On Wed 8/29/20012 at 8 PM ET / 5 PM PT please join us for a Tweetchat hosted by SPM member Janice McCallum @janicemccallum about Big Data in Healthcare. One good way to participate is to use the website

http://tweetchat.com/room/s4pm

Go to the site at the right time, login with your Twitter user name and enjoy the discussion!

If you want to find out more about Big Data, Janice prepared a few links that will get you started. Below is the outline of the chat:

We’ll kick-off with a definition of Big Data. Definitions vary, but the “3V” definition seems to be most widely used: collections of data with high Volume,Variety of formats, and Velocity. Formats can include structured and non-structured data types.

For background, see

• Jeff Jonas’s explanation of big data on IBM’s Smarter Planet.     
• Richard Dale’s A Better Definition of Big Data.  

Be sure to read Doug Laney’s comment on Richard’s post & click through to his original post defining 3V – in 2001!

For this #S4PM Tweetchat, we’re focusing on Big Data in healthcare. Here are 4 topics for discussion with a couple of examples from me to kick-off discussion if needed.  We can skip T4 if we don’t have time.

T1:       What are some current examples of Big Data in context of healthcare?

• Genomics
• Google Flu Trends

T2:        What can Big Data do to help transform healthcare?

• Improve access to research information for Clinical purposes (á la IBM Watson)
• Resolve textual/dictated info problem by improving integration of structured & textual data from clinical settings.

 T3:        Is Healthcare Ready for Big Data?

• We haven’t exactly mastered exchange and analysis of structured data yet.
• Are some segments more ready than others? E.g., payers.
• How close are we to benefiting from outcomes data generated by EHRs
• How about EHRs/PHRs that record patient-generated outcomes?

T4:        What is the biggest obstacle to implementing Big Data solutions in healthcare?

• Access to the data across organizations and across stakeholder groups.
• Privacy concerns.
• Methods for analyzing the data.