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I33 Data quality indicators for huntington’s disease observational studies; data quality indicators framework – an explorative study
  1. Jeton Iseni1,
  2. Olaf Jacob2
  1. 1Ulm University Hospital, Department of Neurology, Ulm, Germany
  2. 2University of Applied Sciences, Dean Faculty Information Management, Neu-Ulm, Germany

Abstract

Background The benefit of scientific cohort and registry studies like Enroll-HD, is highly dependent on the quality of the collected data. The data quality indicators (DQIs) can be used to highlight potential quality concerns, identify areas that need further study and investigation, and track changes over time. The goal of global non-interventional Huntington’s disease (HD) studies like Enroll-HD, is to build a large and rich database of clinical information and bio-­specimens that will serve as a basis for future studies aimed at developing tools and biomarkers for progression and prognosis. High quality data and important DQIs are indispensable for this intention.

Aim To explore DQIs recommended in the literature, to develop an appropriate framework of DQIs for non-interventional studies and to analyse the indicators while conducting an explorative study.

Method For the systematic literature analysis, the focus was on publications and books which were published after 1996. The DQIs from the Enroll-HD protocol were combined with the DQIs from the literature review. After creating the set of DQIs, the explorative study was conducted with the management tool Success Resource Deployment. A questionnaire was sent to 24 experts to evaluate the importance of each DQI.

Results The combination of the DQI set in the global study Enroll-HD with the proposed DQIs methods from the literature, resulted into a strong DQIs frame, with a total number of 28 indicators. For the explorative analysis, out of 24 approached experts to participate in this study, 33% sent back their questionnaires. The DQIs total importance level for the pre­sent time was rated in average with 5.925 out of the maximum of 7.00 points. This rate is quite high and shows that the DQIs listed are very important for the daily work with observational studies. Looking into the importance ratings for the future, the importance rate in aver­age will increase to 6.02 points, which means the indicators will become even more im­portant. Looking into the difference of importance between today and the future, the minimum of the importance change is −0.86 and the maximum is +0.50.

Conclusion The explorative study demonstrated that the developed DQIs frame from the combination of the quality set in the global study Enroll-HD with the proposed DQIs methods from the literature is very strong and highly important. The low number of the study participants is a limiting factor and therefore, the results should be cross-checked while conducting an empirical study.

  • Data Quality Indicators
  • Data Quality
  • Quality Indicators Framework
  • Cohort Studies

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