Reason # 1 – You don’t have a data champion.
As with all endeavors, success rises and falls with leadership’s ability to define and execute on a strategy. Effectively leveraging data to improve outcomes is no different than any other mission critical business strategy. In some cases, the subject area of data harnessing is more difficult to define because it’s a new and emerging competency. Also, there is an abundance of solution marketing hype and noise to sort through matched only by a scarcity of actual successes. Someone in leadership must have a deep appreciation of data as a strategic asset and its potential value to improving outcomes. That person or person(s) needs to be assigned as the data champion. With fee-for-value compensation models linking care to fees, liberating and harnessing data should be a strategic priority directed toward specific outcome goals.
Reason # 2 – Your data is trapped and needs to be liberated.
For many of the modern EMR applications, patient data is stored in a relational database, such as SQL Server or Progress. The data is organized into a set of complex tables structures not easily understood to the lay person. As you can see from the table diagram below, an understanding of the relationships between the various tables is required to make sense out of the structures.
Although EMR applications have some internal reporting and analytical functions, they are not designed to support fast ad-hoc manipulation of large data sets to answer questions on-demand. EMR applications are designed to capture transnational data about a patient’s visit, medical history, diagnosis, and treatments. The screen shot below illustrates this purpose by the user interface design:
The data needs to be made available in an easy to understand format that non-technical people can understand. Data from the EMR application should be extracted and organized into a separate analytical platform. A separate platform offers flexibility to combine multiple data sources and create custom algorithms to enhance the value and usability of the data. The data should be easy to query using data discovery or visualization tools such as Tableau.
Reason # 3 – You don’t have people who know how to harness data.
A typical healthcare organization has technical resources, either provided by the vendor or internally, who understand the data structures of the EMR application and serve as report writers.
However, for the clinician who wants the flexibility to ask questions of the data on-demand, or is looking for insights from the data, this model impedes success. Employing a data scientist with a clinical or business background can elevate an organization’s ability to harness data. The data scientist is fast becoming one of the most valued skill sets in the clinical and business world. Data scientists are always at the top of the list of fastest growing careers, and this trend should continue as data collection continues to grow. A good data scientist is someone who understands how to transform raw data into valuable information to support specific needs.