A Review of Data Quality Assessment Methods for Public Health Information Systems
By Hong Chen, D. Hailey, Ning Wang, R. Yu. International Journal of Environmental Research and Public Health, 2014
303 citations recorded by the source corpus.
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.
Subfields: Epidemiology, Management Science and Operations Research, Public Health, Environmental and Occupational Health
View Paper: https://www.semanticscholar.org/paper/efba03144444f52b6141a17e3772aadabe0042f0
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Citations in the corpus
- Engaging Solidarity in Data Collection Practices for Community Health (2018, background): “These include accuracy, timeliness, reliability, completeness, and integrity of data collectors [16].”