Tue, 06/12/2018

In my introductory blog, I spoke of the importance of getting the foundational aspects of data correct before moving into more technologically advanced areas.  Getting our “data house in order” is the first step to avoiding a lot of rework, which reminds me of a cautionary cartoon a co-worker had pinned in his cube when I first started as a software developer; “Don’t worry about the mule going blind, just load the cart.” 

It is important to think of data as the valued assets that they are, and when it comes to treating data as an asset, we need a new, modernized way of thinking, especially in healthcare.  If data are inaccurate in retail, finance and manufacturing, it can cause logistical issues or cost money.  If they are wrong in healthcare, people get hurt, clinical outcomes suffer and population health declines.  However, if Integration and Integrity (quality) are done correctly, there is a massive increase in speed to value for achieving Intelligence/Analytics with a significant reduction in long-term FTE overhead.  Thereafter, all staff and leadership involved in the effort to solve Data Rich and Information Poor are able to operate at the top of their license, education and experience. 

So, where do we look for this modern approach to data strategy?  Some EHRs claim they can bring all these data together in a way that is matched, merged, clean and consistent.  I get it, many have invested heavily in EHRs and would like to believe an analytic ROI is there.  Granted, it is a key source of transactional and clinical data.  One system I was with even called the EHR a “clinical centerpiece”.  However, of the 3 Integrated Delivery Networks I’ve been a part of in the last 20 years, all of them have had over 300 transactional systems that were sources of data, at least half of which are of key importance regarding integration and integrity to achieve intelligence and analytics.

Further, if you are a believer in Gartner and other industry analysts, none of them are evaluating any of the EHR vendors on these categories:

  • Data Integration
  • Data Profiling/Quality
  • Address Standardization
  • Geocoding
  • Master Data Management
  • Data Stewardship
  • Business Intelligence and Analytics

As my colleague Bill Kotraba’s article pointed out at the start of the year, these are not core competencies of EHR vendors.  So how do we solve this foundational data dilemma of successful integration and integrity to achieve intelligence?  Problem solving 101 dictates that first we make sure we have defined the problem well, and to that end, my blog post next week will offer 3 “real life illustrations that define healthcare’s unique data challenges” for your consideration.