# A call to action for adverse drug event (ADE) detection and prevention

## TL;DR

This paper calls for better detection and prevention of adverse drug events using standardized data and AI.

## Contribution

The proposal of an ADE value set to standardize ADE data for improved AI and clinical decision support.

## Key findings

- Standardized ADE data can improve identification and documentation in EHRs.
- AI and ML capabilities can be enhanced through standardized ADE information.

## Abstract

Injury from medication use, known as an adverse drug event (ADE) accounts for millions of emergency department visits globally and thousands of hospitalizations annually within the United States. Efforts to prevent and detect ADEs within healthcare systems are complicated by data quality, lack of data standardization, and actionable clinical decision support systems. United States Pharmacopeia (USP) proposes the use of an ADE value set, a standardized grouping of medical terms, to improve the identification, documentation, and use of ADE information in EHRs. Artificial Intelligence and Machine Learning capabilities would be further strengthened through the standardization of ADE data and information.