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:\nDevelop skills needed to identify and demolish big-data myths\nBecome an expert in separating hype from reality\nUnderstand the V’s that matter in healthcare and why\nHarmonize the 4 C’s across little and big data\nChoose data fi delity over data quality\nLearn how to apply the NRF Framework\nMaster applied machine learning for healthcare\nConduct a guided tour of learning algorithms\nRecognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs)\nThe 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.

Demystifying Big Data and Machine Learning for Healthcare

Business & Money

Smaltz, Detlev H.

Natarajan, Prashant

Frenzel, John C.

Industries

CRC-Press

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: <ul>
<li>Develop skills needed to identify and demolish big-data myths</li> <li>Become an expert in separating hype from reality</li> <li>Understand the V’s that matter in healthcare and why</li> <li>Harmonize the 4 C’s across little and big data</li> <li>Choose data fi delity over data quality</li> <li>Learn how to apply the NRF Framework </li> <li>Master applied machine learning for healthcare</li> <li>Conduct a guided tour of learning algorithms</li> <li>Recognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs)</li> </ul>
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.

Demystifying Big Data and Machine Learning for Healthcare (Himss Book)

This bestselling text provides managers with the knowledge, skills, and competencies necessary to effectively manage healthcare information systems. Key Features: New and revised material includes: Expanded discussion of strategic planning, including the importance of system integration and IM/IT governance Extensive updates to the project management chapter, including information on establishing a centralized IM/IT portfolio management office (PMO) to improve project success rates A new chapter on the government s role in IM/IT, including the impact of HIPAA and other legislation A new chapter that explains how IM/IT investments are evaluated and provides a framework for conducting these analyses Updated information on the electronic health record and other clinical and administrative applications used in healthcare enterprises A comprehensive profile of hospital IM/IT leadership, including the role of the Chief Information Officer (CIO) Each chapter features learning objectives, web resources, and discussion questions Includes a case that illustrates the design of an EMR in a multi-specialty group practice Includes a glossary that clarifies technical terms

Austin and Boxerman's Information Systems For Healthcare Management

Glandon, Gerald L.

Slovensky, Donna J.

Management & Leadership

Health Administration Pr

<div>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. 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.</div>

Information Technology for Healthcare Managers, Ninth edition

AUPHA/HAP Book

The editors of the HIMSS Books' best-seller Health: From Smartphones to Smart Systems have returned to deliver an expansive survey of the initiatives, innovators, and technologies driving the patient-centered mobile healthcare revolution. mHealth Innovation: Best Practices from the Mobile Frontier explores the promise of mHealth as a balance between emerging technologies and process innovations leading to improved outcomes-with the ultimate aim of creating a patient-centered and consumer-driven healthcare ecosystem. Examining the rapidly changing mobile healthcare environment from myriad perspectives, the book includes a comprehensive survey of the current-state ecosystem-app development, interoperability, security, standards, organizational and governmental policy, innovation, next-generation solutions, and mBusiness-and 20 results-driven, world-spanning case studies covering behavior change, patient engagement, patient-provider decision making, mobile gaming, mobile prescription therapy, home monitoring, mobile-to-mobile online delivery, access to care, app certification and quality evaluations, mixed media campaigns, and much more.

Analytics in Healthcare: An Introduction (HIMSS Book Series)

Adams, James

Gaston, James E.

Garets, David

Melton, Genevieve B.

Ott, Kim

Simon, György J.

VanNorman, Samuel

Wells, Nathaniel A.

HIMSS Publishing

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