JMIR Publications

ABSTRACT

Background:

Identification of the essential components of quality of data collection is the starting point for the design of effective data quality management strategies in public health information systems. An inductive analysis of global public health informatics literature on the data collection process derived a four-dimensional (4D) component framework that focuses on four dimensions of the process: management, personnel, data collection system, and environment. It is necessary to empirically validate the framework for further research and practice.

Objective:

This study aimed to obtain empirical evidence to confirm the components of the 4D framework, and if needed, to further develop this preliminary framework.

Methods:

Expert elicitation was used to evaluate the preliminary framework in the context of Chinese national AIDS information management system. The research processes included the development of an interview guide and data collection form, data collection, and data analysis. Twenty-eight experts, including three public health administrators, fifteen public health work-ers, and ten healthcare practitioners participated in the elicitation session. A framework quali-tative data analysis approach was followed to elicit themes from interview transcripts and to compare with the elements of the 4D framework.

Results:

A total of 302 codes were extracted from the interview transcripts, which verified 116 (78%) original indicators and generated 47 new indicators. The final 4D component framework consists of 116 indicators including 82 facilitators and 34 barriers. The first component, data collection management, includes data collection protocol and quality assurance, which is measured by 41 (35% of the 116) indicators. It was followed by data collection environment measured by 37 (32%) indicators, which comprises leadership, training, and funding, as well as three newly added subcomponents, i.e., organisational policy, high-level management support, collaboration among parallel organisations. The third component, data collection personnel, is described by a perception of data collection, skill/competence, communication, and staffing pattern, which is measured by 22 (19%) indicators. The fourth, data collection system, contain-ing functions, integration of different data collection systems, technical support, and device for data collection, is measured by 16 (14%) indicators.

Conclusions:

This expert elicitation study situated in national AIDS information management systems validated and made improvements to the 4D component framework measuring the quality of the data collection process for public health information systems. The validated 4D component framework can be applied by researchers and practitioners in designing and managing the public health data collection process.