Excellent coordination and relations with suppliers and customers are essential in acquiring external resources in a timely fashion and in reducing uncertainties in the business operation. In the health care sector, there are several influencing factors involving in the service supply chain. Extensive research has been done on understanding how the various factors in the value chain contribute to the operational performance of the health care service system. Optimal management of the informatics contributes not only to lower operational cost but also improves performance efficiency, medical service and as well as patients' satisfaction. The studies are mainly focusing on the operational processes or supply chain, but merely on the entire value chain in which multiple stakeholders are concerned. This research specifically involves the informatics within the health care sector that affect the predictive and diagnostics model whilst utilising social big data and machine learning techniques.
This project is within the scope of the social health care sector, with the project aim to develop an integrative health care service system. There are several objectives in this project:
- Comprehensive analysis of the service supply chain in the health care sector, regionally or nationally. By incorporating the various actors, a research model highlighting all the stakeholders/players in the health care system is expected. Specifically, the study can be further focusing on a particular patient or concerned groups;
- Empirical studies are carried out for the proposed research model on the complexity of drug supply chain using social big data or other informatics techniques (this can be a comparative study among different countries by using social big data);
- Development of a predictive model with the possible application of various algorithm (i.e. time series) and use of big data.
Applications close 5pm, Friday 30 August 2019
This scholarship is available over 3 years.
- Stipend of $27,596 per annum tax exempt (2019 rate)
To be eligible you must:
- be a domestic candidate (domestic includes candidates with Australian Citizenship, Australian Permanent Residency or New Zealand Citizenship).
- meet Deakin's PhD entry requirements
- be enrolling full time and hold an honours degree (first class) or an equivalent standard master's degree with a substantial research component.
Please refer to the research degree entry pathways page for further information.
Additional desirable criteria include:
- background and expertise in data science and artificial intelligence.
How to apply
Please apply using the expression of interest form
For more information about this scholarship, please contact Dr Kris Law
Dr Kris Law
Associate Professor, Engineering Management and Education
Email Kris Law
+61 3 522 73533