Data Day Health Conference Asks Deep Questions About Health Care IT


By: Larry Loeb|


Austin, Texas, is trying to build on the entrepreneurial and research activity that has entered around the city and help institutionalize the practices that have worked well for innovation in the past. For the last five years, the city has hosted a conference called Data Day Texas focusing on big data and its uses.

Day Day Health

A new conference, Data Day Health, ran for the first time on Jan. 15, 2017, one day after Data Day Texas, widening its scope to look at the combination of data, health and medicine. The catchphrase for the new event was “data in the service of medicine.” Among other topics, the presentations focused on how to improve health-data reproducibility and obtain the correct information for each patient.

One of the sessions addressed the “most fundamental” topic in health care advancement: secure, system-wide data plumbing. The presenters say that they’ve come up with a block-chain system that will provide cost transparency, in-network provider recommendations, lower claim-management costs and a universal health identity as a bonus. They look for data integration to ensure transparency, access and options in health care.

A New Type of Business

Health organizations are businesses, too, and they have to be considered from that perspective. In a talk entitled “Biorevolutions: A Data Science Approach to Success in Biotechnology,” the presenters asked which biotech companies are going to be good investments in the coming years. The speakers used familiar applied data science techniques to analyze some biotech startups’ success rates.

Of course, because of the plethora of growing startups, some new words popped up in an effort to describe the ideas these companies put forth. One such innovation is the term “radiomics,” a shorthand for “imaging phenotype” and the central concept in a presentation by Sanjay Joshi of Dell EMC. While some work has already been ongoing in this area, it’s typically involved smaller data sets, Joshi noted. His session delineated how this sort of investigation can be ramped up to provide clinical utility in the real world.

Part of Joshi’s talk touched on how artificial intelligence (AI) will play a much larger role in this effort. Of course, that’s not an uncommon attitude these days: Image recognition has evolved far enough that it can be considered an available web service that may be used only if it’s needed. Using AI to analyze recognized structures and assign clinical implications based on them seems like an intuitive, straight-line approach from the existing research and the available data.

Presenters at Data Day Health also investigated AI for its clinical use with data coming from Internet of Things devices. One session talked about creating an algorithm for early epileptic-seizure detection based on wearable tech, as well as detecting an individual’s activities through sensor data.

Conferences like this one can only improve the integration of today’s most cutting-edge data techniques into the fabric of health care systems and decision-making.

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