Research in Health

Since 2013 the CMCRC has expanded the coverage of its advanced data sciences program to address the challenges and opportunities facing healthcare funders, providers and consumers. The goals of the R&D program are to drive enhanced integrity and efficiency of the Health Market through development of advanced analytical and performance management solutions. These solutions will maximise use of administrative and transactional data5 of healthcare funders and providers, enhanced by the relevant findings and metrics from Australian and international research.

The program has secured multi-year funding from industry and the Government’s CRC Program as well as academic participation from the medical, health informatics and economics, data mining and information technology faculties and centres of 10 leading universities. A key requirement of the CRC program is that research has to be relevant to and driven by the needs of ‘end users’, who are termed industry participants. Our HMQ industry partners span the public health, private health and accident compensation insurance sectors. Additional health market organisations are expected to join this initiative on an ongoing basis. 

  • The research program is divided into five streams:

1. Low and High Value Care 

  NSW Health, HAMBS

                                      • Identify/ measure low value care

                                      • Measure cost-effectiveness cardiovascular procedures


2. Consumer Empowerment


                                      • Provide personalised information

                                      • Provide Analysis of provider choice

                                      • Improve patient centric care

                                      • Promote Equity and Fairness

3. Predictive Modelling and Risk Stratification

   NSW Health, TAC, Medibank, Health Roundtable, HAMBS

                                      • Design Risk stratification tools (re-admission, chronic disease, return to work)

                                      • Simulate Policy (prevention / new treatments)

                                      • Evaluate DRG design

4. NLP and Unstructured Data

   CMCRC, TAC, Sintelix

                                      • Design tools for analysis of clinical notes/ guidelines

                                      • Analyse phone calls for predictive purposes

​5. Spatio -Temporal Variation

   AIHW / DoH

                                      • Design customisable geographies

                                      • Clustering and analysing health trajectories


Latest Health Research

Chronic conditions can be costly but also preventable as well as predictable. We develop a model to predict in the short term (2-3 years) the onset of one or more chronic conditions.

The study illustrates a framework to predict the progression of chronic diseases from a new perspective using graph theory and social network analysis methods.

Previous studies have documented the application of electronic health insurance claim data for health services research purposes.

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Level 3, 55 Harrington Street, Sydney NSW 2000, Australia.
t. 61 (2) 8088 4200