Discovery of data quality issues in electronic health records: profound consequences for critical care medicine applications – a systematized review

Electronic health records (EHRs) in critical care settings generate vast amounts of data that increasingly drive machine learning (ML) models for clinical decision support, yet data quality issues may have profound consequences for downstream prediction, classification, and optimization applications. This study aims to systematically examine EHR data quality issues in critical care medicine and their impact on ML model performance, clinical outcomes, and patient safety. We conducted a systematized review following expert-based questions, searching MEDLINE, Embase, IEEE Xplore, ACM Digital Library, CINAHL, Google Scholar, DBLP, Web of Science, and the Cochrane Library. Six distinct questions addressed missing data patterns, temporal data quality, bias and health equity, multi-modal integration, real-time monitoring, and institutional variability. 281 relevant studies examining EHR data quality in critical care settings. After applying the eligibility criteria, 29 studies were selected. EHR data quality issues in critical care were pervasive and multifaceted. Missing data rates exceeded 80% for some variables, with 40% of predictive features being missingness indicators rather than actual values. EHR-related medication errors comprised 34% of all medication errors in ICUs, with one-third having life-threatening potential. Copy-paste prevalence reached 82% in residents’ progress notes. ML model performance degraded significantly under real-world conditions, with external validation showing AUC drops from 0.76 to 0.63 for sepsis detection models. Temporal data quality deteriorated throughout ICU stays, with vital sign quality degrading at 60–75% of average length of stay. Data quality issues in critical care EHRs create cascading effects that compromise ML model reliability, clinical decision-making, and patient safety. The evidence demonstrates an urgent need for systematic data quality monitoring, bias-aware assessment methods, and comprehensive quality improvement frameworks specifically designed for critical care environments.