Data quality - The foundation of real-world studies

Area of Science:

• Health Services Research
• Data Science
• Clinical Research Methodology

Background:

• Real-world data (RWD) offers valuable insights for real-world studies (RWS).
• RWD is collected routinely from diverse sources beyond traditional clinical trials.
• Challenges exist in managing the quality of RWD.

Purpose of the Study:

• To review critical quality aspects of data essential for RWS.
• To highlight the importance of data quality in real-world evidence generation.
• To guide researchers in addressing data quality challenges in RWS.

Main Methods:

• Literature review focused on data quality in real-world studies.
• Analysis of common data quality issues in routinely collected data.
• Discussion of strategies for ensuring data quality for RWS.

Main Results:

• Sub-optimal and inconsistent data quality is a significant challenge in RWS.
• Proactive planning and consistent management are necessary to ensure data quality.
• Specific quality aspects relevant to RWS are identified.

Conclusions:

• Ensuring high-quality data is paramount for the validity and reliability of real-world studies.
• Addressing data quality proactively is essential for generating robust real-world evidence.
• This review provides a foundation for understanding and managing data quality in RWS.