Generating scientific models of knowledge using arcs

Implementing common data elements across studies to advance research

Challenges arise in building the knowledge needed for evidence-based practice partially because obtaining clinical research data is expensive and complicated, and many studies have small sample sizes. Combining data from several studies may have the advantage of increasing the impact of the findings or expanding the population to which findings may be generalized. The use of common data elements will allow this combining and, in turn, create big data, which is an important approach that may accelerate knowledge development. This article discusses the philosophy of using common data elements across research studies and illustrates their use by the processes in a developmental center grant funded by the National Institutes of Health. The researchers identified a set of data elements and used them across several pilot studies. Issues that need to be considered in the adoption and implementation of common data elements across pilot studies include theoretical framework, purpose of the common measures, respondent burden, teamwork, managing large data sets, grant writing, and unintended consequences. We describe these challenges and solutions that can be implemented to manage them.