Extending Achilles Heel Data Quality Tool with New Rules Informed by Multi-Site Data Quality Comparison

Area of Science:

• Health Informatics
• Clinical Research Data Management

Background:

• Electronic Health Record (EHR) data is vital for clinical research.
• Data quality is a significant concern in distributed research networks.
• General data quality assessment is feasible and desired, despite question-specific nuances.

Purpose of the Study:

• To compare data quality across 12 Electronic Health Record datasets.
• To extend the Achilles Heel software with new data quality rules and characterization measures.
• To facilitate general data quality assessment on a dataset level.

Main Methods:

• Comparative analysis of 12 Electronic Health Record datasets.
• Development and integration of new data quality rules into the Achilles Heel tool.
• Implementation of novel data characterization measures.

Main Results:

• Identification of variations in data quality across the 12 datasets.
• Successful extension of the Achilles Heel tool with enhanced functionalities.
• Demonstration of the feasibility of general data quality assessment.

Conclusions:

• General data quality assessment of Electronic Health Record datasets is achievable.
• The enhanced Achilles Heel tool provides improved capabilities for data quality evaluation.
• Standardized data quality measures are essential for reliable clinical research.