Challenges of Data Quality in Clinical Data Life Cycle: A Systematic Review (Preprint)

Abstract

BACKGROUND It is anticipated that electronic health record (EHR) data will inform the development of health policy systems across countries and furnish valuable insights for the advancement of health and medical technology. As the current paradigm of clinical research is shifting toward data-centricity, the utilization of healthcare data is becoming increasingly emphasized. OBJECTIVE We aimed to review the literature on clinical data quality management and define a process for ensuring the quality management of clinical data, especially in the secondary utilization of data. METHODS A systematic review of PubMed articles from 2010 to October 2023 was conducted to assess the quality of electronic health record (EHR) and clinical data. Articles that defined quality management procedures based on the life cycle of clinical data quality management and discussed quality management assessment methods and tools were selected. The articles were categorized into four themes. RESULTS We reviewed 105 papers describing the clinical data quality management process. This process is based on a four-stage life cycle: planning, construction, operation, and utilization. The most frequently used dimensions were completeness, plausibility, concordance, security, currency, and interoperability. CONCLUSIONS Given the importance of the secondary use of EHR data, standardized quality control methods and automation are necessary. This study proposes a process to standardize data quality management and develop a data quality assessment system.