Demystifying Big Data and Machine Learning for Healthcare | Prashant Natarajan | HIMSS18-11
On this episode, we chat with Prashant Natarajan, an innovation and product leader, best-selling author on Big Data, Analytics, Machine Learning and AI, and, if I do say so myself, an all-around swell guy! Prashant takes a very pragmatic approach to demystifying big data, ML and AI and shows us that it all starts with the data. He introduces us to the concept of data fidelity, which is the appropriateness of data for a purpose. That concept is so important to grasp. It allows us to get started where we are, drive towards value over time and enables us to get value from innovative tools like machine learning an AI.
We don’t spend all our time in the data weeds. Prashant also tells us how we can operationalize big data, machine learning and artificial intelligence and align it with the current and future needs of healthcare. It can support us in our fee-for-service and value-based contracts. It enables consumerism and patient engagement. It can help employer groups drive change. In short, with data fidelity, big data/ML/AI become more than headlines and begin to drive real value in healthcare.
This interview is part of our HIMSS18 coverage. We’ll be talking with thought leaders and vendors all week at the annual Health Information Management Society conference in Las Vegas.
About Prashant Natarajan
Prashant is an innovation and product leader and a consultant with an award-winning track record of conceptualizing & delivering innovative solutions. He is a best-selling author on topics in big data, analytics, machine learning, AI and precision medicine. He is also a Senior Fellow at HeathITnow.
About Demystifying Big Data and Machine Learning for Healthcare
I find this book’s practical approach to enabling big data and machine learning through proper data management to be very refreshing. It lays out many of the data quality and analytics concepts that it took me 15+ years to learn the hard way. I strongly recommend this book to anyone working on data and analytics problems in 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 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 fidelity 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.
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