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Clinical Information Model Based Data Quality Checks: Theory and Example.

Erik Tute1, Antje Wulff1, Michael Marschollek1

  • 1Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School.

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|April 4, 2019
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Summary
This summary is machine-generated.

Clinical Information Models (CIMs) effectively define data quality checks for range, format, and value sets. While useful, CIMs alone do not provide a complete solution for standardized data quality assessment in healthcare.

Keywords:
Data qualityinteroperabilityquality assessmentreuse

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Area of Science:

  • Health Informatics
  • Clinical Data Management

Background:

  • Clinical Information Models (CIMs) offer a structured approach to managing healthcare data.
  • Ensuring data quality (DQ) is crucial for reliable clinical research and patient care.

Purpose of the Study:

  • To describe principles for using CIMs to perform data quality checks.
  • To demonstrate the application of these principles in a real-world clinical setting.

Main Methods:

  • Utilized openEHR compliant CIMs to define data quality checks as constraints.
  • Applied these methods to an Extract, Transform, Load (ETL) process involving assisted ventilation data from a pediatric intensive care unit.

Main Results:

  • A generic component successfully logged aggregated DQ-check results across approximately 28 million data entries.
  • Identified data quality issues included range violations, format errors, and value set discrepancies.

Conclusions:

  • Clinical Information Models are suitable for defining specific data quality constraints (range, format, value set, cardinality).
  • CIMs are a valuable component but not a standalone solution for comprehensive, standardized data quality assessment.