Intelligent Telehealth in Pharmacovigilance: A Future Perspective

Heba Edrees, Wenyu Song, Ania Syrowatka, Aurélien Simona, Mary G. Amato and David W. Bates ()
Additional contact information
Heba Edrees: Brigham and Women’s Hospital
Wenyu Song: Brigham and Women’s Hospital
Ania Syrowatka: Brigham and Women’s Hospital
Aurélien Simona: Brigham and Women’s Hospital
Mary G. Amato: Brigham and Women’s Hospital
David W. Bates: Brigham and Women’s Hospital

Drug Safety, 2022, vol. 45, issue 5, No 6, 449-458

Abstract: 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.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s40264-022-01172-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:drugsa:v:45:y:2022:i:5:d:10.1007_s40264-022-01172-5

Ordering information: This journal article can be ordered from
http://www.springer.com/adis/journal/40264

DOI: 10.1007/s40264-022-01172-5

Access Statistics for this article

Drug Safety is currently edited by Nitin Joshi

More articles in Drug Safety from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().