A Review of Data Quality Assessment Methods for Public Health Information Systems

International Journal of Environmental Research and Public Health

Hong Chen²  David Hailey²  Ning Wang¹ 

[1]National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China

[2] E-Mail:

[3]School of Information Systems and Technology, Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, NSW, 2522, Australia

[4] E-Mails:

关键词: data quality;    information quality;    data use;    data collection process;    evaluation;    assessment;    public health;    population health;    information systems;   

DOI  :  10.3390/ijerph110505170

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.