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Routinely collected data as a strategic resource for research: priorities for methods and workforce
1Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia l.jorm@unsw.edu.au.
Leveraging routinely collected health data requires investment in advanced research methods and a skilled workforce. Strategic partnerships are key to maximizing the potential of this data for health system improvement.
Area of Science:
- Health services research
- Data science
Background:
- Routinely collected data offers significant potential for improving health system effectiveness and population health.
- Australia's policy and data governance frameworks increasingly support research using routinely collected data.
Purpose of the Study:
- To outline priorities for methods and workforce development to capitalize on routinely collected data for research.
- To identify strategies for maximizing investment returns in this research area.
Main Methods:
- Identifying priorities for methods development, including validation studies, longitudinal data analysis, bias exploration, and natural experiments.
- Identifying priorities for workforce development, focusing on skill broadening and interdisciplinary team formation.
- Highlighting the importance of large-scale, long-term partnerships involving government, industry, and researchers.
Main Results:
- Methods development priorities include data validation, complex longitudinal analysis, linkage error bias assessment, and policy evaluation tools.
- Workforce development priorities emphasize interdisciplinary teams with expertise in computer science, partnership research, and research translation.
- Large-scale, long-term partnerships are identified as crucial for maximizing research investment returns.
Conclusions:
- Investment in research methods and workforce is essential to harness the potential of routinely collected data.
- Interdisciplinary teams and robust data governance are critical for effective research.
- Collaborative, long-term partnerships are the most promising approach to maximize the value of routinely collected data for health research.
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