June 25, 2014
Building confidence in the big data and analytic initiatives requires good governance. Data from a variety of sources, and in different formats, heightens the need for strong, proactive governance. Health information management professions area ideal candidates for this critical need.
May 7, 2014
Patient identification and patient matching have been a decades-long challenge for the healthcare industry. Hear some of the recent industry activities addressing these challenges, as well as the UPMC case study.
March 28, 2014
There are many paths to actively engaging consumers in their health and wellness, and the ONC Patient Matching Final report provided yet another avenue.
March 11, 2014
Patient matching requires a solution that includes people, process and technology. There is no silver bullet such as a national patient identifier.
February 20, 2014
HIMSS14 attendees are in for a treat this year; in addition to the amazing educational sessions and exhibits, visitors will have the opportunity to an exciting story about real-time analytics for research and data quality.
October 28, 2013
Big data means more data – from more sources, in more formats. It also involves data that is created at a more rapid pace. All of those factors make it harder to establish context. Where did this data come from? How much do you trust it? What steps were taken to correct, or massage, the data?
October 7, 2013
Protecting and security sensitive big data is necessary to ensure data is shared for new forms of analysis.
Living the Big Data Dream: Confidence, Confidentiality and Continuous Automation in the 21st Century
September 26, 2013
Big data is about much more than scaling, accelerating and broadening your analytic applications.
September 25, 2013
At a recent IBM event focused on building confidence in big data, a highlight was a wide-ranging discussion by a panel with very diverse background
September 24, 2013
Confidence in big data is highly variable. Some data sources have inherent uncertainty. So why shouldn’t you spend as much time as needed to make big data perfect? Time. You simply don’t have enough time to sort out every data irregularity, every ambiguity, every incomplete attribute.