Health Data Analytics
The health data analytic team vision is to deliver scientifically robust analytics to improve health outcomes
Our aim is to improve performance and sustainability of the Australian health system by transforming clinical/operational data to create knowledge with analytics, optimisation, real-time monitoring for decision support, risk stratification tools and evaluations.
The team is working with the health sector to address the challenges of our healthcare system. We are developing robust digital productivity tools and evidence to improve workflows and realise operational efficiencies enabling better management of resources and evaluation frameworks for benefits of healthcare reform.
The Health Data Analytics team’s focus is on health data analytics and modelling, evaluation of health systems and their implementations, and performance oversight of health systems. The work comprises a suite of patient flow modelling and evaluation projects with our government, hospital and industry partners. Our analytics, optimisation, operational decision support tools and evaluation frameworks have helped governments, hospital and health organisations gain a better understanding of how to implement strategies to meet patient flow performance targets, capacity visualisation and triage of patients.
Impact on the Health System
Improved patient outcomes by assisting public and private hospitals demonstrate efficiency gains and improved bed management practices. The reduction of overcrowding results in improvements in health outcomes, mortality rates, work related stress and decreased complaints.
Our models have provided information to hospitals to quantify the effect of early discharge on reducing peak occupancy, demonstrating how having the right mix of speciality beds can reduce the length of stay in emergency departments, and how understanding a hospital’s demand dynamics will allow the implementation of strategies to provide a better degree of control.
Health system evaluations have demonstrated how new Telehealth enabled care models impact on patient quality of life and the benefits to primary and acute health care systems. Novel evaluation approaches are utilised for benefits realisation analysis of health systems, service and intervention level impact of new hospital funding programs.
We have developed and validated several models that utilise cohort population and clinical data and are capable of precisely identifying chronic disease patients with a high risk of rehospitalisation. The developed models use high precision predictive algorithms for identifying hospital ‘frequent-flyers’ and are used in National Health Care Homes’ trial and hospital based trials. Preventable hospital readmissions have a crippling effect on the health of a chronic disease patient, healthcare funding and resource utilisation.
Health Data Analytics & Modelling solutions provides:
Health Systems Implementation and Evaluation solutions provides:
Health service performance advice and oversight provides:
Team leader and Senior Principle Research Consultant working on primary and acute healthcare reform and in new models of care for chronic disease management.
Senior Principal Research Scientist working on demand prediction models used in public hospitals.
Principle Statistician winning the CSIRO H&B/AAHL Outstanding Collaboration Award for contributions to the Health Statistics Team, 2015.
Senior Research Scientist focussing on findings used to change hospital Key Performance Indicators.
Senior Health Statistician investigating predictive models for risk of re-hospitalisation.
Research Co-ordinator implementing a novel evaluation framework for Healthlinks Chronic Care.
Postdoctoral Fellow shortlisted for Branko Cesnik Award ‘Best Scientific Paper’ HISA 2017.
Principal Research Consultant leading the IM delivery program at Melbourne Genomic Health Alliance.