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Common Data Quality Elements for Health Information Systems: A Systematic Review Publisher Pubmed



Ghalavand H1 ; Shirshahi S2 ; Rahimi A2 ; Zarrinabadi Z1 ; Amani F3
Authors
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Authors Affiliations
  1. 1. Department of Medical library and Information Science, Abadan University of Medical Sciences, Abadan, Iran
  2. 2. Department of Medical library and Information Science, School of Health Management and Information Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
  3. 3. Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran

Source: BMC Medical Informatics and Decision Making Published:2024


Abstract

Background: Data quality in health information systems has a complex structure and consists of several dimensions. This research conducted for identify Common data quality elements for health information systems. Methods: A literature review was conducted and search strategies run in Web of Knowledge, Science Direct, Emerald, PubMed, Scopus and Google Scholar search engine as an additional source for tracing references. We found 760 papers, excluded 314 duplicates, 339 on abstract review and 167 on full-text review; leaving 58 papers for critical appraisal. Results: Current review shown that 14 criteria are categorized as the main dimensions for data quality for health information system include: Accuracy, Consistency, Security, Timeliness, Completeness, Reliability, Accessibility, Objectivity, Relevancy, Understandability, Navigation, Reputation, Efficiency and Value- added. Accuracy, Completeness, and Timeliness, were the three most-used dimensions in literature. Conclusions: At present, there is a lack of uniformity and potential applicability in the dimensions employed to evaluate the data quality of health information system. Typically, different approaches (qualitative, quantitative and mixed methods) were utilized to evaluate data quality for health information system in the publications that were reviewed. Consequently, due to the inconsistency in defining dimensions and assessing methods, it became imperative to categorize the dimensions of data quality into a limited set of primary dimensions. © The Author(s) 2024.
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