Results for The Australian eHealth Research Centre


Quality linking of health data to evaluate patient and health service outcomes and key performance indicators following the implementation of patient flow strategies.

In order to understand the effectiveness of health service delivery, and the impact of changes in processes and procedures, it is important to first be capable of analysing the data that documents patients’ journeys through the hospital. This project will bring together key data from multiple disjointed information systems so that analysis can be undertaken on the flow of patients through the Gold Coast Hospital (GCH); from the ambulance, through the Emergency Department, and admission to a ward, including the operating rooms, radiology, pathology, and pharmacy that they encounter up to their departure. With this holistic view of patients’ journey of care, the baseline and measure impact of initiatives will be determined to ensure that patients flow through the environments with minimal delay and improved outcomes.

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

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.

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