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Intelligent Telehealth in Pharmacovigilance: A Future Perspective

Author

Listed:
  • Heba Edrees

    (Brigham and Women’s Hospital
    MCPHS University
    Harvard Medical School)

  • Wenyu Song

    (Brigham and Women’s Hospital
    Harvard Medical School)

  • Ania Syrowatka

    (Brigham and Women’s Hospital
    Harvard Medical School)

  • Aurélien Simona

    (Brigham and Women’s Hospital
    Harvard Medical School)

  • Mary G. Amato

    (Brigham and Women’s Hospital)

  • David W. Bates

    (Brigham and Women’s Hospital
    Harvard Medical School
    Harvard School of Public Health)

Abstract

Pharmacovigilance improves patient safety by detecting and preventing adverse drug events. However, challenges exist that limit adverse drug event detection, resulting in many adverse drug events being underreported or inaccurately reported. One challenge includes having access to large data sets from various sources including electronic health records and wearable medical devices. Artificial intelligence, including machine learning methods, such as natural language processing and deep learning, can detect and extract information about adverse drug events, thus automating the pharmacovigilance process and improving the surveillance of known and documented adverse drug events. In addition, with the increased demand for telehealth services, for managing both acute and chronic diseases, artificial intelligence methods can play a role in detecting and preventing adverse drug events. In this review, we discuss two use cases of how artificial intelligence methods may be useful to improve the quality of pharmacovigilance and the role of artificial intelligence in telehealth practices.

Suggested Citation

  • Heba Edrees & Wenyu Song & Ania Syrowatka & Aurélien Simona & Mary G. Amato & David W. Bates, 2022. "Intelligent Telehealth in Pharmacovigilance: A Future Perspective," Drug Safety, Springer, vol. 45(5), pages 449-458, May.
    • Handle: RePEc:spr:drugsa:v:45:y:2022:i:5:d:10.1007_s40264-022-01172-5
    DOI: 10.1007/s40264-022-01172-5
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    References listed on IDEAS

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    1. Christopher McMaster & David Liew & Claire Keith & Parnaz Aminian & Albert Frauman, 2019. "Correction to: A Machine-Learning Algorithm to Optimise Automated Adverse Drug Reaction Detection from Clinical Coding," Drug Safety, Springer, vol. 42(6), pages 807-807, June.
    2. Wei-Hsuan Lo-Ciganic & James L Huang & Hao H Zhang & Jeremy C Weiss & C Kent Kwoh & Julie M Donohue & Adam J Gordon & Gerald Cochran & Daniel C Malone & Courtney C Kuza & Walid F Gellad, 2020. "Using machine learning to predict risk of incident opioid use disorder among fee-for-service Medicare beneficiaries: A prognostic study," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-16, July.
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    4. Christopher McMaster & David Liew & Claire Keith & Parnaz Aminian & Albert Frauman, 2019. "A Machine-Learning Algorithm to Optimise Automated Adverse Drug Reaction Detection from Clinical Coding," Drug Safety, Springer, vol. 42(6), pages 721-725, June.
    5. Susmitha Wunnava & Xiao Qin & Tabassum Kakar & Cansu Sen & Elke A. Rundensteiner & Xiangnan Kong, 2019. "Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding," Drug Safety, Springer, vol. 42(1), pages 113-122, January.
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