Patients with limited English proficiency face measurable disadvantages in emergency departments, including longer stays, more diagnostic tests, and higher rates of unplanned return visits. Associate professor Roberto Forero’s research addresses these gaps by developing an artificial intelligence supported tool that helps clinicians interpret symptoms accurately across multiple languages and dialects. Forero draws on evidence from emergency care systems in the UK and Australia, including evaluations of the four-hour target, a benchmark requiring most patients to be admitted, discharged, or transferred within the four-hour target. While the target can improve patient flow, it may increase pressure on staff without clear evidence of better clinical outcomes. Working in south-western Sydney, where the Arabic-speaking population is three to four times higher than the national average, he observed delays when interpreters were unavailable outside weekday hours. Improving emergency care communication for diverse populations Using conference connections to inform the next phase of work
The International Forum has been very useful to think about how to build different stages… [It gave us] contacts from different parts of the world. Roberto Forero Associate professor, School of Clinical Medicine, University of New South Wales, Australia
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Arabic dialect complexity was an early driver for the tool, which integrates clinical context, linguistic nuance, and interpreter expertise to support assessment when real-time interpreter support is unavailable. Presenting an early poster at the International Forum on Quality and Safety in Healthcare, Canberra 2025, connected Forero with international colleagues, facilitated knowledge exchange, and informed the next phase of the project.
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