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Category: HEALTH RESEARCH PAPERS

LAUNCH FOR THE NATIONAL DIGITAL HEALTH STRATEGY AND FRAMEWORK FOR ACTION

Director of Partner Relations and Program Office Lee-Ann Berger represented the Digital Health CRC at the launch of National Digital Health Strategy and Framework for Action. The Framework was developed in consultation with key health and consumer stakeholders and will act as a road map to high-quality digital healthcare. The strategy will help deliver improved services

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Health, Passion, Career: A chat with Director of Health Services Management Centre about her journey

Health PhD candidate Jo Khoo was a guest host alongside Dr Amalie Dyda on the soundcloud podcast “Stories in Public Health”  interviewing with the Director of Health Services Management Centre Professor Judith Smith, University of Birmingham. In the podcast, Professor Judith Smith shares her professional journey into health services research and management. Her own research

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Scholarship for our Health PhD candidate

Health Ph.D. Candidate Kelsey Chalmers has been offered a scholarship with McMaster University Health Forum in Canada for the Spring/Summer intake of 2018. The Canadian Queen Elizabeth II Diamond Jubilee Scholarship in Strengthening Health Systems program and workshops aims to develop and implement health policies. “I’m really excited to be accepted as a Queen Elizabeth

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PhD Candidate published in Health Journal

Congratulations to our health PhD James John, who published his paper in the International Journal of Environmental Research and Public Health. John’s paper provides an understanding of the various social determinants and health behavioural risk factors associated with dental caries among primary school children in the rural community of Lithgow. Mr James John partners with

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CMCRC HEALTH PHD STUDENTS AT HSRAANZ CONFERENCE

The 10th Health Services and Policy Research Conference was held from 1-3 November 2017 at Surfers Paradise Marriott on the Gold Coast. The conference theme was “Shifting priorities: balancing acute and primary care services”. The conference was presented by the Health Services Research Association of Australia and New Zealand and hosted by the Australian Centre

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JOINT PREDICTION OF ONSET CHRONIC CONDITIONS

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. Five chronic conditions are considered: heart disease, stroke, diabetes, hypertension and cancer. Predictions are made on the basis of standard demographic/socio-economic variables, risk

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PREDICTING CHRONIC DISEASES FROM HEALTHCARE DATA-A FRAMEWORK BASED ON GRAPH THEORY AND SOCIAL NETWORK MEASURES

The study illustrates a framework to predict the progression of chronic diseases from a new perspective using graph theory and social network analysis methods. The framework utilizes large and untapped longitudinal administrative data sets that contain ICD-10-AM disease codes that describe the principal and secondary diagnosis recorded during hospital admissions. The primary focus of the

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A FRAMEWORK FOR ADMINISTRATIVE CLAIM DATA TO EXPLORE HEALTHCARE COORDINATION AND COLLABORATION

Previous studies have documented the application of electronic health insurance claim data for health services research purposes. In addition to administrative and billing details of healthcare services, insurance data reveal important information regarding professional interactions/links that emerge among healthcare service providers through, for example, informal knowledge sharing. By utilising details of such professional interactions and

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JOINT PREDICTION OF CHRONIC CONDITIONS ONSET: COMPARING MULTIVARIATE PROBITS WITH MULTICLASS SUPPORT VECTOR MACHINES

We consider the problem of building accurate models that can predict, in the short term (2-3 years), the onset of one or more specific chronic conditions at individual level. Methods: We consider 5 chronic conditions: heart disease, stroke, diabetes, hypertension and cancer and build two different models that predict all possible combinations of these conditions. Covariates

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