A new paradigm for health analytics

20 August 2013 | Tom Lawry, Global Product Strategy, Health Analytics

Health organizations are increasingly turning to analytics to help them tackle the many challenges they face—from caring for more patients with limited resources, to complying with stricter regulations for reimbursement, to improving processes across the board. As they do so, they’re realizing the old paradigm of analytics just isn’t all that effective in today’s constantly changing health care environment. This old paradigm is characterized by complex tools that only specialists can use and intelligence that’s not as current as it needs to be, especially by the time it gets passed on to the staff who need it.

The good news is that there are many opportunities brought about by today’s technologies to get up-to-date—even predictive—actionable insight into the hands of the right people at the right time. In working with customers around the world, at Microsoft, we’ve identified four capability areas that can help health organizations enable a new paradigm for health analytics:

  • Self-service tools. Most health organization leaders I speak with consider their workforce to be knowledge workers. And today’s health knowledge workers don’t want to wait around for an analyst to send them the information they need for their work. They want to be able to quickly gather actionable analytics meaningful to their specific role using tools they already know how to use. Self-service capabilities empower everyone in the organization to gain knowledge that helps them do what they do even better, smarter, and more efficiently.
  • Real-time information. Information from the past can’t help head off cost or quality issues that are happening right now. Staff need access to real-time or near-real-time information so they can identify trends early and take action to address clinical, financial, or operational issues sooner rather than later. For example, with real-time information an infection-control nurse can catch an outbreak right away to prevent it from spreading.
  • Predictive analytics. Rather than providing health knowledge workers with a look in the rearview mirror, predictive intelligence helps them see the road ahead. The ability to easily analyze data in a way that predicts what might happen in the future provides powerful insight. For example, predictive analytics can help staff identify patients at risk for readmissions so they can be treated accordingly to help prevent and reduce readmissions. Or it can help administrators to forecast usage of services so resources can be more effectively allocated.
  • Data fluidity. The massive amount of data in any given health organization and the health care system in general are spread across a wide range of systems. So health organizations need a platform for analytics that can securely connect data across systems and devices within and beyond their organization—whether on-premises, in the cloud, or a combination of both. Capabilities like this can allow a physician to easily merge a cancer patient’s history and diagnosis with clinical data analysis from the cancer institute, for example, to help determine the most effective and efficient treatment options.

At Microsoft, we’re committed to offering health analytics solutions that provide the above four capability areas and help health knowledge workers improve care quality, maximize reimbursements, and lower costs. Over the coming months, I look forward to continuing to discuss how the new paradigm for health analytics can help health organizations tackle their toughest challenges.

Tom Lawry
Global Product Strategy, Health Analytics