This book 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. 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.

Introduction -- Healthcare and the big data V's -- Big data : how to get started -- Big data : challenges -- Best practices : separating myth from reality -- Big data advanced topics -- Applied machine learning for healthcare -- Introduction to case studies -- Penn medicine : precision medicine and big data -- Ascension : our advanced analytics journey -- University of Texas MD Anderson : streaming analytics -- US health insurance organization : financial reporting analytics with big data -- CIAPM : California Initiative to Advance Precision Medicine -- University of California San Francisco : AI for imaging of neurological emergencies -- BayCare health system : actionable, agile analytics using data variety -- Arterys : deep learning for medical imaging -- Big data technical glossary.