Most of us at one point in our elementary school existence, were introduced to the organizational glory of the Dewey Decimal System. To be real, at the time we probably didn’t think it was all that glorious. It was just another monotonous thing we had to learn to pass fifth grade English.
But little did our fifth grade selves know, this proprietary library classification system, invented by Melvil Dewey in 1876, held the key to managing and tracking the most coveted literature in massive libraries across the country. Mr. Dewey was an organizational genius. He was also a man of foresight.
Libraries across the world are still using the Dewey Decimal System today. And with the help of modern computing, the system’s become more efficient than ever at classifying and managing titles. So, if Dewey could figure this out back in the 1800s, we should surely be able to catalog simple reports and dashboards leveraging the fancy new healthcare technology we have today, right?
Sorta. There is nothing simple about reports, dashboards, or data in healthcare, nor is the classification of healthcare solutions simple. However, it is possible. We’re even seeing EHR vendors step up and help in this capacity, along with the technology companies that cut their teeth in non-healthcare industries, such as banking and retail. Like many decisions in healthcare, each vendor presents compelling advantages and considerations (costs being a big one!). Taking the time to build a strategy to understand where your valuable resources spend their time is critical to a successful Data Governance program.
As Quint Studer states it in his book Hardwiring Excellence, “what gets measured gets done.” This couldn’t be more accurate. Nor could it be a better justification for Data Governance. Data Governance principles and processes are a key driver to help identify what should be measured and how it should be measured.
From my experience, there are five actionable KPIs most executives are focusing on at any given time to drive results (i.e., current census, claim denial rates, medication reconciliation, total visits, etc.). Surfacing those KPIs throughout the organization, or department for measures at a departmental level, creates the transparency required to drive improvement. One key area your Data Governance team can focus on, is ensuring the transactional data clinicians use daily is derived from the same source the executive team is looking at, and vise versa.
It sounds obvious, but we’ve all seen scenarios where the team executing on an actionable list is derived from a data source separate from the executive team’s dashboard. How can that possibly happen? One likely cause is data transformation. Sometimes, the data team trying to ‘organize’ data efficiently for better executive dashboard performance, inadvertently aggregates the data in a way that jeopardizes its integrity (i.e., through organization or integration activity). It’s not the analytics developers’ fault (usually), but instead a lack of oversight (aka Data Governance) at an enterprise level. Identifying those core KPIs is the first step, but validating the data sources and movement is an equally important step.
I was encouraged to see a recent Gartner article highlight the value of Data Governance for remote work. As many of us know, this Data Governance problem isn’t new to healthcare professionals. But now more than ever, it’s become a problem we have to address. Validating those ‘gold standard’ data sources and/or data pipelines to enable analytics and self-serving activities will allow a fluidity that fuels creativity and innovation. When a blended project team of technical and clinical resources can ask continuous questions of data, an innovative culture can be formed.
We’ve seen this within organizations that are operating on the edge of AI. Moving data to new Cloud technologies to leverage the limitless compute is a fantastic use of technology. But, if the data isn’t validated, trusted, and regularly managed, project teams are set up for sure failure.
It’s not by chance that the Dewey Decimal System has been revised through 23 major editions since 1876. Similarly, Data Governance is more than a means to an end. It’s a continuous effort your organization needs to collaborate on across your strategic departments. Having witnessed healthy debate over what a “new patient” really is among executive leadership spanning Finance, Quality, Clinical, Operational, and IT departments, I can assure you Data Governance takes a village. I can also confidently tell you that everyone has the right definition in mind, or at least they’re right from the perspective of the department they’re representing.
The beauty of it all is that it doesn’t matter who is right or wrong. It only matters that the collective group is consistent. The only way to achieve that consistency is to identify misalignment, address it to land on an agreeable conclusion, and document it so the next time it comes up your organization knows where to look and how to interpret the given measure or dataset.
If you’re looking for someone to give you a hand with your classification efforts, or your first iteration of the Dewey Decimal System, give us a call.
