This paper compares between the results obtained from two de-identified data systems i2b2, and Epic Slicedicer, and discuss the data quality dimensions' specific to the clinical research informatics context, and the possible data quality issues between the de-identified systems. In this paper, we describe a real-life case study on assessing and improving the data quality at one of healthcare organizations. The second category is related to poor ETL and data mapping processes. The first category is related to inconsistency among data resources such as format, syntax, and semantic inconsistencies. Problems with data quality tend to fall into two categories. However, achieving high-quality data standards is a major task because of the variety of ways that errors might be introduced in a system and the difficulty of correcting them systematically. In clinical research informatics, better data quality translates into better research results and better patient care. For an increasing number of users who rely on information as one of their most important assets, enforcing high data quality levels represents a strategic investment to preserve the value of the data. Assessing and improving the quality of data used by clinical research informatics tools are both important and difficult tasks.
Some of the clinical research tools are referred to as de-identified data tools. As a crucial step of the clinical research process, it is important to establish and maintain organization-wide standards for data quality management to ensure consistency across all systems designed primarily for cohort identification, allowing users to perform an enterprise-wide search on a clinical research data repository to determine the existence of a set of patients meeting certain inclusion or exclusion criteria. In addition, effective data governance and management are essential to ensuring accurate data counts, reports, and validation. Providing high-quality, reliable, and statistically sound data is a primary goal for clinical research informatics. Maintaining data quality is a fundamental requirement for any successful and long-term data management.
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