Tamara StClaire, COO, BaseHealth
I recently read a Becker’s Hospital Review article on four common roadblocks organizations face when adopting predictive analytics in healthcare. Emily Rappleye, the reporter who penned the article, highlighted insights from Andy Bartley, senior solutions architect at Intel Corp., who discussed these roadblocks at the Becker’s Hospital Review 3rd Annual Health IT + Revenue Cycle Conference in Chicago.
Andy identified the following common areas that healthcare organizations should focus on to make the jump into predictive analytics:
1. Scalable infrastructure
2. Executive sponsorship
3. Change management
4. Use case selection
This spurred me to reflect on how my own company is addressing and tackling these obstacles. At BaseHealth, we take a different approach to population health — we use predictive analytics to make patient and population data immediately actionable, enabling physicians to deliver better care, reduce healthcare costs and identify what we call “Invisible Patients” (people who look healthy on paper until they hit a tipping point, where they face one adverse health event after another, but with the right medical intervention at the right time, we can change their healthcare trajectory).
Along the way, we’ve encountered the roadblocks Andy outlined above, but we’ve been able to successfully move past them and achieve desirable results. Looking for proof? Check out my byline in Becker’s that summarizes the specific ways BaseHealth is hurdling each of these four barriers.