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Data Quality in the ProVal-MS Study: Challenges and Lessons Learned
Peter Pallaoro1,2, Sandra Bilger3, Martin Boeker2
1Data Integration Center, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
Implementing an early data quality (DQ) assessment in multicenter medical research can save significant time. ProVal-MS study data quality management revealed potential savings of 70% working hours.
Area of Science:
- Medical Research
- Data Science
- Clinical Trials
Background:
- Data-driven findings in medical research necessitate robust data quality assessment, especially in multicenter studies.
- Complex data processing pipelines are used to harmonize and integrate data across centers for analysis.
- The ProVal-MS cohort study aims to validate a treatment decision score for adults with relapsing multiple sclerosis.
Purpose of the Study:
- To evaluate and analyze the implemented data quality management strategy within the ProVal-MS study.
- To identify, assess, and resolve data quality issues encountered during data processing.
- To explore and implement improvements for data quality assessment in multicenter studies.
Main Methods:
- A data quality management strategy was developed and implemented for the ProVal-MS cohort study.
- A questionnaire was utilized to identify, evaluate, and analyze data quality issues and resolution times.
- The study focused on the evaluation and analysis of the data quality assessment strategy itself.
Main Results:
- Data quality issues were identified and analyzed using a questionnaire.
- An earlier data quality assessment could potentially save up to 700 working hours (70% of total time).
- The time required to detect and resolve data quality issues was a key metric.
Conclusions:
- Early data quality assessment is crucial for improving efficiency in multicenter medical research.
- The findings suggest a need to integrate data quality assessment earlier in the data processing pipeline.
- Implementing this change is expected to significantly reduce the time and resources spent on data quality management.
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