Literature DB >> 12071777

Good pharmacovigilance practices: technology enabled.

Robert C Nelson1, Bruce Palsulich, Victor Gogolak.   

Abstract

The assessment of spontaneous reports is most effective it is conducted within a defined and rigorous process. The framework for good pharmacovigilance process (GPVP) is proposed as a subset of good postmarketing surveillance process (GPMSP), a functional structure for both a public health and corporate risk management strategy. GPVP has good practices that implement each step within a defined process. These practices are designed to efficiently and effectively detect and alert the drug safety professional to new and potentially important information on drug-associated adverse reactions. These practices are enabled by applied technology designed specifically for the review and assessment of spontaneous reports. Specific practices include rules-based triage, active query prompts for severe organ insults, contextual single case evaluation, statistical proportionality and correlational checks, case-series analyses, and templates for signal work-up and interpretation. These practices and the overall GPVP are supported by state-of-the-art web-based systems with powerful analytical engines, workflow and audit trials to allow validated systems support for valid drug safety signalling efforts. It is also important to understand that a process has a defined set of steps and any one cannot stand independently. Specifically, advanced use of technical alerting methods in isolation can mislead and allow one to misunderstand priorities and relative value. In the end, pharmacovigilance is a clinical art and a component process to the science of pharmacoepidemiology and risk management.

Mesh:

Year:  2002        PMID: 12071777     DOI: 10.2165/00002018-200225060-00004

Source DB:  PubMed          Journal:  Drug Saf        ISSN: 0114-5916            Impact factor:   5.606


  4 in total

1.  Automated support for pharmacovigilance: a proposed system.

Authors:  Roselie A Bright; Robert C Nelson
Journal:  Pharmacoepidemiol Drug Saf       Date:  2002-03       Impact factor: 2.890

2.  We need a postmarketing drug development process!

Authors:  R Nelson
Journal:  Pharmacoepidemiol Drug Saf       Date:  2000-05       Impact factor: 2.890

3.  [Imputation of the unexpected or toxic effects of drugs. Actualization of the method used in France].

Authors:  B Bégaud; J C Evreux; J Jouglard; G Lagier
Journal:  Therapie       Date:  1985 Mar-Apr       Impact factor: 2.070

4.  A method for estimating the probability of adverse drug reactions.

Authors:  C A Naranjo; U Busto; E M Sellers; P Sandor; I Ruiz; E A Roberts; E Janecek; C Domecq; D J Greenblatt
Journal:  Clin Pharmacol Ther       Date:  1981-08       Impact factor: 6.875

  4 in total
  2 in total

1.  Adverse drug reactions: analysis of spontaneous reporting system in Europe in 2007-2009.

Authors:  Jindrich Srba; Veronika Descikova; Jiri Vlcek
Journal:  Eur J Clin Pharmacol       Date:  2012-02-01       Impact factor: 2.953

2.  Information technology in pharmacovigilance: Benefits, challenges, and future directions from industry perspectives.

Authors:  Zhengwu Lu
Journal:  Drug Healthc Patient Saf       Date:  2009-10-15
  2 in total

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