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, 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
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.
Conflict of interest statement
Declaration of interests ASy, WS, MGA, DF, HE, ZC, SD, and DWB received salary support from a grant funded by IBM Watson Health. DWB has received research support and consults for EarlySense, which makes patient safety monitoring systems. He receives cash compensation from CDI (Negev), which is a not-for-profit incubator for health IT startups. He receives equity from Valera Health, which makes software to help patients with chronic diseases, Clew, which makes software to support clinical decision making in intensive care, and MDClone, which takes clinical data and produces deidentified versions of it. He consults for and receives equity from AESOP, which makes software to reduce medication error rates, and FeelBetter. He has received research support from MedAware. GPJ is employed by IBM Watson Health, and her compensation includes salary and equity. KR was employed by IBM Watson Health, and is employed by CVS Health; his compensation from both IBM and CVS Health includes salary and equity. All other authors declare no competing interests.
Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.
Source: PubMed