Continuous Use of Data

In today’s environment of “data to action” there is increased focus on building an Analytics Center of Excellence. While the concept of analytics has been around for a long time, it is now a strategic focus given advances in healthcare digitization. Capturing, storing, and acting on this exponentially expanding amount of clinical knowledge can spur growth in artificial intelligence, machine learning, and a learning health system.

An important aspect of an Analytics Center of Excellence is the ability to reuse data for more than one purpose sometimes called secondary use1.  Unfortunately, that capability will not be enough to support cognitive analytics and augmented reality.  Those both require continuous use of data.  

To clarify the terms:

  • “Data reuse” involves deploying a data asset and using it more than once for the same purpose.
  • “Continuous use” involves deploying a data asset previously used for one (or more) specific purpose(s) and using that data set for a completely different purpose.

For example, if we have an application that uses a lab value to generate a clinical decision support recommendation to adjust a medication dosage and then later in the day uses the same lab value to predict a readmission, that would be defined as “reuse.” On the other hand, taking the same lab value combined with other clinical data to calculate the acuity of the patient and need for nurse staffing would be an example of repurposing that data, i.e. continuous use.  It is important not to confuse continued use and continuous data. Continuous data is information that can be measured on a continuum or scale. Continuous data can have almost any numeric value and can be meaningfully subdivided into finer and finer increments, depending upon the precision of the measurement system.

The governance aspects for multiple instances of reuse and continuous use mandate assessing data quality requirements. Are all the quality expectations going to be identical? Alternatively, when a data set is repurposed, whose responsibility is it to document data quality rules and acceptability thresholds as well as integrate validation of the data into upstream processes? More importantly, what does one do if the repurposing of the data is very far from origination? 

In order to realize big data benefits, the reuse and continuous use of data should be encouraged for scientific enquiry and debate, promoting innovation and potential new data uses, thereby reducing the cost of duplicating data collection and increasing the impact and visibility of research. Good data management is the key to an Analytics Center of Excellence.


  1. Harper, E.M. (2013). The economic value of healthcare data. Nursing Administration Quarterly, 37(2), 1-4

About the author: With more than 30 years of experience in healthcare, Ellen can leverage her professional nursing practice knowledge and health system operational experience to develop strategic initiatives and redesign of clinical business processes that result in improved quality, patient safety and reduced costs