Systematische Bewertung und Steigerung der Qualität medizinischer Daten

• Verfasst von: Jacke, Christian Olaf [VerfasserIn]
• Titel: Systematische Bewertung und Steigerung der Qualität medizinischer Daten
• Verf.angabe: C.O. Jacke, M. Kalder, M. Koller, U. Wagner, U.S. Albert
• E-Jahr: 2012
• Jahr: November 2012
• Umfang: 9 S.
• Fussnoten: Gesehen am 21.11.2018
• Titel Quelle: Enthalten in: Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz
• Ort Quelle: Berlin : Springer, 1997
• Jahr Quelle: 2012
• Band/Heft Quelle: 55(2012), 11, Seite 1495-1503
• ISSN Quelle: 1437-1588
• DOI: doi:10.1007/s00103-012-1536-x
• Datenträger: Online-Ressource
• Sprache: ger
• Sach-SW: Cohort studies
• Sach-SW: Data quality
• Sach-SW: Data quality indicators (DQI)
• Sach-SW: Datenqualität
• Sach-SW: Guidelines
• Sach-SW: Kohortenstudie
• Sach-SW: Leitlinie
• Sach-SW: Originaldatenabgleich
• Sach-SW: Qualitätsindikatoren
• Sach-SW: Source data verification
• K10plus-PPN: 1583917330

Abstract

Public health research depends on empirical information that is based on data of high quality. The aim of this study was to apply the current guidelines developed by the Technology and Methodology Platform for Networked Medical Research (TMF) for the independent assessment and enhancement of data quality. A clinical register of female breast cancer patients from two periods (N = 389 of 1996-1997 and N = 488 of 2003-2004) was used. To check the plausibility, organization, and correctness of the data quality levels, data quality indicators (DQI) were chosen, operationalized, and the variance ratios of normative-analytic-defined thresholds were calculated. Significant deviations led to data improvement, which included the commonly known source data verification (SDV). A summary data quality score was calculated before and after application of the guidelines. Eleven out of 24 DQIs were tested. Data quality systematically increased from 51.6 to 67.7%. The guidelines facilitate a systematic assessment and improvement of data quality with a reasonable use of resources. This target-oriented procedure allows for a high transparency of the available data quality, which is essential for health research.