Skip to main content
placeholder image

A review of data quality assessment methods for public health information systems

Journal Article


Download full-text (Open Access)

Abstract


  • High quality data and effective data quality assessment are required for accurately evaluating the impact of public health interventions and measuring public health outcomes. Data, data use, and data collection process, as the three dimensions of data quality, all need to be assessed for overall data quality assessment. We reviewed current data quality assessment methods. The relevant study was identified in major databases and well-known institutional websites. We found the dimension of data was most frequently assessed. Completeness, accuracy, and timeliness were the three most-used attributes among a total of 49 attributes of data quality. The major quantitative assessment methods were descriptive surveys and data audits, whereas the common qualitative assessment methods were interview and documentation review. The limitations of the reviewed studies included inattentiveness to data use and data collection process, inconsistency in the definition of attributes of data quality, failure to address data users’ concerns and a lack of systematic procedures in data quality assessment. This review study is limited by the coverage of the databases and the breadth of public health information systems. Further research could develop consistent data quality definitions and attributes. More research efforts should be given to assess the quality of data use and the quality of data collection process.

Publication Date


  • 2014

Citation


  • Chen, H., Hailey, D., Wang, N. & Yu, P. (2014). A review of data quality assessment methods for public health information systems. International Journal of Environmental Research and Public Health, 11 (5), 5170-5207.

Scopus Eid


  • 2-s2.0-84900790703

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=3266&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/2257

Number Of Pages


  • 37

Start Page


  • 5170

End Page


  • 5207

Volume


  • 11

Issue


  • 5

Abstract


  • High quality data and effective data quality assessment are required for accurately evaluating the impact of public health interventions and measuring public health outcomes. Data, data use, and data collection process, as the three dimensions of data quality, all need to be assessed for overall data quality assessment. We reviewed current data quality assessment methods. The relevant study was identified in major databases and well-known institutional websites. We found the dimension of data was most frequently assessed. Completeness, accuracy, and timeliness were the three most-used attributes among a total of 49 attributes of data quality. The major quantitative assessment methods were descriptive surveys and data audits, whereas the common qualitative assessment methods were interview and documentation review. The limitations of the reviewed studies included inattentiveness to data use and data collection process, inconsistency in the definition of attributes of data quality, failure to address data users’ concerns and a lack of systematic procedures in data quality assessment. This review study is limited by the coverage of the databases and the breadth of public health information systems. Further research could develop consistent data quality definitions and attributes. More research efforts should be given to assess the quality of data use and the quality of data collection process.

Publication Date


  • 2014

Citation


  • Chen, H., Hailey, D., Wang, N. & Yu, P. (2014). A review of data quality assessment methods for public health information systems. International Journal of Environmental Research and Public Health, 11 (5), 5170-5207.

Scopus Eid


  • 2-s2.0-84900790703

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=3266&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/2257

Number Of Pages


  • 37

Start Page


  • 5170

End Page


  • 5207

Volume


  • 11

Issue


  • 5