Challenges for Data Quality in the Clinical Data Life Cycle: Systematic Review

Area of Science:

• Health Informatics
• Data Management
• Clinical Research

Background:

• Electronic health record (EHR) data are crucial for health policy and medical technology advancement.
• The shift towards data-centric clinical research emphasizes the importance of healthcare data utilization.

Purpose of the Study:

• To review literature on clinical data quality management.
• To define a process for ensuring data quality, particularly for secondary data utilization.

Main Methods:

• Systematic review of PubMed articles (2010-2023).
• Screening of 82,346 articles, selecting 105 relevant papers on data quality management.
• Focus on life cycles, assessment methods, and tools for clinical data quality.

Main Results:

• A 4-stage clinical data quality management process (planning, construction, operation, utilization) was identified.
• Key data quality dimensions include completeness, plausibility, concordance, security, currency, and interoperability.
• 105 papers were reviewed detailing these management processes.

Conclusions:

• Standardized quality control and automation are essential for the secondary use of EHR data.
• A proposed process aims to standardize data quality management.
• Development of a data quality assessment system is recommended.