total Pages: 591

ISBN-13: 1351984330

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How can a smartwatch help patients with diabetes manage their disease? Why can’t patients find out prices for surgeries and other procedures before they happen? How can researchers speed up the decade-long process of drug development? How will "Precision Medicine" impact patient care outside of cancer? What can doctors, hospitals, and health systems do to ensure they are maximizing high-value care? How can healthcare entrepreneurs find success in this data-driven market? A revolution is transforming the $10 trillion healthcare landscape, promising greater transparency, improved efficiency, and new ways of delivering care. This new landscape presents tremendous opportunity for those who are ready to embrace the data-driven reality. Having the right data and knowing how to use it will be the key to success in the healthcare market in the future. We are already starting to see the impacts in drug development, precision medicine, and how patients with rare diseases are diagnosed and treated. Startups are launched every week to fill an unmet need and address the current problems in the healthcare system. Digital devices and artificial intelligence are helping doctors do their jobs faster and with more accuracy. MoneyBall Medicine: Thriving in the New Data-Driven Healthcare Market, which includes interviews with dozens of healthcare leaders, describes the business challenges and opportunities arising for those working in one of the most vibrant sectors of the world’s economy. Doctors, hospital administrators, health information technology directors, and entrepreneurs need to adapt to the changes effecting healthcare today in order to succeed in the new, cost-conscious and value-based environment of the future. The authors map out many of the changes taking place, describe how they are impacting everyone from patients to researchers to insurers, and outline some predictions for the healthcare industry in the years to come.

An Introduction to Healthcare Informatics
An Introduction to Healthcare Informatics

Author: Peter Mccaffrey

Publisher: Academic Press

Published: 2020-07-29

Total Pages: 342

ISBN-13: 0128149167

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An Introduction to Healthcare Informatics: Building Data-Driven Tools bridges the gap between the current healthcare IT landscape and cutting edge technologies in data science, cloud infrastructure, application development and even artificial intelligence. Information technology encompasses several rapidly evolving areas, however healthcare as a field suffers from a relatively archaic technology landscape and a lack of curriculum to effectively train its millions of practitioners in the skills they need to utilize data and related tools. The book discusses topics such as data access, data analysis, big data current landscape and application architecture. Additionally, it encompasses a discussion on the future developments in the field. This book provides physicians, nurses and health scientists with the concepts and skills necessary to work with analysts and IT professionals and even perform analysis and application architecture themselves. - Presents case-based learning relevant to healthcare, bringing each concept accompanied by an example which becomes critical when explaining the function of SQL, databases, basic models etc. - Provides a roadmap for implementing modern technologies and design patters in a healthcare setting, helping the reader to understand both the archaic enterprise systems that often exist in hospitals as well as emerging tools and how they can be used together - Explains healthcare-specific stakeholders and the management of analytical projects within healthcare, allowing healthcare practitioners to successfully navigate the political and bureaucratic challenges to implementation - Brings diagrams for each example and technology describing how they operate individually as well as how they fit into a larger reference architecture built upon throughout the book

Secondary Analysis of Electronic Health Records
Secondary Analysis of Electronic Health Records

Author: MIT Critical Data

Publisher: Springer

Published: 2016-09-09

Total Pages: 435

ISBN-13: 3319437429

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This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.

Integrating Social Care into the Delivery of Health Care
Integrating Social Care into the Delivery of Health Care

Author: National Academies of Sciences, Engineering, and Medicine

Publisher: National Academies Press

Published: 2020-01-30

Total Pages: 195

ISBN-13: 0309493439

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Integrating Social Care into the Delivery of Health Care: Moving Upstream to Improve the Nation's Health was released in September 2019, before the World Health Organization declared COVID-19 a global pandemic in March 2020. Improving social conditions remains critical to improving health outcomes, and integrating social care into health care delivery is more relevant than ever in the context of the pandemic and increased strains placed on the U.S. health care system. The report and its related products ultimately aim to help improve health and health equity, during COVID-19 and beyond. The consistent and compelling evidence on how social determinants shape health has led to a growing recognition throughout the health care sector that improving health and health equity is likely to depend â€" at least in part â€" on mitigating adverse social determinants. This recognition has been bolstered by a shift in the health care sector towards value-based payment, which incentivizes improved health outcomes for persons and populations rather than service delivery alone. The combined result of these changes has been a growing emphasis on health care systems addressing patients' social risk factors and social needs with the aim of improving health outcomes. This may involve health care systems linking individual patients with government and community social services, but important questions need to be answered about when and how health care systems should integrate social care into their practices and what kinds of infrastructure are required to facilitate such activities. Integrating Social Care into the Delivery of Health Care: Moving Upstream to Improve the Nation's Health examines the potential for integrating services addressing social needs and the social determinants of health into the delivery of health care to achieve better health outcomes. This report assesses approaches to social care integration currently being taken by health care providers and systems, and new or emerging approaches and opportunities; current roles in such integration by different disciplines and organizations, and new or emerging roles and types of providers; and current and emerging efforts to design health care systems to improve the nation's health and reduce health inequities.

Artificial Intelligence in Healthcare
Artificial Intelligence in Healthcare

Author: Adam Bohr

Publisher: Academic Press

Published: 2020-06-21

Total Pages: 385

ISBN-13: 0128184396

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Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data

Demystifying Big Data and Machine Learning for Healthcare
Demystifying Big Data and Machine Learning for Healthcare

Author: Prashant Natarajan

Publisher: CRC Press

Published: 2017-02-15

Total Pages: 227

ISBN-13: 1315389304

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Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization’s day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. Demystifying Big Data and Machine Learning for Healthcare investigates how healthcare organizations can leverage this tapestry of big data to discover new business value, use cases, and knowledge as well as how big data can be woven into pre-existing business intelligence and analytics efforts. This book focuses on teaching you how to: Develop skills needed to identify and demolish big-data myths Become an expert in separating hype from reality Understand the V’s that matter in healthcare and why Harmonize the 4 C’s across little and big data Choose data fi delity over data quality Learn how to apply the NRF Framework Master applied machine learning for healthcare Conduct a guided tour of learning algorithms Recognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs) The variety of data in healthcare spans multiple business workflows, formats (structured, un-, and semi-structured), integration at point of care/need, and integration with existing knowledge. In order to deal with these realities, the authors propose new approaches to creating a knowledge-driven learning organization-based on new and existing strategies, methods and technologies. This book will address the long-standing challenges in healthcare informatics and provide pragmatic recommendations on how to deal with them.

Healthcare and Artificial Intelligence
Healthcare and Artificial Intelligence

Author: Bernard Nordlinger

Publisher: Springer Nature

Published: 2020-03-17

Total Pages: 275

ISBN-13: 3030321614

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This book provides an overview of the role of AI in medicine and, more generally, of issues at the intersection of mathematics, informatics, and medicine. It is intended for AI experts, offering them a valuable retrospective and a global vision for the future, as well as for non-experts who are curious about this timely and important subject. Its goal is to provide clear, objective, and reasonable information on the issues covered, avoiding any fantasies that the topic “AI” might evoke. In addition, the book seeks to provide a broad kaleidoscopic perspective, rather than deep technical details.

Healthcare Service Management
Healthcare Service Management

Author: Li Tao

Publisher: Springer

Published: 2019-05-08

Total Pages: 183

ISBN-13: 3030153851

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Healthcare service systems are of profound importance in promoting the public health and wellness of people. This book introduces a data-driven complex systems modeling approach (D2CSM) to systematically understand and improve the essence of healthcare service systems. In particular, this data-driven approach provides new perspectives on health service performance by unveiling the causes for service disparity, such as spatio-temporal variations in wait times across different hospitals. The approach integrates four methods -- Structural Equation Modeling (SEM)-based analysis; integrated projection; service management strategy design and evaluation; and behavior-based autonomy-oriented modeling -- to address respective challenges encountered in performing data analytics and modeling studies on healthcare services. The thrust and uniqueness of this approach lies in the following aspects: Ability to explore underlying complex relationships between observed or latent impact factors and service performance. Ability to predict the changes and demonstrate the corresponding dynamics of service utilization and service performance. Ability to strategically manage service resources with the adaptation of unpredictable patient arrivals. Ability to figure out the working mechanisms that account for certain spatio-temporal patterns of service utilization and performance. To show the practical effectiveness of the proposed systematic approach, this book provides a series of pilot studies within the context of cardiac care in Ontario, Canada. The exemplified studies have unveiled some novel findings, e.g., (1) service accessibility and education may relieve the pressure of population size on service utilization; (2) functionally coupled units may have a certain cross-unit wait-time relationship potentially because of a delay cascade phenomena; (3) strategically allocating time blocks in operating rooms (ORs) based on a feedback mechanism may benefit OR utilization; (4) patients’ and hospitals’ autonomous behavior, and their interactions via wait times may bear the responsible for the emergence of spatio-temporal patterns observed in the real-world cardiac care system. Furthermore, this book presents an intelligent healthcare decision support (iHDS) system, an integrated architecture for implementing the data-driven complex systems modeling approach to developing, analyzing, investigating, supporting and advising healthcare related decisions. In summary, this book provides a data-driven systematic approach for addressing practical decision-support problems confronted in healthcare service management. This approach will provide policy makers, researchers, and practitioners with a practically useful way for examining service utilization and service performance in various ``what-if" scenarios, inspiring the design of effectiveness resource-allocation strategies, and deepening the understanding of the nature of complex healthcare service systems.