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Public Health Research and Practice Public Health Research and Practice Society
The peer-reviewed journal of the Sax Institute
RESEARCH ARTICLE (Open Access)

Routinely collected data as a strategic resource for research: priorities for methods and workforce

Louisa Jorm A *
+ Author Affiliations
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A Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia.

* Correspondence to: l.jorm@unsw.edu.au

Public Health Research and Practice 25, e2541540 https://doi.org/10.17061/phrp2541540
Published: 30 September 2015

2015 © Jorm. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Licence, which allows others to redistribute, adapt and share this work non-commercially provided they attribute the work and any adapted version of it is distributed under the same Creative Commons licence terms.

Abstract

In the era of ‘big data’, research using routinely collected data offers greater potential than ever before to drive health system effectiveness and efficiency, and population health improvement. In Australia, the policy environment, and emerging frameworks and processes for data governance and access, increasingly support the use of routinely collected data for research. Capitalising on this strategic resource requires investment in both research methods and research workforce. Priorities for methods development include validation studies, techniques for analysing complex longitudinal data, exploration of bias introduced through linkage error, and a robust toolkit to evaluate policies and programs using ?natural experiments?. Priorities for workforce development include broadening the skills base of the existing research workforce, and the formation of new, larger, interdisciplinary research teams to incorporate capabilities in computer science, partnership research, research translation and the ?business? aspects of research. Large-scale, long-term partnership approaches involving government, industry and researchers offer the most promising way to maximise returns on investment in research using routinely collected data.