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Improving Data Quality in Clinical Research Informatics Tools.

Ahmed AbuHalimeh1

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Frontiers in Big Data
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Summary
This summary is machine-generated.

High-quality data is crucial for clinical research informatics. This study assesses data quality in de-identified systems (i2b2, Epic SlicerDicer) and proposes rules for consistent data management across healthcare organizations.

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clinical research datadata qualityinformaticsmanagement of clinical dataresearch informatics

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Area of Science:

  • Clinical Research Informatics
  • Health Data Management
  • Data Governance

Background:

  • Maintaining high data quality is essential for reliable clinical research and patient care.
  • Inconsistent data formats, syntax, semantics, and poor ETL processes challenge data quality.
  • De-identified data systems are increasingly used for cohort identification, necessitating quality assessment.

Purpose of the Study:

  • To assess and improve data quality within clinical research informatics tools.
  • To compare data quality between two de-identified systems: i2b2 and Epic SlicerDicer.
  • To propose actionable steps for sustaining data quality across healthcare systems.

Main Methods:

  • A real-life case study was conducted at a healthcare organization.
  • Data quality was assessed by comparing results from i2b2 and Epic SlicerDicer.
  • Data quality dimensions specific to clinical research informatics were discussed.

Main Results:

  • Identified data quality issues inherent in de-identified systems.
  • Highlighted inconsistencies between i2b2 and Epic SlicerDicer data.
  • Provided a comparative analysis of data quality in the studied systems.

Conclusions:

  • Establishing organization-wide data quality standards is vital for consistency.
  • Continuous monitoring and improvement of data quality are strategic investments.
  • Proposed rules aim to help healthcare organizations sustain data quality for business intelligence and data governance.