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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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Use of a collaborative database for epidemiological analyses and professional practice evaluation.

Evelyne Decullier1, Laurent Juillard, Mathilde Bailly

  • 1Hospices Civils de Lyon, Pôle IMER, Lyon, France. evelyne.decullier@chu-lyon.fr

Journal of Evaluation in Clinical Practice
|June 30, 2011
PubMed
Summary

Implementing quality control in nephrology databases like TIRCEL is crucial. It detects 7.5% inconsistent data, ensuring reliable epidemiological analysis and professional practice evaluation.

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Area of Science:

  • Nephrology
  • Medical Informatics
  • Epidemiology

Background:

  • The NEOERICA project demonstrated using general practice databases for nephrology research.
  • Routinely collected data offers potential for epidemiological analysis and professional practice evaluation.
  • The TIRCEL network in Lyon utilizes an online database with 468 professionals and 983 patients in 2008.

Purpose of the Study:

  • To assess the impact of a quality control process on data integrity within operational databases.
  • To investigate the influence of measurement scales on error frequency.
  • To determine how data quality affects variables used for professional practice evaluation.

Main Methods:

  • A quality control process was established to evaluate data.
  • The impact of this process on data accuracy was documented.
  • Error frequency was analyzed concerning measurement scales and data quality's effect on evaluative variables.

Main Results:

  • Quality control identified 7.5% of data as inconsistent, with error rates varying by parameter (e.g., <1% for blood pressure, >30% for serum iron).
  • Data was validated (80.4%), corrected directly (12.9%), corrected by the lab (5.6%), or set to missing (1.2%).
  • Average proteinuria measurements significantly changed post-quality control (2.09 g/24h vs. 0.82 g/24h), while the median remained stable (0.21 g/24h).

Conclusions:

  • Strict quality control, data-entering limits, and alarms are essential for specialty databases like TIRCEL.
  • Without these measures, such databases are unsuitable for epidemiological research.
  • Reliable extraction of professional practice indicators necessitates rigorous data quality assurance.