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BACKGROUND: Data quality is the degree to which data are fit for their intended purpose and is described using quality dimensions. The increased use of medical data in clinical research and medical artificial intelligence development has rendered data quality assessment essential. Despite existing data quality definitions, frameworks, and tools, data quality assessment in real-world settings faces multiple challenges. This stems from a lack of understanding of how to assess real-world data quality and interpret the results. Therefore, practical approaches to data quality assessment are needed that are appropriate for diverse data environments, intended use, quality dimensions, and requirements. OBJECTIVE: This study proposes a practical approach for assessing the completeness of electronic health records for medical research. This approach integrates structural completeness, rule-based assessment, and descriptive analyses of completeness and data diversity to clarify how data quality can be measured and meaningfully interpreted in practice. METHODS: Completeness of a large-scale electronic health record (EHR) dataset from Gachon University Gil Medical Center was evaluated, covering January 2005 to December 2023. Completeness was assessed using a three-part approach comprising (1) structural completeness assessment, (2) rule-based assessment, and (3) descriptive analyses of completeness and data diversity. Assessments were conducted using clinical data quality assessment tools. This practical approach was used to assess EHR completeness for medical research from 1,798,153 patient records. RESULTS: In the structural assessment, 7 out of 39 tables were empty or unavailable, indicating limited capturing of clinician free-text data. The rule-based assessment identified substantial missingness in vocabulary fields (30.6%) and missing or special-characteristic values in relation to observation (23.8%), measurement (4.0%), care site (1.6%), and death (0.3%). Descriptive analyses demonstrated a balanced gender distribution (49.3% male and 50.7% female) and a predominantly Korean racial distribution (96.75%). Collectively, these findings illustrate the completeness quality of a multi-perspective completeness assessment for medical research. CONCLUSIONS: This study demonstrates how data quality dimensions can be measured in practice through a real-world completeness assessment. This practical approach enables evaluation of EHR completeness and provides insight into data quality. Its findings have implications for researchers conducting data quality assessments and applying quality dimensions in medical research.
Lim et al. (Tue,) studied this question.