Business intelligence and clinical analytics (especially pred
Business intelligence and clinical analytics (especially predictive analytics) are hot topics in hospital and clinic executive suites these days. And yes, I know these are also topics that cause eyes to glaze over for many of us who work in healthcare, especially clinical staff. We all remember the days when we lined up in front of the data analyst’s office and made our bid for a report. We also remember that by the time we got that report, most of it had become old news, irrelevant or not to be trusted. But that is certainly not the case for organizations using today’s more flexible, fluid and intuitive technologies.
My colleague, Tom Lawry, who serves as worldwide director for our health team at Microsoft and specializes in business intelligence and analytics for our industry group, has published a post for the Microsoft in Health Blog. I liked it so much that I wanted to share some of it here on HealthBlog. In particular, Tom illustrates the many opportunities today’s newest technologies provide for getting 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, he identifies four capability areas that can help health organizations gain greater insight to their business operations and care quality.
- Self-service tools. Most health organization leaders 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.
Like any really good professor, Tom makes it easy to understand the value proposition for contemporary business intelligence and analytics solutions in health and healthcare.