Implementing common data elements across studies to advance research.

Area of Science:

• Health Services Research
• Clinical Informatics
• Biomedical Big Data

Background:

• Clinical research data acquisition is costly and complex, often resulting in small sample sizes.
• Limited data hinders the development of evidence-based practice.
• Combining data from multiple studies can increase impact and generalizability.

Purpose of the Study:

• To discuss the philosophy and implementation of using common data elements (CDEs) across research studies.
• To illustrate CDE application in pilot studies funded by the National Institutes of Health (NIH).
• To identify challenges and solutions for adopting CDEs in multi-study research.

Main Methods:

• Researchers identified a core set of CDEs for use across several pilot studies.
• The study examined the processes and outcomes of implementing these CDEs.
• Considerations for adoption and implementation were analyzed, including theoretical frameworks and data management.

Main Results:

• The use of CDEs facilitates data aggregation, creating valuable big data resources.
• Implementation requires careful consideration of factors such as theoretical framework, respondent burden, and data management.
• Successful adoption of CDEs can accelerate knowledge development in clinical research.

Conclusions:

• Common data elements are crucial for enabling data sharing and the creation of big data in research.
• Addressing challenges related to CDE implementation is essential for maximizing their benefits.
• This approach supports the advancement of evidence-based practice through more robust and generalizable research findings.