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.
Highlights
The US National Academy of Medicine has defined an adverse drug event (ADE) as “an injury resulting from medical intervention related to a drug”.1 These events include non-preventable Adverse drug events (ADEs), and adverse events resulting from medication errors
A systematic review of potentially preventable ADEs showed that rates varied widely across inpatient populations, ranging from less than 0·1% to 13·3%, and depended on the approach for event detection, with more cases identified using prospective reporting methods than retrospective or voluntary reporting methods.[4]
A scoping review showed that rates of ADEs in primary care varied widely according to the study population, setting, medications, and ADEs under study; estimates ranged from 6% in community-dwelling patients prescribed medications for dementia to 81% in patients treated for drug-resistant tuberculosis.[5]
Summary
The US National Academy of Medicine has defined an adverse drug event (ADE) as “an injury resulting from medical intervention related to a drug”.1 These events include non-preventable ADEs ( called adverse drug reactions), and adverse events resulting from medication errors. The US National Academy of Medicine has defined an adverse drug event (ADE) as “an injury resulting from medical intervention related to a drug”.1. An analysis of 28 US state inpatient databases showed that ADEs occurred during 2·1% of all inpatient stays and were present on admission in 5·1% of stays, and management of these ADEs has been estimated to cost US$28 billion annually.[3] This analysis was based on documented diagnostic codes, and undoubtedly underestimated true rates. A systematic review of potentially preventable ADEs showed that rates varied widely across inpatient populations, ranging from less than 0·1% to 13·3%, and depended on the approach for event detection, with more cases identified using prospective reporting methods than retrospective or voluntary reporting methods.[4]. A scoping review showed that rates of ADEs in primary care varied widely according to the study population, setting, medications, and ADEs under study; estimates ranged from 6% in community-dwelling patients prescribed medications for dementia to 81% in patients treated for drug-resistant tuberculosis.[5]
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