The use of analytics in healthcare is evolving, as external forces place continual change on organizations to discover ways to improve how they operate and optimize patient outcomes.

While many healthcare organizations are still at a basic level in using analytics, more are seeing the need to improve capabilities, and quickly, said Curtis Smith, senior director of commercial innovation at Janssen Pharmaceuticals, during this week’s SAS Health Analytics Executive Forum in Cary, N.C.

Also See: Healthcare Organizations Not Using Data, Analytics to Full Potential

Organizations see that they need to move beyond using analytics to describe what’s happened in the past, so that they can use data to predict what is likely to occur in the future, and then take pre-emptive steps, said Joseph Colorafi, M.D., vice president and chief medical information officer at Dignity Health, a San Francisco-based delivery system.

“Instead of looking back in the rear view mirror, we need to be looking through the front windshield and be able to prevent something from happening,” he said. “When building a predictive analytics approach, we’re trying to be proactive and preventive; we have to be able to track and trend, and make analytics sustainable.”

The importance of analytics has risen, as quality initiatives and fee-for-value approaches become key business drivers, said Jason Cooper, vice president and chief analytics officer at Horizon Blue Cross Blue Shield of New Jersey.

“Analytics are maturing; in the past, it’s been used very tactically, and typically within silos in an organization,” he added. “In most of our organizations, analytics has been thought of as a cost center. I like to think of it as a value center, and to do that, it’s important to focus on targets and KPIs, so you can monitor and track the value of your analytics efforts. Data is just data; we have to transform it to information, and then transform information to insights.”

Increasing technological capabilities, such as parallel processing, faster servers and the ability to run a query “in memory” has reduced the time to perform complex analytics, Colorafi said. What used to take a year and a half can be examined in a matter of days, he said, giving organizations “the ability to run iterations of a model very quickly.”

“The change in technology enables us to increase the speed for delivering results,” Smith said. “We want to deliver analytic results on a real-time basis, and that wasn’t possible before.”

The rise in importance of analytics also has raised the stakes for organizations to do more than just analyze data – results are important, and analytics initiatives must do more to ensure that change actually occurs.

“Great analytics that are unimplemented does no one any good,” Smith said. “It’s a consultative art; how do you get your ‘client’ to implement what you’re suggesting? You only get credit for what you’ve implemented.” Organizations see more value in including visualization technology and storytelling to more effectively turn analytic results into operational changes, Cooper added.

Register or login for access to this item and much more

All Health Data Management content is archived after seven days.

Community members receive:
  • All recent and archived articles
  • Conference offers and updates
  • A full menu of enewsletter options
  • Web seminars, white papers, ebooks

Don't have an account? Register for Free Unlimited Access