About

Anton Pak is a Research Fellow in Applied Economics and Data Science at the Australian Institute of Tropical Health and Medicine, James Cook University. Anton is an applied economist by training and his research interests focus on the behaviour of patients and their choices, health workforce, utilisation of emergency department services, and primary care. He examines empirical questions by utilising health economics theory and concepts and by analysing large panel and cross-sectional datasets (including linked data) using classical econometrics techniques, as well as machine learning methods. Anton also supports collaboration across JCU Colleges and Townsville Hospital providing health economics and data analysis advice.

Before commencing at JCU in 2019, Anton completed his PhD in Economics from the University of Queensland. Previously, he worked as a business and research consultant in private and public sectors.

See my publications at https://sites.google.com/view/antonpak/research

 

Research Disciplines
Socio-Economic Objectives
Publications

These are the most recent publications associated with this author. To see a detailed profile of all publications stored at JCU, visit ResearchOnline@JCU. Hover over Altmetrics badges to see social impact.

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ResearchOnline@JCU stores 13+ research outputs authored by Dr Anton Pak from 2015 onwards.

Current Funding

Current and recent Research Funding to JCU is shown by funding source and project.

Emergency Medicine Foundation - Rural and Remote Grant

ED waiting time predictions in real-time: development of data acquisition system and performance evaluation of advanced statistical models.

Indicative Funding
$36,733 over 1 year (administered by Metro South Hospital and Health Service)
Summary
Emergency department (ED) waiting times are a significant predictor of the patient experience. This project aims to use advanced statistical models and machine-learning algorithms to capture dynamic fluctuations in waiting time, to implement and validate the prediction performance of these models. A solution that is capable of sourcing data from ED information systems and feed it into prediction models to generate waiting time forecasts would bring practical benefits for staff and patients. There is also potential to assist clinicians and nurses to estimate demand for care and calibrate workflow.
Investigators
Andrew Staib, Anton Pak and Kelly Trinh in collaboration with Rob Eley and Brenda Gannon (Metro South Hospital and Health Service, Australian Institute of Tropical Health & Medicine, College of Science & Engineering and The University of Queensland)
Keywords
Waiting time prediction; Data acquisition system; Machine learning; Emergency department
Collaboration

The map shows research collaborations by institution from the past 7 years.
Note: Map points are indicative of the countries or states that institutions are associated with.

  • 5+ collaborations
  • 4 collaborations
  • 3 collaborations
  • 2 collaborations
  • 1 collaboration
  • Indicates the Tropics (Torrid Zone)

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