Literature DB >> 30545608

Deconstructing the Pharmacovigilance Hype Cycle.

Manfred Hauben1, Robert Reynolds2, Patrick Caubel2.   

Abstract

Data science is making increasing contributions to pharmacovigilance. Although the technical innovation of these works are indisputable, efficient progress in real-world pharmacovigilance signal detection may be hampered by corresponding technology life cycle effects, with a resulting tendency to conclude that, with large enough datasets and intricate algorithms, "the numbers speak for themselves," discounting the importance of clinical and scientific judgment. A practical consequence is overzealous declarations regarding the safety or lack of safety of drugs. We describe these concerns through a critical discussion of key results and conclusions from case studies selected to illustrate these points.
Copyright © 2018. Published by Elsevier Inc.

Entities:  

Keywords:  Big data; Clinical judgement; Informatics; Pharmacovigilance; Safety signals

Mesh:

Year:  2018        PMID: 30545608     DOI: 10.1016/j.clinthera.2018.10.021

Source DB:  PubMed          Journal:  Clin Ther        ISSN: 0149-2918            Impact factor:   3.393


  6 in total

1.  From Data Silos to Standardized, Linked, and FAIR Data for Pharmacovigilance: Current Advances and Challenges with Observational Healthcare Data.

Authors:  Vassilis Koutkias
Journal:  Drug Saf       Date:  2019-05       Impact factor: 5.606

2.  Adverse Drug Reaction Case Safety Practices in Large Biopharmaceutical Organizations from 2007 to 2017: An Industry Survey.

Authors:  Stella Stergiopoulos; Mortiz Fehrle; Patrick Caubel; Louise Tan; Louise Jebson
Journal:  Pharmaceut Med       Date:  2019-12

3.  Response to "Pharmacovigilance 2030: Invited Commentary for the January 2020 'Futures' Edition".

Authors:  Manfred Hauben; William W Gregory; Patrick Caubel
Journal:  Clin Pharmacol Ther       Date:  2020-03-20       Impact factor: 6.875

4.  Hypothesis-free signal detection in healthcare databases: finding its value for pharmacovigilance.

Authors:  Andrew Bate; Ken Hornbuckle; Juhaeri Juhaeri; Stephen P Motsko; Robert F Reynolds
Journal:  Ther Adv Drug Saf       Date:  2019-08-05

5.  Identifying Actionability as a Key Factor for the Adoption of 'Intelligent' Systems for Drug Safety: Lessons Learned from a User-Centred Design Approach.

Authors:  George I Gavriilidis; Vlasios K Dimitriadis; Marie-Christine Jaulent; Pantelis Natsiavas
Journal:  Drug Saf       Date:  2021-10-21       Impact factor: 5.606

6.  A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation.

Authors:  Imogen S Stafford; Mark M Gosink; Enrico Mossotto; Sarah Ennis; Manfred Hauben
Journal:  Inflamm Bowel Dis       Date:  2022-10-03       Impact factor: 7.290

  6 in total

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