About the Project
This project will investigate sepsis prediction algorithms for both children and adults. Recognising sepsis as a global threat, Queensland Health has recently created the Sepsis Breakthrough Collaborative. One focus of this initiative is to rely on the power of data and machine learning algorithms to develop a decision tool that supports early disease detection and treatment. While training and developing robust machine learning models are critical in detecting the disease with high accuracy, it is equally important that these models are effectively integrated into clinical workflows and effectively used by health practitioners such as doctors and nurses.
This project focuses on the effective use of sepsis detection algorithms and how the algorithmic challenges can be overcome. Overall, it will address the following research question: How can sepsis prediction algorithms be effectively integrated, used by health practitioners and adapted and diffused to different clinical settings?
The research will advance knowledge on the use of algorithms for sepsis detection in practice, and how algorithmic processes and tools should be designed, developed, and integrated to maximise value for patients, doctors and other stakeholders. The aim is to develop a theoretical model of effective use for sepsis prediction algorithms, as well as expert guidance on how algorithmic work processes should be designed and managed in practice.
About the Team
This project commenced in February 2022 with the recruitment of PhD researcher Krishna Dermawan who is based at The University of Queensland. Krishna will have the opportunity to collaborate with leading experts in Queensland Health to understand how clinical teams can best leverage new AI risk prediction algorithms, and work with the Sepsis Breakthrough Collaborative, a new initiative at Queensland Health, aiming to utilize machine learning algorithms for early detection of sepsis in children and adults. The project will help and support the Queensland Health team to minimize the risks and maximize the value of algorithmic decision making. In addition to helping to understand and improve the rollout and use of new risk prediction tools for sepsis, the knowledge from this project will have implications for how clinicians use a range of new digital health tools, as the sepsis case is an instance of a general trend occurring across the clinical specialties. It is supervised by CIRES Research Director and Chief Investigator Professor Marta Indulska, Dr Ida Asadi Someh, Professor Andrew Burton-Jones, and Dr Adam Irwin (Queensland Health).
This project is one of three CIRES projects with Queensland Health related to paediatric sepsis management and focuses on how clinicians can best use the tools and implementation issues that need to be managed. The other two projects will develop technical transparent solutions for sepsis detection and more details can be found via the project pages below:
Expanding Data Sets to Allow Improved Critical Care for Children – Inpatient Risk Prediction
Expanding Data Sets to Allow Improved Critical Care for Children – Outpatient Risk Prediction
project researchers
Prof Andrew Burton-Jones
Prof Marta Indulska (Principal Advisor)
Mr Krishna Dermawan (PhD Researcher)
Dr Adam Irwin (Queensland Health)
partner investigator
