How to do big data in healthcare
Many healthcare organisations like to talk about data analytics. However, here are eight pieces of expert advice to help you actually do it.
By Brian Eastwood | CIO US | Published: 11:34, 23 May 2014
Healthcare is replete with big data analytics use cases that offer measurable results, including reduced hospital readmissions, better medication management, improved strategic planning and heightened fraud detection.
That's all well and good, but for one key factor: How do you start? Most healthcare data remains unstructured, proprietary and siloed — and creating a clinical data warehouse is a complex task that time-crunched healthcare CIOs can't always justify.
Fortunately, there are lessons to be learned from healthcare's big data implementations. Here's some advice from providers who have been there, done that and lived to tell the tale.
Data in the hand for life's rich demand
Speaking at the recent Oracle Industry Connect, the Mayo Clinic's James Buntrock recalled a time when organisations simply added applications, databases and point-to-point interfaces between them. The result? A plethora of disconnected data warehouses. Rather than focus on apps, Mayo takes a data-centric approach, Buntrock says, treating data as a critical asset for research, clinical and other needs. Getting this right means aligning IT and business objectives, he says, as well as supporting informatics and data mining in addition to traditional business intelligence.