The Other F-Words: Fear, Frustration, and Fascination with Big Health Data

admin Our Thots

by Monica Horvath, PhD

It seems lately that ‘analytics’ and ‘big data’ are inescapable terms in most technology sectors.  Even the most banal items in our lives are achieving an artificial intelligence of sorts to help us with daily decisions.   But healthcare organizations have only recently started to adopt data-driven lifestyles.  Harnessing ‘big data’ power will touch every participant in the patient care ecosystem.  As a result, fear, frustration, and fascinationare some of the other ‘F-words’ that may arise in the journey to make meaningful use of analytics insights.


Fear can be a powerful yet uncomfortable motivator.  Remaining competitive while the incentive structure for reimbursement evolves feels a bit like ‘building a plane while flying it.’  So turning to comprehensive business intelligence is one approach to manage this fear.   Just the prospect alone of losing some Medicare/Medicaid reimbursement funds has driven most care organizations to adopt electronic health records.  We don’t just have defensive medicine, but we also have defensive technology.


The pervasive lack of interoperability between distinct health IT products—or any sense of semantic interface—is a major stumbling point for those who want to create and mine a multifaceted dataset.    Clinical workflows in EHRs are nuanced, business dictionaries follow an ‘oral tradition’ knowledge management model, and standards lack majority adoption.  From this grows a culture where certain business analysts can cling to the job security in being the only person who knows how to get data from ‘super critical yet obscure database XYZ’.  For analytics professionals that just want to jump into the data and ask questions, this is maddening.  How many shoulders should you have to tap, email, or otherwise prod to get confirmation as to the accepted date-time stamp for a hospital admission at your organization?


This is the fun part.  Fascination, for me, comes from taking a large dataset that both begs questions of a business area as well as conceals answers.  Our job is to tease out and pair the Q&A’s, and if you are lucky, you may just find that you are the first person at your organization to have a solid hypothesis as to *why* a business outcome is observed.     With this in mind, a ‘data driven’ culture would know how to react to these ideas even if the data tells an unexpected story.


Does your organization have a fair weather approach to data?


Fear, frustration, and fascination are natural responses that come into play when data challenges a culture’s status quo.  For example, the Choosing Wisely initiative has put forward a series of recommendations that outline the appropriate use of care services in the face of over-utilization and “defensive” medicine.  With electronic, enterprise data, it is fairly straightforward to assess adherence to such recommendations and estimate achievable cost saving efforts to adjust provider practices.

As an analytics practitioner, I am fascinated with the opportunity to contrast individual practice habits both within and across specialties as a means to aid care redesign.   But the affected providers may justifiably feel frustration if such analysis results are presented to them (gotcha!) almost as an audit without their consultation.   There may be fear that the analysis may risk creating policy that could place barriers in front of diagnostic testing thus harming their patients.  And the business managers of a clinical service line may have concerns that reducing utilization will lessen income as well—at least in the outpatient setting.

The ‘F-word’ that best characterizes executive reaction to data-driven insights is an indicator of an organization’s overall readiness for analytics—the mindset with which analysis is integrated into a continuous improvement approach to patient care.   The desire to help organizations navigate fear, frustration, and fascination is exactly why I chose this new journey with ThotWave.  ThotWave’s health analytics maturity model is built of five competency vectors, each having three core components on the path to maturity:  mindset, skillset, and toolset.  I will get into that topic in a future post.

Now, as for the ‘original’ F-word, you should know that one very well.  It’s my favorite word in the whole wide world.


You were thinking of something else?