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

Hong Chen, David Hailey, Ning Wang and Ping Yu
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Hong Chen: School of Information Systems and Technology, Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, NSW, 2522, Australia
David Hailey: School of Information Systems and Technology, Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, NSW, 2522, Australia
Ning Wang: National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
Ping Yu: School of Information Systems and Technology, Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, NSW, 2522, Australia

IJERPH, 2014, vol. 11, issue 5, 1-38

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.

Keywords: data quality; information quality; data use; data collection process; evaluation; assessment; public health; population health; information systems (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2014
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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