Transforming Opioid Poisoning Surveillance Through Novel Technologies: Rationale and Methodological Protocol for Applying Natural Language Processing to Emergency Department Data.

ABSTRACT:

Introduction

Timely surveillance of opioid-related harm is critical to inform public health responses and policy evaluation. In Australia, where prescription and illicit opioids remain a leading cause of unintentional drug-induced deaths, emergency departments (ED) are a vital point of contact for acute opioid poisonings. Existing surveillance systems rely on structured coding, yet much relevant information is recorded in free-text fields, leading to underreporting or misclassification. This limits opportunistic identification of emerging patterns and weakens the evidence base for evaluating policy reforms. We aim to improve surveillance accuracy by applying natural language processing (NLP) to routinely collected ED data.

Methods

Using medical concept annotation tools, we w

SUBMITTER: Xia T 

PROVIDER: S-EPMC12917345 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature