Monash Addiction Research Centre (MARC) has received a grant from the National Health and Medical Research Council (NHMRC) to enhance surveillance of opioid-related harms in hospital emergency departments (EDs) through AI.
The project, led by MARC’s Dr Ting Xia, aims to refine ED data coding for improved detection and analysis of opioid incidents.
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Xia said: “By developing the new technology to capture good quality, structured ED data from existing unstructured medical records, we will be able to monitor rates of opioid harm more closely and understand whether current policy interventions lead to their intended outcomes.
“It also has the potential to provide timely identification of unintended consequences or negative outcomes from policy changes, enabling more responsive and effective interventions.
“With recent changes in opioid policies and the drug market driving shifts in patterns of harm – such as the transition to illicit opioids or other drugs – this technology is well-positioned to capture these evolving trends.”
Over the past decade, opioid-related mortality in Australia has doubled, prompting urgent policy changes.
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By GlobalDataHowever, the potential of ED data to assess the impact of these policies is not fully realised due to inadequate detail in current coding methods.
The new NHMRC-funded project seeks to address this gap by developing AI technology to better capture and code ED data.
Xia added: “We know that a range of important details are recorded in the free-text sections of electronic health records.”
Natural language processing (NLP) algorithms will be employed to scrutinise the free-text sections of health records for any indications of opioid-related cases that may have been missed.
Dr Xia explained that these algorithms could identify and accurately code details from phrases such as “took Panadeine Forte”, “took methadone tablets,” or “intentional OD”.
The accurate coding of this data is expected to lead to more ‘reliable’ and timely reporting on the outcomes of policies addressing opioid-related harms.
