PDF(Pubmed)
Abstract：
BACKGROUND: Many areas of healthcare are impacted by a paucity of research that is translatable to clinical practice. Research utilising real-world data, such as routinely collected patient data, may be one option to efficiently create evidence to inform practice and service delivery. Such studies are also valuable for exploring (in)equity of services and outcomes, and benefit from using non-selected samples representing the diversity of the populations served in the \'real world\'. This scoping review aims to identify and map the published research which utilises routinely collected clinical healthcare data. A secondary aim is to explore the extent to which this literature supports the pursuit of social justice in health, including health inequities and intersectional approaches.
METHODS: This review utilises Arksey and O\'Malley\'s methodological framework for scoping reviews and draws on the recommended enhancements of this framework to promote a team-based and mixed methods approach. This includes searching electronic databases and screening papers based on a pre-specified inclusion and exclusion criteria. Data relevant to the research aims will be extracted from included papers, including the clinical/professional area of the topic, the source of data that was used, and whether it addresses elements of social justice. All screening and reviewing will be collaborative and iterative, drawing on strengths of the research team and responsive changes to challenges will be made. Quantitative data will be analysed descriptively, and conceptual content analysis will be utilised to understand qualitative data. These will be collectively synthesised in alignment to the research aims.
CONCLUSIONS: Our findings will highlight the extent to which such research is being conducted and published, including gaps and make recommendations for future endeavours for real-world data studies. The findings from this scoping review will be relevant for practitioners and researchers, as well as health service managers, commissioners, and research funders.
METHODS: This review utilises Arksey and O\'Malley\'s methodological framework for scoping reviews and draws on the recommended enhancements of this framework to promote a team-based and mixed methods approach. This includes searching electronic databases and screening papers based on a pre-specified inclusion and exclusion criteria. Data relevant to the research aims will be extracted from included papers, including the clinical/professional area of the topic, the source of data that was used, and whether it addresses elements of social justice. All screening and reviewing will be collaborative and iterative, drawing on strengths of the research team and responsive changes to challenges will be made. Quantitative data will be analysed descriptively, and conceptual content analysis will be utilised to understand qualitative data. These will be collectively synthesised in alignment to the research aims.
CONCLUSIONS: Our findings will highlight the extent to which such research is being conducted and published, including gaps and make recommendations for future endeavours for real-world data studies. The findings from this scoping review will be relevant for practitioners and researchers, as well as health service managers, commissioners, and research funders.
摘要：
背景：许多医疗保健领域受到缺乏可转化为临床实践的研究的影响。利用现实世界数据的研究，例如常规收集的患者数据，可能是有效创建证据以告知实践和服务交付的一种选择。这些研究对于探索服务和成果的公平性也很有价值，并受益于使用代表“现实世界”中服务的种群多样性的非选择样本。本范围审查旨在识别和绘制已发表的利用常规收集的临床医疗保健数据的研究。次要目的是探索这些文献在多大程度上支持在健康中追求社会正义，包括健康不平等和交叉方法。
方法：本综述利用Arksey和O\'Malley\的方法框架进行范围审查，并借鉴了该框架的建议增强功能，以促进基于团队和混合方法的方法。这包括根据预先指定的纳入和排除标准搜索电子数据库和筛选文件。与研究目标相关的数据将从包含的论文中提取，包括该主题的临床/专业领域，使用的数据源，以及它是否涉及社会正义的要素。所有的筛选和审查将是协作和迭代的，将利用研究团队的优势并应对挑战。定量数据将进行描述性分析，概念内容分析将用于理解定性数据。这些将根据研究目标进行集体综合。
结论：我们的研究结果将强调进行和发表此类研究的程度，包括差距，并为现实世界数据研究的未来努力提出建议。这次范围审查的结果将与从业人员和研究人员相关，以及卫生服务经理，专员,和研究资助者。
方法：本综述利用Arksey和O\'Malley\的方法框架进行范围审查，并借鉴了该框架的建议增强功能，以促进基于团队和混合方法的方法。这包括根据预先指定的纳入和排除标准搜索电子数据库和筛选文件。与研究目标相关的数据将从包含的论文中提取，包括该主题的临床/专业领域，使用的数据源，以及它是否涉及社会正义的要素。所有的筛选和审查将是协作和迭代的，将利用研究团队的优势并应对挑战。定量数据将进行描述性分析，概念内容分析将用于理解定性数据。这些将根据研究目标进行集体综合。
结论：我们的研究结果将强调进行和发表此类研究的程度，包括差距，并为现实世界数据研究的未来努力提出建议。这次范围审查的结果将与从业人员和研究人员相关，以及卫生服务经理，专员,和研究资助者。
