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Interplay of Spontaneous Reporting and Longitudinal Healthcare Databases for Signal Management: Position Statement from the Real-World Evidence and Big Data Special Interest Group of the International Society of Pharmacovigilance

Author

Listed:
  • Salvatore Crisafulli

    (University of Verona)

  • Andrew Bate

    (Global Safety, GSK
    London School of Hygiene and Tropical Medicine)

  • Jeffrey Stuart Brown

    (TriNetX
    Harvard Medical School)

  • Gianmario Candore

    (Bayer AG)

  • Rebecca E. Chandler

    (Coalition for Epidemic Preparedness Innovations)

  • Tarek A. Hammad

    (Takeda Development Center Americas, Inc.)

  • Samantha Lane

    (Drug Safety Research Unit
    University of Portsmouth)

  • Judith Christina Maro

    (Harvard Medical School)

  • G. Niklas Norén

    (Uppsala Monitoring Centre)

  • Antoine Pariente

    (Université de Bordeaux, INSERM, BPH, Team AHeaD, U1219
    Service de Pharmacologie Médicale, CHU de Bordeaux, INSERM, U1219)

  • Mulugeta Russom

    (National Medicines and Food Administration, Ministry of Health
    Erasmus Medical Center)

  • Maribel Salas

    (Bayer Pharmaceuticals Inc.
    University of Pennsylvania Perelman School of Medicine)

  • Andrej Segec

    (European Medicines Agency)

  • Saad Shakir

    (Drug Safety Research Unit
    University of Portsmouth)

  • Andrea Spini

    (University of Verona)

  • Sengwee Toh

    (Harvard Medical School)

  • Marco Tuccori

    (University of Verona)

  • Eugène Puijenbroek

    (Netherlands Pharmacovigilance Centre Lareb
    University of Groningen, Groningen Research Institute of Pharmacy)

  • Gianluca Trifirò

    (University of Verona)

Abstract

Signal management, defined as the set of activities from signal detection to recommendations for action, is conducted using different data sources and leveraging data from spontaneous reporting databases (SRDs), which represent the cornerstone of pharmacovigilance. However, the exponentially increasing generation and availability of real-world data collected in longitudinal healthcare databases (LHDs), along with the rapid evolution of artificial intelligence-based algorithms and other advanced analytical methods, offers a wide range of opportunities to complement SRDs throughout all stages of signal management, especially signal detection. Integrating information derived from SRDs and LHDs may reduce their respective limitations, thus potentially enhancing post-marketing surveillance. The aim of this position statement is to critically evaluate the complementary role of SRDs and LHDs in signal management, exploring the potential benefits and challenges in integrating information coming from these two data sources. Furthermore, we presented successful cases of the interplay between SRDs and LHDs for signal management, along with future opportunities and directions to improve such interplay.

Suggested Citation

  • Salvatore Crisafulli & Andrew Bate & Jeffrey Stuart Brown & Gianmario Candore & Rebecca E. Chandler & Tarek A. Hammad & Samantha Lane & Judith Christina Maro & G. Niklas Norén & Antoine Pariente & Mul, 2025. "Interplay of Spontaneous Reporting and Longitudinal Healthcare Databases for Signal Management: Position Statement from the Real-World Evidence and Big Data Special Interest Group of the International," Drug Safety, Springer, vol. 48(9), pages 959-976, September.
    • Handle: RePEc:spr:drugsa:v:48:y:2025:i:9:d:10.1007_s40264-025-01548-3
    DOI: 10.1007/s40264-025-01548-3
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