Data Quality Management and Analysis

Good data quality management in pharmacovigilance can be relied only on information gathered from the collection of individual case safety reports and other pharmacoepidemiological data. 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 are made. The quality of a pharmacovigilance data system can be defined as a measure of excellence or a state of being free from defects, deficiencies and significant variations and the data quality management includes all the activities that organizations use to direct, control and coordinate the quality of data.

Market Analysis:

The global pharmacovigilance (PV) market size was estimated at USD 3.40 billion in 2016 and is anticipated to witness a CAGR of 13.1% over the forecast period. Increasing incidence of Adverse Drug Reactions (ADRs) is expected to accelerate the demand for pharmacovigilance services over the forecast period. Growing prevalence of chronic diseases is another major contributor for the PV growth. Treatment of these chronic diseases require uptake of combination of drugs resulting in ADR. To curb this problem, PV services are used. According to statistics published in Journal of American Medical Association (JAMA), in 2014, ADR is one of the leading causes of mortality in the U.S. resulting in more than 100,000 deaths every year

  • Data Quality Control and Assurance
  • Quality Planning and Improvement
  • Data Safety Monitoring

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