Common data quality elements for health information systems: a systematic review

被引:0
作者
Ghalavand, Hossein [1 ]
Shirshahi, Saied [2 ]
Rahimi, Alireza [2 ]
Zarrinabadi, Zarrin [1 ]
Amani, Fatemeh [3 ]
机构
[1] Abadan Univ Med Sci, Dept Med Lib & Informat Sci, Abadan, Iran
[2] Isfahan Univ Med Sci, Sch Hlth Management & Informat Sci, Dept Med Lib & Informat Sci, Esfahan, Iran
[3] Abadan Univ Med Sci, Student Res Comm, Abadan, Iran
关键词
Data quality; Health Information System; Systematic review; RECORD SYSTEM; MANAGEMENT; DIMENSIONS;
D O I
10.1186/s12911-024-02644-7
中图分类号
R-058 [];
学科分类号
摘要
BackgroundData 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.MethodsA 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.ResultsCurrent 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.ConclusionsAt 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.
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