His research combines clinical expertise, data analysis, and machine learning to create frameworks that help clinicians assess and improve patient care across multiple sites.
Can you tell us about your background and what motivated you to pursue research in emergency medicine?
I’ve been an emergency physician for nearly 10 years. My research journey began during my training when I undertook studies such as the alcohol intoxication trial and steroid use for back pain. These projects highlighted the importance of evidence-based practice and the impact research can have on patient care and department efficiency. Over time, I realised that understanding how clinical pathways are applied in real-world settings, and how we can systematically assess them, could improve outcomes for many patients.
Can you provide an overview of your research and describe how EMF funding has supported your research?
My PhD focused on evaluating how chest pain pathways are applied in the emergency department. I combined three elements: understanding the clinical criteria for risk stratification, extracting relevant data from electronic health records, and applying machine learning to classify patient risk based on the available information. EMF funding supported this work by allowing me to access the necessary resources, tools, and collaborations to undertake such a complex, multi-layered project.
How does your work aim to improve patient outcomes or enhance system sustainability?
This work creates frameworks for systematically auditing and monitoring clinical practice over time. By understanding how pathways are applied, we can identify areas where care can be improved, reduce unnecessary investigations, and ensure patients receive appropriate interventions. Over time, this approach supports better patient outcomes, efficient resource use, and safer, evidence-based practice.
What impact do you hope your research will have on emergency medicine practice or healthcare policy in the long term?
The goal is to develop scalable, transferable frameworks that can be applied across multiple hospitals and clinical pathways. By creating systematic audits and longitudinal datasets, we can compare performance across sites, track improvement, and inform quality initiatives or policy decisions. Ultimately, I hope this approach will help standardise care, reduce variation, and support better-informed clinical decisions
What role has collaboration with other clinicians or researchers played in your project, and how has it enhanced your work?
Collaboration was crucial. I worked with PhD supervisors, CSIRO data experts, and colleagues across multiple sites to design data extraction tools, annotation frameworks, and machine learning models. Their expertise allowed me to tackle the different components of the project, clinical knowledge, data management, and technology, in a way that was feasible and meaningful.
Based on your experience, what advice would you give to other clinicians considering applying for EMF funding?
Don’t pursue a grant just for prestige, start with the research question and identify what support you actually need. EMF funding is a tool to help you achieve practical outcomes, not the endpoint itself. Also, be patient, focus on building a strong team, and manage your workload carefully. Mental health and work-life balance are critical, especially when juggling clinical duties, research, and family commitments.
What are the next steps for your research?
I’m now applying the frameworks and processes developed in my PhD to other clinical pathways and emergency departments. The aim is to establish scalable, longitudinal audits and clinical quality registries that allow systematic evaluation of multiple pathways, facilitate comparative research across hospitals, and ultimately improve patient care at a broader scale.