Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review

Ania Syrowatka; Wenyu Song; Mary G Amato; Dinah Foer; Heba Edrees; Zoe Co; Masha Kuznetsova; Sevan Dulgarian; Diane L Seger; Aurélien Simona; Paul A Bain; Gretchen Purcell Jackson; Kyu Rhee; David W Bates

doi:10.1016/S2589-7500(21)00229-6

Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review

Lancet Digit Health. 2022 Feb;4(2):e137-e148. doi: 10.1016/S2589-7500(21)00229-6. Epub 2021 Nov 23.

Authors

Ania Syrowatka¹, Wenyu Song², Mary G Amato³, Dinah Foer⁴, Heba Edrees³, Zoe Co⁵, Masha Kuznetsova⁶, Sevan Dulgarian⁵, Diane L Seger⁵, Aurélien Simona², Paul A Bain⁷, Gretchen Purcell Jackson⁸, Kyu Rhee⁹, David W Bates¹⁰

Affiliations

¹ Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA. Electronic address: [email protected].
² Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
³ Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA; Massachusetts College of Pharmacy and Health Sciences, Boston, MA, USA.
⁴ Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA; Division of Allergy and Clinical Immunology, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
⁵ Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA.
⁶ Harvard Business School, Boston, MA, USA.
⁷ Countway Library of Medicine, Harvard Medical School, Boston, MA, USA.
⁸ IBM Watson Health, Cambridge, MA, USA; Department of Pediatric Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.
⁹ IBM Watson Health, Cambridge, MA, USA; CVS Health, Wellesley Hills, MA, USA.
¹⁰ Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Harvard T H Chan School of Public Health, Boston, MA, USA.

PMID: 34836823
DOI: 10.1016/S2589-7500(21)00229-6

Abstract

Adverse drug events (ADEs) represent one of the most prevalent types of health-care-related harm, and there is substantial room for improvement in the way that they are currently predicted and detected. We conducted a scoping review to identify key use cases in which artificial intelligence (AI) could be leveraged to reduce the frequency of ADEs. We focused on modern machine learning techniques and natural language processing. 78 articles were included in the scoping review. Studies were heterogeneous and applied various AI techniques covering a wide range of medications and ADEs. We identified several key use cases in which AI could contribute to reducing the frequency and consequences of ADEs, through prediction to prevent ADEs and early detection to mitigate the effects. Most studies (73 [94%] of 78) assessed technical algorithm performance, and few studies evaluated the use of AI in clinical settings. Most articles (58 [74%] of 78) were published within the past 5 years, highlighting an emerging area of study. Availability of new types of data, such as genetic information, and access to unstructured clinical notes might further advance the field.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Artificial Intelligence*
Drug-Related Side Effects and Adverse Reactions / prevention & control*
Humans
Machine Learning*