DataQuality Dashboard (DQD)
The DataQuality Dashboard (DQD) is a tool that helps check and improve the quality of health data before it is used for research. It is especially useful when converting data into a standard format called the OMOP Common Data Model (CDM). The tool identifies errors in how data is structured, checks if important information is missing, and detects unlikely or impossible values. Using the DQD helps ensure that databases meet basic quality standards for research use.
At a glance
Use when
Converting observational health data to the OMOP CDM, preparing databases for research use, assessing data quality in federated networks
Avoid when
Working with non-OMOP data formats without prior mapping, when only study-specific quality checks are needed
Inputs
Observational health data mapped to the OMOP Common Data Model (CDM)
Outputs
Standardized data quality reports including conformance, completeness, and plausibility metrics
How it works
The DataQuality Dashboard (DQD) is an open-source tool designed to evaluate the quality of observational health data mapped to the OMOP Common Data Model (CDM). It performs conformance, completeness, and plausibility checks across multiple databases in a federated network. The tool generates standardized reports that highlight data issues, enabling data partners to iteratively improve data quality. It has been applied across 15 data partners from 10 countries, demonstrating consistent improvements in data quality, particularly in conformance to CDM specifications.
- Project
- EHDEN
- Funding
- IMI
- Project status
- Completed 2024
- HTA domains
- Clinical Effectiveness
- Categories
- RWE
- Technology
- Non-specific
- Assumptions
- Data has been mapped to the OMOP CDM; the presence of certain data fields is expected for meaningful quality assessment
- Strengths
- Open-source, standardized, supports federated networks, effective at identifying conformance issues, enables iterative quality improvement
- Limitations
- Less effective at addressing completeness and plausibility issues compared to conformance; requires prior mapping to OMOP CDM; may not capture study-specific data quality needs
- Also known as
- DQD
Questions this answers
- › Does my database follow the OMOP CDM structure correctly?
- › Are there missing or incomplete data fields that could affect research results?
- › Are there implausible values in my dataset, like impossible ages or dates?
- › How can I improve the quality of my health data before using it in a study?
- › Can I compare data quality across different databases in a network?
- › Is my data ready for research after mapping to a standard format?
Related methods
Similar by meaning
Beta record. Generated from the primary source via AI extraction and independent audit, pending final human review.