Routinely collected data as a strategic resource for research: priorities for methods and workforce
Routinely collected data as a strategic resource for research: priorities for methods and workforce
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DOI：
10.17061/phrp2541540
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发表时间：
2015-09-01
影响因子：
4.4
通讯作者：
Jorm, Louisa
中科院分区：
文献类型：
--
作者：
Jorm, Louisa
在“大数据”时代，使用常规收集的数据进行研究为推动卫生系统的有效性和效率以及人口健康的改善提供了比以往更大的潜力。在澳大利亚，政策环境以及数据治理和访问的新兴框架和流程越来越支持使用常规收集的数据进行研究。利用这一战略资源需要对研究方法和研究人员进行投资。方法开发的优先事项包括验证研究、分析复杂纵向数据的技术、对连锁误差引入的偏差的探索，以及使用“自然实验”评估政策和计划的强大工具包。劳动力发展的优先事项包括扩大现有研究人员的技能基础，以及组建新的、更大的跨学科研究团队，以整合计算机科学、合作研究、研究翻译和研究的“商业”方面的能力。政府、工业界和研究人员参与的长期伙伴关系方法提供了利用常规收集的数据最大限度地提高研究投资回报的最有希望的方法。
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.