Exploring data quality monitoring procedures in the clinical research setting: Insights from clinical studies
Year: 2020
Type: dissertation
Abstract: To learn about human health, clinical research studies are conducted. A substantial concern for all clinical research studies is the failure to collect, process and present good quality data. Poor data quality may stem from error. International guidelines have identified that it is an essential need to monitor study activity to ensure that the rights, safety and wellbeing of participants are protected. However, the guidelines provide limited insight on how to perform monitoring procedures including the nature and extent of monitoring needed to ensure quality. Without clear guidance, this leaves clinical researchers confused about the most appropriate quality assurance and control procedures.\nThe central hypothesis of this thesis is that despite the wide variations, exploration and evaluation of appropriate data quality monitoring procedures in clinical research studies will provide guidance toward developing a “fit-for-use” data quality monitoring framework (DQMF). This hypothesis was tested in five key studies using an explanatory sequential design guided by the Data- Information-Knowledge-Wisdom (DIKW) model as the theoretical framework.
Author: Lauren Houston
Language: English
Topic: Data Quality and Management
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Australian Government
OpenAlex ID: W3201336485