The U.S. ranks 46th among 48 countries listed in a 2013 Bloomberg ranking of nations with the most efficient healthcare systems. As a world leader, the U.S. has a long way to go to catch up with other industrialized nations.
For example:

  • There is a 28% EHR adoption rate
  • 15% of patients account for 80% of healthcare costs
  • Only 24% of primary care practitioners are notified of an emergency department admission

Today, healthcare reform, and more specifically the shift toward value-based reimbursements, has created a clear need for hospitals and health systems to focus on fixing the problems that persist in the healthcare system. Organizations are investing heavily in analytics and big data technologies to improve population health management initiatives by optimizing cost structures and coordinating care both internally and across broader healthcare communities.

Working with some of the nation’s leading provider, payer, and governmental organizations, we have seen too many cases where this investment has fallen far short of expectations. Most of these failed or underperforming population health management initiatives are doomed from their earliest stages for two critical reasons:

  • Healthcare organizations consistently fail to ensure that their initiative rests on a data foundation that is comprehensive, accurate, normalized, accessible and actionable for a range of constituencies.
  • These organizations fail to embrace proven best practices at each stage of the initiative, from initial data acquisition and architecture all the way to actually effecting change at the point of care and in the back office.

This is the first in a series of perspective papers addressing these common and costly pitfalls. In this paper we will focus primarily on building the foundation for and ensuring success of analytics and business intelligence (BI) for population health initiatives. Future papers will drill down further into best practices for driving adoption and care coordination.