Health Care Business Intelligence: Where to Start

One of the greatest challenges in health care right now is that the data environment built over many decades does not support the kind of analytics required to drive accountability.  Accountability for delivering value (the right care at the right price) at the provider level requires a data environment that delivers "the story" quickly and efficiently to senior executives, the Board and to providers on the ground.

The challenge is daunting…even in public companies, where accountability to shareholders is paramount, challenges in data management are enormous.  Just recently I read an article from McKinsey’s "Global Survey".  It cited the fact that while "more than 75% of respondents to the survey report that their organization’s greatest benefits from data use flow from clear and timely reporting of financial and performance metrics", 3 of the top 4 "barriers to increasing the use of data and advanced analytics are

  • Lack of skills in translating and synthesizing data for use by decision makers
  • Lack of sufficient data"

In major hospitals and provider systems, the challenge is compounded by the fact that data has historically reside in "point solution" software…3rd party applications that support a specific function extremely well.  Examples are ERPs, operating room software, EHRs and registration systems, to name just a few.

The Situation for Finance

The impact of these disparate software packages is that finance is left chasing and cleaning data, reconciling spreadsheets, rather than analyzing the data.  Because they haven’t needed to, financial departments have not built a data infrastructure that will support the delivery of insight to the leadership and to the clinicians. 

Now, however, CFOs have realized that they must build the right analytical infrastructure to acquire adequate data from the disparate systems and improve data quality, all while trying to transform their finance functions into analysts versus "data dogs".  This is a very large undertaking, one that requires both the vision and the patience to achieve the vision.

Tackling the Problem: Think Like a City Planner

I would compare where to start with what has happened here in Boston in the Seaport area.  City planners, the mayor and some very forward thinking business folks realized that, if they were going to rehabilitate the water front on the South side of the canal into a vibrant part of the city, transportation to the area would be prerequisite!  Once the commitment for "T" stops and bus routes were in place, office towers, one by one, and restaurants, one by one, and hotels, one by one, started to be planned and built, all providing great value as each opened.

Building a data environment is very similar.  Create your vision!  Think big! Have a business intelligence architect create the entire map of your city!  Then build your "T"…meaning, put in place the technical infrastructure required to manage large data volumes, put the right "meta data management" tools in place for access to that data and become expert in how to build and deliver dashboards to the end users.

Become a Hero to Your Business Partners

Once you’ve built your infrastructure, then you can begin to solve specific information challenges:  a labor productivity dashboard, a volume dashboard, an occupancy dashboard.  Once the infrastructure is in place, there will be so much demand, you have to think about that city plan… start small, build your first building (your first subject area), complete the entire project.  Source the right data, make the data accessible on a self-service basis to the business users and create meaningful dashboards and reports useful to managing performance.

Thinking big can feel futile.  It can feel risky – what if I’m wrong?  It doesn’t have to be risky!  But building the proper infrastructure for true business intelligence is work that has to be done.  Your organization is depending on your getting this right.


McKinsey Company, McKinsey Global Survey results @ www.mckinseyquarterly.com, Dec 2011.

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