Exploring completeness in clinical data research networks with DQe-c.

Area of Science:

• Clinical Informatics
• Data Science
• Health Data Management

Background:

• Electronic Health Record (EHR) data quality assessment is often hindered by fragmented, ad hoc processes.
• Ensuring data completeness and conformance is crucial for reliable clinical research and decision-making.
• Existing tools may lack interoperability and scalability for diverse EHR repositories.

Purpose of the Study:

• To develop an open-source, interoperable, and scalable tool for evaluating EHR data quality.
• To provide visualization of data completeness and conformance.
• To offer a reproducible solution for assessing clinical data repositories.

Main Methods:

• The study describes the design and architecture of the DQe-c tool.
• A sample dataset of 200,000 patient records from the Research Patient Data Registry (RPDR) was used for evaluation.
• The tool's outputs were analyzed, including load and test details, completeness, conformance, and missingness in key indicators.

Main Results:

• DQe-c generates web-based reports with graphics and tables summarizing data completeness and conformance.
• The tool was tested on a large EHR dataset, demonstrating its capability to assess data quality.
• Results are organized into detailed sections for comprehensive analysis.

Conclusions:

• The DQe-c tool offers a reproducible and scalable approach to EHR data quality assessment.
• Its open-source nature and interoperability facilitate adoption across different institutions.
• The tool provides valuable insights for managing and processing data in clinical data repositories.