Data quality management in pharmacovigilance.

Abstract

Pharmacovigilance relies on information gathered from the collection of individual case safety reports and other pharmacoepidemiological data. Even given the inherent limitations of spontaneous reports, the usefulness of this data source can be improved with good data quality management. Although under-reporting cannot be remedied this way, the negative impact of incomplete reports, which is another serious problem in pharmacovigilance, can be reduced. Quality management consists of quality planning, quality control, quality assurance and quality improvements. The pharmacovigilance data processing cycle starts with data collection and, in computerised systems, data entry; the next step is data storage and maintenance; followed by data selection, retrieval and manipulation. The resulting data output is analysed and assessed. Finally, conclusions are drawn and decisions made. The increased knowledge feeds back into the data processing cycle. Focussing on the first three steps of the data processing cycle, the different quality dimensions associated with these steps are described in this review, together with examples relevant to pharmacovigilance data. Functioning, well documented, and transparent quality management systems will benefit not only those involved in data collection, management and output production, but, ultimately, also the pharmacovigilance end users, the patients.