Detlev H. Smaltz

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.

Though healthcare is largely technology driven, the deployment of health information technology (HIT) has occurred in waves rather than a steady flow, and usually in response to government mandates. This emergent HIT strategy has culminated in highly complex and dynamic systems crafted over many years using products from multiple vendors. Healthcare organizations are now focused on big data aggregated from myriad data-producing applications both in and beyond the enterprise. Healthcare leaders must position themselves to leverage the new opportunities that arise from HIT's ascendance and to mine the vast amount of available data for competitive advantage. Where can they turn for insight? With the unique advantage of both academic and real-world experience in HIT leadership, the authors of Information Technology for Healthcare Managers blend management theory, cutting-edge tech knowledge, and a thorough grounding in the healthcare applications of technology. The ninth edition offers the following: - Rigorously updated content that reflects this rapidly changing field, including discussions of the Internet of Things, artificial intelligence, blockchain, cybersecurity, and cloud computing - An entirely new chapter on data analytics for leaders who want to harness the power of data for decision support, evidence-based management, and other strategic uses - A review of the core HIT elements of healthcare delivery - Guidance on management issues specific to healthcare organizations, including regulation, HIT and data governance, infrastructure, the selection of applications, contract negotiating, and portfolio management - Management and systems theory, applied to the challenges of HIT Opinions abound on technology's best uses for society, but healthcare organizations need more than opinion--they need knowledge and strategy. This book will help leaders combine tech savvy with business savvy for sustainable success in a dynamic environment.

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.