Tag Archives: Machine Learning

PopHealth Week with Prashant Natarajan – Big Data, Machine Learning and AI

Prashant Natarajan, Director of Business Strategy, Oracle Corporation

Discussing Big Data, Machine Learning, AI and Healthcare


Prashant Natarajan on PopHealth Week

Prashant Natarajan is Director of Product Strategy at Oracle, where he is responsible for business strategy, product management, and go-to market solutions for a portfolio of Prashantinformatics products & cloud services for  population health, precision medicine, interoperability, and integrated little + big data analytics. Prashant received his chemical engineering degree from Mangalore University (India) in 1998 and his master’s degree in technical and professional communications from Auburn University (USA) in 2005. He is a prior recipient of the SBC/Chancellor’s Endowed Fellowship for graduate research.

Prashant is a lead author or contributor to 4 books on analytics, machine learning & AI, and precision medicine. He serves on the Board of Advisors for Council for Affordable Health Coverage. He is also Industry Advisor for Data Science & AI at UCSF/CIAPM. Prashant currently serves as Chairperson of the HIMSS NorCal Chapter’s annual Innovation Conference & Showcase.

Follow Prashant’s work via Big Data CXO.


PopHealth Week is Produced by Health Innovation Media




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Data, Devices and Doers – The Three D’s for Creating Accountable Health

I recently had a conversation with the President of one of the larger big data health care analytics companies and as we discussed what the future might look like the cliché “lightbulb” went off  – DataDevicesDoers. The infamous three-legged stool to support our health care future.  So lets discuss  how these three components need to come together to solve our health care crisis.


The idea of big data while not new, is new to health care and we are seeing a proliferation of companies enter this space with machine learning and natural language processing systems going through huge amounts of structured and unstructured data looking for patterns, identifying the unique and providing usable information to groups seeking to manage or provide services to these populations.


Mobile is taking over the world, as QualcommLife has pointed out so well with their slide on the number of mobile devices:


The  “super-computer on the hip” now allows for the collection of huge amounts of user-generated data about health and behavior and even non smart phones using text messaging technology can collect and send back valuable information.  It has also become clear that these devices have a profound impact on changing the behavior of the users. We check them all the time, we won’t leave home without them, etc. as Jay Walker from TEDMED said at last years Care Continuum Alliance Conference “they are the on ramp to our lives.”

While the realtime biometric data coming in from these devices and connected sensors will be enormously helpful, it’s the behavioral piece that I find most fascinating.  This behavioral data, analyzed by the Big Data engine, can offer unique insights into the individual, allowing segmentation and the optimization of feedback and behavior change approaches which can then be leveraged back through the device to the user providing actionable advice or  a “nudge” to drive  behaviors to improve ones health. The best feature of the device is it’s a two-way street.

Which  brings us to the third leg the:


At the end of the day, changing the health of populations requires changing the health of individual’s, one at a time.  These are the Doers. How can we create intrinsically motivated Doers accountable for their own health? We do that by leveraging the Device that is already changing user behavior,  through an analysis of their unique Data to provide actionable information at the right time, place and in a meaningful behavioral approach that is most likely to be accepted and begin the change they and we in the health community seek.

So through this Data aggregation and Device we can see if we got the change we were looking for in the Doer, thereby creating another loop and more knowledge for the system.

An Accountable Health Loop based on the best knowledge, information, analysis and connection, targeted to an n of 1 to move a population of billions.

Thanks to QualcommLife for providing me with copies of their slides.

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