Demystifying Big Data and Machine Learning for Healthcare

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...
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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.

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, this book proposes a new approach to creating a knowledge-driven learning organization-based on new and existing strategies, methods and technologies.

This book will investigate how hospitals and health systems 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 at hospitals and health systems. Finally, this book will address challenges and provide pragmatic recommendations on how to deal with them.