I’m in Boston this week at the Connected Health Symposium. I’ve attended a number of great sessions already, but I think I’m going to start with the one that left me wanting for more.
My first official session of the day was entitled “Big Data Healthcare Analytics: Frontier or Fiction?” The panelists included Charlie Baker (Entrepreneur at General Catalyst Partners), Chris Kryder, MD (CEO at D2Hawkeye), and Michael Weintraub (President and CEO of Humedica). As the session title implies, the overall message was “Buyer beware. The promise of Big Data in healthcare is more than can actually be realized today.” In essence, the speakers were stating that Big Data is a lot of talk but not a lot of action. However, as the discussion carried on it was not at all clear what the panelists considered “Big Data” to be. Several regulars on Twitter pointed this out:
@CortneyNic: Clearly we need to get the elementary definition of #bigdata covered first in this panel #cHealth12“
\0x200F@LukeGale Worth pointing out during #cHealth12 panel on big data that recent @IDC survey found half of respondents were unsure what big data means.
It was not until the end of the discussion that one of the panelists took a venture at providing a definition. Michael Weintraub defined Big Data as, “The use of data to modify behavior.” Charlie Baker further stated that instead of concentrating on Big Data we should instead focus on organizing the data we already have.
May I be the first to say “Huh?” First of all, in all of my discussions and learnings on the topic of Big Data (my MBA is in Business Intelligence), I’ve never heard it defined in this way. Second, what else is Big Data than a focus on the petabytes of data that already exist and trying to derive meaning from that enormous gob of data?
So, what is Big Data exactly? Like any good data scientist, I’m going to take it to Wikipedia first:
“In information technology, big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools. The challenges include capture, curation, storage, search, sharing, analysis, and visualization. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to “spot business trends, determine quality of research, prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions.”
One of my colleagues, Pete Stiglich, blogs regularly on the topic of Big Data in Healthcare. You can view an on-demand webinar he hosted entitled “Using Big Data for Improved Healthcare Operations and Analytics” or read some posts written by Pete and other colleagues on the topic of Big Data in healthcare:
- Big Data – Where can it be used in Healthcare?
- Big Data – Data Management Challenges
- “Schema-less” Big Data
- Helping Data Scientists Navigate Big Data with the Semantic Web
- From Little Data to BIG Data – One Step at a Time
- Big Data to attack Healthcare Fraud
At the end of the day, we all want to know if the thing we call Big Data will ever be useful on the patient level. Fortunately, the very next session was entitled “Why Accountable Care Matters” where our speaker Elliot Fisher, MD took us through findings that were essentially gathered from Big Data in terms of population health management. In my humble opinion, Big Data becomes the biggest star when we realize that Accountable Care can be defined, from a technological perspective, as Health Information Exchange meets Analytics. Now that’s Big Data!
Perhaps what our panel was trying to communicate was Big Data is “when the size of the data itself becomes part of the problem”. This was a definition of Big Data eloquently penned by Mike Loukides of O’Reilly Radar. In Healthcare we have data sources that are large (or shall I say big?). The data comes from an incomprehensible (big?) number of sources, and these data sets are not at all fully interoperable. This cluster of Big Data becomes part of the problem when we are trying to keep populations, and individuals, healthy.
Now let’s get to work!
