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.

Learning Objective 1: After participating in this session, the learner should be better able to:
1. Understand the current issues around data quality monitoring in clinical research, including the methods and techniques available to monitor data quality.
2. Discuss and recommend approaches to minimising error throughout the life-cycle of a clinical research trial.
3. Formulate an approach to implement the proposed data quality monitoring framework for a broad range of clinical trials.
4. Describe the challenges and possible solutions to monitoring and maintaining data quality across different clinical research settings.


Lauren Houston (Presenter)
University of Wollongong

Ping Yu, University of Wollongong
Allison Martin, University of Wollongong
Yasmine Probst, University of Wollongong

Presentation Materials: