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.
Data reuse and continuous use
According to Harper, an important aspect of this envisioned Analytics Center of Excellence is the ability to reuse data for more than one purpose sometimes called secondary use. Unfortunately, that capability will not be enough to support cognitive analytics and augmented reality. Those both require continuous use of data.
- “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, Haper writes: ‘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 the concepts of continues use and continuous data. Continuous data is information that can be measured on a continuum or scale. It can have almost any numeric value and can be meaningfully subdivided into finer and finer increments, depending upon the precision of the measurement system.
Role of government
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?
Encouraging reuse and continuous use
In conclusion: in order to realize big data benefits in healthcare, the reuse and continuous use of data should be encouraged for scientific enquiry and debate, promoting innovation and potential new data uses, Harper states. This way the cost of duplicating data collection can be reduced, while increasing the impact and visibility of research.