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You’ve sold the vision. By becoming a self-service, data-driven healthcare system, you will improve the bottom line and make your hospital system more competitive. You’ll improve the quality of care for every patient, and allow the organization to blow the doors off your patient satisfaction scores by tailoring services to predict the patient’s ideal meal selection along with the best possible care outcome. You’ve purchased and deployed the latest data warehousing appliances and data management suite. You’ve spent months integrating data from the far corners of your enterprise. You’ve selected the sexiest self-service business intelligence tools on the market. You’ve trained thousands of users, stood up centers of excellence, presented your pilot program results to physician leadership and executive management. Your healthcare analytics program is live and medical miracles are waiting to reveal themselves out of your data. Or maybe not.
The healthcare analytics market for 2016 is expected to top $9.96 billion USD with a +26% compounded annual growth rate (CAGR) over the next 5 years, reaching almost $32 billion annually in 2021. Sixty-one percent of that investment, or more than $6 billion this year, is coming from the North America healthcare. In comparison, the juggernaut US EMR/EHR market was only a third larger at $9.3 billion in 2015 with a far more modest 7.1% annual growth rate.
Yet, despite the massive investment and some very promising anecdotes, wide spread use and adoption of analytics, especially self-service BI, feels like it is falling short of the vision and potential value. While quantifying the value of analytics efforts is notoriously challenging, only a few of the healthcare organizations I’ve spoken with will tell you they are getting everything they need and expect out of their analytics platforms and investments.
Let’s look at some of the remaining challenges standing in the way of fully realizing the value of an analytics investment and see if there might be some common root causes that we can address.
While the above list of challenges facing healthcare data governance programs is not exhaustive, these common analytics adoption challenges identify a pattern of poor data governance as a significant contributing issue. The root causes include:
To put it plainly, in order to improve the value from analytics investments, we need to focus on data governance. The diagram below represents the concept visual. I encourage you to take an honest look at the value the organization is realizing from your healthcare analytics program today and determine how large the opportunity is to improve the value.
The first step in any self-improvement effort is understanding that improvement is needed. In future posts, I’ll share how and where to focus your data governance efforts to increase the value from your analytics initiatives.
Chris helps healthcare organizations get more out of their data-driven initiatives by focusing on data governance as a tool for engaging the business and clinical organizations.
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