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Defining and Developing a Generic Framework for Monitoring Data Quality in Clinical Research

Journal Article


Abstract


  • Evidence for the need for high data quality in clinical research is well established. The rigor of clinical research conclusions rely heavily on good quality data, which relies on good documentation practices. Little attention has been given to clear guidelines and definitions to monitor data quality. To address this, a "fit-for-use" data quality monitoring framework (DQMF) for clinical research was developed based on a holistic design-oriented approach. An integrated literature review and feasibility study underpinned the framework development. Ontology of key terms, concepts, methods, and standards were recorded using a consensus approach and mind mapping technique. The DQMF is presented as a nested concentric network illustrating concept relationships and hierarchy. Face validation was conducted, and common terminology and definitions are listed. The consolidated DQMF can be adapted according to study context and data availability aiding in the development of a long-term strategy with increased efficacy for clinical data quality monitoring.

Authors


Publication Date


  • 2018

Citation


  • Houston, L., Yu, P., Martin, A. & Probst, Y. (2018). Defining and Developing a Generic Framework for Monitoring Data Quality in Clinical Research. AMIA Annual Symposium Proceedings., 2018 1300-1309.

Scopus Eid


  • 2-s2.0-85062381937

Number Of Pages


  • 9

Start Page


  • 1300

End Page


  • 1309

Volume


  • 2018

Place Of Publication


  • United States

Abstract


  • Evidence for the need for high data quality in clinical research is well established. The rigor of clinical research conclusions rely heavily on good quality data, which relies on good documentation practices. Little attention has been given to clear guidelines and definitions to monitor data quality. To address this, a "fit-for-use" data quality monitoring framework (DQMF) for clinical research was developed based on a holistic design-oriented approach. An integrated literature review and feasibility study underpinned the framework development. Ontology of key terms, concepts, methods, and standards were recorded using a consensus approach and mind mapping technique. The DQMF is presented as a nested concentric network illustrating concept relationships and hierarchy. Face validation was conducted, and common terminology and definitions are listed. The consolidated DQMF can be adapted according to study context and data availability aiding in the development of a long-term strategy with increased efficacy for clinical data quality monitoring.

Authors


Publication Date


  • 2018

Citation


  • Houston, L., Yu, P., Martin, A. & Probst, Y. (2018). Defining and Developing a Generic Framework for Monitoring Data Quality in Clinical Research. AMIA Annual Symposium Proceedings., 2018 1300-1309.

Scopus Eid


  • 2-s2.0-85062381937

Number Of Pages


  • 9

Start Page


  • 1300

End Page


  • 1309

Volume


  • 2018

Place Of Publication


  • United States