确定数据收集过程质量的基本组成部分是为公共卫生信息系统设计有效的数据质量管理策略的起点。对公共卫生数据收集过程质量的全球文献进行归纳分析,形成了初步的4D组件框架,也就是说,数据收集管理,数据收集人员,数据采集系统,和数据收集环境。有必要对该框架进行实证验证,以便在未来的研究和实践中使用。
本研究旨在获得经验证据,以确认框架的组成部分,如果需要,进一步发展这一框架。
在中国国家艾滋病毒/艾滋病综合反应信息管理系统的背景下,使用专家启发来评估初步框架。研究过程包括制定采访指南和数据收集表格,数据收集,和分析。共有3名公共卫生管理人员,15名公共卫生工作者,10名卫生保健从业人员参加了启发会议。遵循框架定性数据分析方法和定量比较分析,从访谈笔录中引出主题,并将其映射到初步4D框架的要素。
从访谈记录中提取了总共302个代码。经过迭代和递归比较,分类,和映射,出现了46个新指标;由于缺乏证据支持,删除了24.8%(37/149)的原始指标,并合并了另外28.2%(42/149)。经过验证的4D组件框架由116个指标(82个促进者和34个障碍)组成。第一部分,数据收集管理,包括数据收集协议和质量保证。用41个指标来衡量,从原来的49%(73/149)下降到35.3%(41/116)。第二部分,数据收集环境,用37个指标衡量,从原来的13.4%(20/149)上升至31.9%(37/116)。它包括领导,培训,资金,组织政策,高层管理支持,以及平行组织之间的合作。第三部分,数据收集人员,包括对数据收集的感知,技能和能力,通信,和人员配备模式。数据收集人员的比例没有变化(19.5%对19.0%),尽管其指标数量从29个减少到22个。第四部分,数据收集系统,使用16个指标进行测量,率略有下降,从18.1%(27/149)降至13.8%(16/116)。它包括功能,系统集成,技术支持,和数据收集设备。
这项专家启发研究验证并改进了4D框架。该框架可用于开发问卷调查工具,以在验证心理测量属性和项目减少后测量公共卫生数据收集过程的质量。
Identification of the essential components of the quality of the data collection process is the starting point for designing effective data quality management strategies for public health information systems. An inductive analysis of the global literature on the quality of the public health data collection process has led to the formation of a preliminary 4D component framework, that is, data collection management, data collection personnel, data collection system, and data collection environment. It is necessary to empirically validate the framework for its use in future research and practice.
This study aims to obtain empirical evidence to confirm the components of the framework and, if needed, to further develop this framework.
Expert elicitation was used to evaluate the preliminary framework in the context of the Chinese National HIV/AIDS Comprehensive Response Information Management System. The research processes included the development of an interview guide and data collection form, data collection, and analysis. A total of 3 public health administrators, 15 public health workers, and 10 health care practitioners participated in the elicitation session. A framework qualitative data analysis approach and a quantitative comparative analysis were followed to elicit themes from the interview transcripts and to map them to the elements of the preliminary 4D framework.
A total of 302 codes were extracted from interview transcripts. After iterative and recursive comparison, classification, and mapping, 46 new indicators emerged; 24.8% (37/149) of the original indicators were deleted because of a lack of evidence support and another 28.2% (42/149) were merged. The validated 4D component framework consists of 116 indicators (82 facilitators and 34 barriers). The first component, data collection management, includes data collection protocols and quality assurance. It was measured by 41 indicators, decreased from the original 49% (73/149) to 35.3% (41/116). The second component, data collection environment, was measured by 37 indicators, increased from the original 13.4% (20/149) to 31.9% (37/116). It comprised leadership, training, funding, organizational policy, high-level management support, and collaboration among parallel organizations. The third component, data collection personnel, includes the perception of data collection, skills and competence, communication, and staffing patterns. There was no change in the proportion for data collection personnel (19.5% vs 19.0%), although the number of its indicators was reduced from 29 to 22. The fourth component, the data collection system, was measured using 16 indicators, with a slight decrease in percentage points from 18.1% (27/149) to 13.8% (16/116). It comprised functions, system integration, technical support, and data collection devices.
This expert elicitation study validated and improved the 4D framework. The framework can be useful in developing a questionnaire survey instrument for measuring the quality of the public health data collection process after validation of psychometric properties and item reduction.