PhD Scholarship Applications now open!
Deadline 24 October 2021
For January or April 2022 start
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.
This project builds on the two other CIRES projects with Queensland Health that will develop technical transparent solutions for sepsis detection.
About the Candidate
This scholarship is an opportunity for a highly motivated student to join the CIRES project team, collaborating with leading experts in Queensland Health to understand how clinical teams can best leverage new AI risk prediction algorithms. The scholarship is one of three CIRES projects with Queensland Health related to paediatric sepsis management. Whereas the other projects focus on the technicalities of the prediction tools, this project focuses on how clinicians can best use the tools and implementation issues that need to be managed.
The successful candidate will work with Sepsis Breakthrough Collaborative, a new initiative at Queensland Health, aiming to utilize machine learning algorithms for early detection of sepsis in children and adults. This project will help and support the QH 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.
For this position, CIRES is seeking a candidate with interdisciplinary interest and capabilities. The candidate will need knowledge in at least one of three areas: (i) clinical work (medical, nursing, or allied health), (ii) computer science or data analytics, and (iii) business management or management information systems. We do not expect candidates to have knowledge/expertise in all three areas, but combinations of expertise will be highly useful and desirable. Experience with field research and qualitative methods will also be valuable.
For January or April 2022 start