The reliability of patient information in electronic systems
Keywords:
coding systems, data quality, data science, electronic health records, machine learning, reviewAbstract
Electronic health records (EHRs) are digitized data used for clinical research and data science. Data quality in EHRs is crucial for accurate results, including accuracy, completeness, consistency, credibility, timeliness, accessibility, adequacy, comprehensibility, and interpretability. However, the quality of digital data in EHRs is a major concern, with issues such as incompleteness, duplication, poor organization, fragmentation, and insufficient use of coded data. To improve data accuracy, researchers can use statistical measures and validate data using methods like central tendency, dispersion, and goodness-of-fit tests. Missing data in EHRs can be classified into three forms: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). Challenges in EHR data include documentation without explicit units of measurement, discrepancies in data collection across organizations, and unstructured text data. To ensure high-quality data, researchers should familiarize themselves with the EHR platform, secondary data sources, and data collection methodologies. Regulations at national and international levels are established to ensure the optimal handling, safeguarding, and usage of personal data, including healthcare information. This study provides an overview of the key elements of high-quality electronic health record (EHR) systems, as well as common practices in their deployment.
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