Impact of the Emergency Department Patient Admissions Predictive Tool (EDPAPT)

Can a model that forecasts patient admissions assist with the allocation of inpatient beds to alleviate overcrowding and access block?

Grant ID: PROJ-2008-006-Green-EDPAPT

Project Summary

Hospital occupancy rates regularly approach 100%, with resultant access block, ambulance bypass, and the last-minute cancellation of elective surgery patients. More efficient management of inpatient beds to reduce these predicaments is imperative. This project will evaluate the impact of a patient admission forecasting system - the Emergency Department Patient Admissions Predictive Tool (EDPAPT) - that has been developed from analysis of historical admissions data at the Gold Coast Hospital.

The aim of the project will determine whether a model that forecasts patient admissions can assist with the allocation of inpatient beds to alleviate one of the major problems of most Emergency Department (ED)s: overcrowding and access block. Specifically it will determine whether the number of elective surgery cancellations and ambulance bypass occurrences are impacted by using a prediction tool, and what impact there is on ED and bed management work practices. The study will also determine if bed managers will make use of prediction tools or whether there are barriers to their use of it, such as perceived inaccuracies, preferences to rely on own judgements or default to current, familiar modus operandi.

The project was a collaboration with CSIRO’s Australian eHealth Research Centre and Queensland Health, with support from Griffith University and the Queensland University of Technology.


Dr Green and his research collaborators showed that the Patient Admission Prediction Tool (PAPT) could predict with more than 90% accuracy: the number of emergency department patient arrivals; the medical urgency and required speciality; and admission and discharge times. The tool is now accessible to all Queensland hospitals, allowing them to accurately predict patient admissions. This means they can roster appropriate staff, allocate the right number of beds, book surgeries ahead of time and reduce emergency waiting times for patients.

CSIRO estimated that if PAPT was rolled out nationally, it would lead to deliver productivity gains of up to $23 million in direct cost savings from improved bed usage, reduced elective surgery cancellations and patient health benefits.

PAPT received the 2012 CeBIT Business Award for Innovation


Jessup, M., Crilly, J., Boyle, J., Wallis, M., Lind, J., Green, D. and Fitzgerald, G., 2016. Users’ experiences of an emergency department patient admission predictive tool: A qualitative evaluation. Health informatics journal, 22(3), pp.618-632.

Crilly, J.L., Boyle, J., Jessup, M., Wallis, M., Lind, J., Green, D. and FitzGerald, G., 2015. The implementation and evaluation of the patient admission prediction tool: assessing its impact on decision-making strategies and patient flow outcomes in 2 Australian hospitals. Quality management in health care, 24(4), pp.169-176.

Boyle, J., Jessup, M., Crilly, J., Green, D., Lind, J., Wallis, M., Miller, P. and Fitzgerald, G., 2012. Predicting emergency department admissions. Emergency Medicine Journal, 29(5), pp.358-365.

Boyle, J.R., Sparks, R.S., Keijzers, G.B., Crilly, J.L., Lind, J.F. and Ryan, L.M., 2011. Prediction and surveillance of influenza epidemics. Medical journal of Australia, 194, pp.S28-S33.

Jessup, M., Wallis, M., Boyle, J., Crilly, J., Lind, J., Green, D., Miller, P. and Fitzgerald, G., 2010. Implementing an emergency department patient admission predictive tool: insights from practice. Journal of health organization and management.



Amount Awarded


Grant Scheme


Principal Investigator:
Dr David Green

Co Investigators:
Prof Marianne Wallis
Prof Gerard FitzGerald
Dr James Lind
Dr Julia Crilly
Dr Melanie Jessup
Dr Justin Boyle
Dr Peter Miller


Collaborating Institutions


  • CSIRO case study
  • CSIRO (2009)
  • Austin Health collaboration

  • Media

  • Australian Hospital and Healthcare Bulletin (13/11/2013)
  • PUlse +IT (1/3/2013)
  • Health IT news (7/3/2018)
  • ABC podcast 2017: "Big data, better hospitals"
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