Objective:

To evaluate the quality of the mapping process during the implementation of the OMOP CDM for lung cancer data and to identify specific challenges and complexities encountered, such as data source variability and mapping inconsistencies.

Key Findings:

• Data quality is critical for the effective secondary use of health data, influencing research findings and clinical decision-making.
• The OMOP CDM facilitates interoperability and large-scale data analysis but faces challenges in mapping quality and consistency, particularly in multicenter settings.
• A data dictionary was provided by the FHIN project, serving as a reference standard for mapping but lacked critical details, complicating replication and leading to potential inconsistencies.

Interpretation:

The study highlights the importance of ensuring data quality during the mapping process to the OMOP CDM, particularly in multicenter research settings, as it directly impacts the reliability of research outcomes.

Limitations:

• The data dictionary lacked critical details regarding original data sources and extraction logic, which may affect the mapping process.
• The evolving nature of the data dictionary may introduce variability and bias in the mapping process, complicating future implementations.

Conclusion:

The study emphasizes the need for structured approaches, such as standardized evaluation frameworks and comprehensive documentation practices, to assess mapping quality and address discrepancies in data integration efforts.