Policy And IT Challenges To Achieving Big Data Outcomes, Part 2

In part one of this series we provided a loose definition of Big Data, described some of the ways that Big Data tools can be used in health, and identified the high degree of alignment of Big Data capabilities with quality and efficiency analytics as well as observational health research. Big Data tools also show great promise in managing the copious amounts of health data emanating from patients via social networking and home monitoring, as well as many areas that have a genomic data component. We also pointed out the irony that while quality and efficiency uses can frequently fall under HIPAA “treatment, payment, and operations,” patient identifiable data for research by virtue of being “designed to develop or contribute to generalizable knowledge,” must address much more strenuous constraints.

Some Big Data analytics and observational research can also be done on HIPAA de-identified data. But the traditional issues with de-identified data will be particular obstacles for other Big Data outcomes. Big Data tools and data sets, for example, will increasingly bring re-identification of HIPAA de-identified data to the fore.