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Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance

Author

Listed:
  • Raymond Kassekert

    (GlaxoSmithKline, Global Safety)

  • Neal Grabowski

    (AbbVie, Pharmacovigilance and Patient Safety Business Process Office)

  • Denny Lorenz

    (Bayer AG, Medical Affairs and Pharmacovigilance, Pharmaceuticals)

  • Claudia Schaffer

    (Merck Healthcare, Case and Vendor Management-Global Patient Safety)

  • Dieter Kempf

    (Genentech, A Member of the Roche Group)

  • Promit Roy

    (Novartis Global Drug Development
    Trinity College)

  • Oeystein Kjoersvik

    (MSD, R&D IT)

  • Griselda Saldana

    (Amgen, Pharmacovigilance Operations)

  • Sarah ElShal

    (UCB, IT Patient Safety)

Abstract

TransCelerate reports on the results of 2019, 2020, and 2021 member company (MC) surveys on the use of intelligent automation in pharmacovigilance processes. MCs increased the number and extent of implementation of intelligent automation solutions throughout Individual Case Safety Report (ICSR) processing, especially with rule-based automations such as robotic process automation, lookups, and workflows, moving from planning to piloting to implementation over the 3 survey years. Companies remain highly interested in other technologies such as machine learning (ML) and artificial intelligence, which can deliver a human-like interpretation of data and decision making rather than just automating tasks. Intelligent automation solutions are usually used in combination with more than one technology being used simultaneously for the same ICSR process step. Challenges to implementing intelligent automation solutions include finding/having appropriate training data for ML models and the need for harmonized regulatory guidance.

Suggested Citation

  • Raymond Kassekert & Neal Grabowski & Denny Lorenz & Claudia Schaffer & Dieter Kempf & Promit Roy & Oeystein Kjoersvik & Griselda Saldana & Sarah ElShal, 2022. "Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance," Drug Safety, Springer, vol. 45(5), pages 439-448, May.
    • Handle: RePEc:spr:drugsa:v:45:y:2022:i:5:d:10.1007_s40264-022-01164-5
    DOI: 10.1007/s40264-022-01164-5
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    Cited by:

    1. Jürgen Dietrich & Philipp Kazzer, 2023. "Provision and Characterization of a Corpus for Pharmaceutical, Biomedical Named Entity Recognition for Pharmacovigilance: Evaluation of Language Registers and Training Data Sufficiency," Drug Safety, Springer, vol. 46(8), pages 765-779, August.

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