Health Information and Quality Authority (HIQA), Cork, Ireland.
The SFI ADAPT Research Centre for AI-Driven Digital Content Technology, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland.
J Am Med Inform Assoc. 2022 Apr 13;29(5):944-952. doi: 10.1093/jamia/ocac017.
The purpose of this study was to develop a framework to assess the quality of healthcare data sources.
First, a systematic review was performed and a thematic analysis of included literature conducted to identify items relating to the quality of healthcare data sources. Second, expert advisory group meetings were held to explore experts' perception of the results of the review and identify gaps in the findings. Third, a framework was developed based on the findings.
Synthesis of the review results and expert advisory group meetings resulted in 8 parent themes and 22 subthemes. The parent themes were: Governance, leadership, and management; Data; Trust; Context; Monitoring; Use of information; Standardization; Learning and training. The 22 subthemes were: governance, finance, organization, characteristics, time, data management, data quality, ethics, access, security, quality improvement, monitoring and feedback, dissemination, analysis, research, standards, linkage, infrastructure, documentation, definitions and classification, learning, and training.
The herein presented framework was developed using a robust methodology which included reviewing literature and extracting data source quality items, filtering, and matching items, developing a list of themes, and revising them based on expert opinion. To the best of our knowledge, this study is the first to apply a systematic approach to identify aspects related to the quality of healthcare data sources.
The framework, can assist those using healthcare data sources to identify and assess the quality of a data source and inform whether the data sources used are fit for their intended use.
本研究旨在制定一个评估医疗数据资源质量的框架。
首先，进行了系统评价，并对纳入文献进行了主题分析，以确定与医疗数据资源质量相关的项目。其次，召开了专家咨询小组会议，以探讨专家对审查结果的看法，并确定研究结果中的差距。第三，根据研究结果制定了一个框架。
审查结果和专家咨询小组会议的综合结果产生了 8 个母主题和 22 个子主题。母主题是：治理、领导和管理；数据；信任；背景；监测；信息使用；标准化；学习和培训。22 个子主题是：治理、财务、组织、特征、时间、数据管理、数据质量、道德、访问、安全、质量改进、监测和反馈、传播、分析、研究、标准、链接、基础设施、文档、定义和分类、学习和培训。
本框架采用了一种稳健的方法制定，包括审查文献和提取数据源质量项目、筛选和匹配项目、开发主题列表，并根据专家意见进行修订。据我们所知，这项研究是首次应用系统方法来确定与医疗保健数据资源质量相关的方面。
该框架可以帮助那些使用医疗保健数据资源的人识别和评估数据源的质量，并告知他们使用的数据源是否适合其预期用途。
