IDEAS home Printed from https://ideas.repec.org/a/spr/drugsa/v45y2022i5d10.1007_s40264-022-01172-5.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40264-022-01172-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40264-022-01172-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. Xi Yang & Jiang Bian & Yan Gong & William R. Hogan & Yonghui Wu, 2019. "MADEx: A System for Detecting Medications, Adverse Drug Events, and Their Relations from Clinical Notes," Drug Safety, Springer, vol. 42(1), pages 123-133, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yiqing Zhao & Yue Yu & Hanyin Wang & Yikuan Li & Yu Deng & Guoqian Jiang & Yuan Luo, 2022. "Machine Learning in Causal Inference: Application in Pharmacovigilance," Drug Safety, Springer, vol. 45(5), pages 459-476, May.
    2. Feifan Liu & Abhyuday Jagannatha & Hong Yu, 2019. "Towards Drug Safety Surveillance and Pharmacovigilance: Current Progress in Detecting Medication and Adverse Drug Events from Electronic Health Records," Drug Safety, Springer, vol. 42(1), pages 95-97, January.
    3. Likeng Liang & Jifa Hu & Gang Sun & Na Hong & Ge Wu & Yuejun He & Yong Li & Tianyong Hao & Li Liu & Mengchun Gong, 2022. "Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources," Drug Safety, Springer, vol. 45(5), pages 511-519, May.
    4. Abhyuday Jagannatha & Feifan Liu & Weisong Liu & Hong Yu, 2019. "Overview of the First Natural Language Processing Challenge for Extracting Medication, Indication, and Adverse Drug Events from Electronic Health Record Notes (MADE 1.0)," Drug Safety, Springer, vol. 42(1), pages 99-111, January.
    5. Hui Xing Tan & Chun Hwee Desmond Teo & Pei San Ang & Wei Ping Celine Loke & Mun Yee Tham & Siew Har Tan & Bee Leng Sally Soh & Pei Qin Belinda Foo & Zheng Jye Ling & Wei Luen James Yip & Yixuan Tang &, 2022. "Combining Machine Learning with a Rule-Based Algorithm to Detect and Identify Related Entities of Documented Adverse Drug Reactions on Hospital Discharge Summaries," Drug Safety, Springer, vol. 45(8), pages 853-862, August.
    6. Shungo Imai & Yoh Takekuma & Hitoshi Kashiwagi & Takayuki Miyai & Masaki Kobayashi & Ken Iseki & Mitsuru Sugawara, 2020. "Validation of the usefulness of artificial neural networks for risk prediction of adverse drug reactions used for individual patients in clinical practice," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-12, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:drugsa:v:45:y:2022:i:5:d:10.1007_s40264-022-01172-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com/economics/journal/40264 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.