Transforming Opioid Poisoning Surveillance Through Novel Technologies: Rationale and Methodological Protocol for Applying Natural Language Processing to Emergency Department Data
This study is the first to apply NLP at large scale to Australian ED data for drug poisonings. By improving the accuracy and consistency of opioid poisoning identification, this approach can strengthen routine surveillance and better inform timely policy and health system responses without increasing the workload for clinical staff.
Authors
- Jane Hayman (ORCID: https://orcid.org/0000-0002-5803-7178)
- Joanna F. Dipnall (ORCID: https://orcid.org/0000-0001-7543-0687)
- Nadine E. Andrew (ORCID: https://orcid.org/0000-0002-4846-2840)
- Amanda Roxburgh (ORCID: https://orcid.org/0000-0001-8609-0075)
- Richard Anthony Beare
- Paul M. Dietze
- Tina Lam
- Suzanne Nielsen
- Ting Xia
Institutions
- Burnet Institute (AU)
- Deakin University (AU)
- Monash Medical Centre (AU)
- Department of Health and Aged Care (AU)
- Monash Alfred Psychiatry Research centre (AU)
- Peninsula Health (AU)
- Monash Health (AU)
- Centre for Ageing Better (GB)
- Monash University (AU)
Publication Details
- Journal
- Drug and Alcohol Review
- Published
- 2026-02-01
- DOI
- https://doi.org/10.1111/dar.70117
- Primary Topic
- Opioid Use Disorder Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Monash University
- Medical Research Council
- National Health and Medical Research Council