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  1. Zaki R, Bulgiba A, Ismail R, Ismail NA
    PLoS One, 2012;7(5):e37908.
    PMID: 22662248 DOI: 10.1371/journal.pone.0037908
    Accurate values are a must in medicine. An important parameter in determining the quality of a medical instrument is agreement with a gold standard. Various statistical methods have been used to test for agreement. Some of these methods have been shown to be inappropriate. This can result in misleading conclusions about the validity of an instrument. The Bland-Altman method is the most popular method judging by the many citations of the article proposing this method. However, the number of citations does not necessarily mean that this method has been applied in agreement research. No previous study has been conducted to look into this. This is the first systematic review to identify statistical methods used to test for agreement of medical instruments. The proportion of various statistical methods found in this review will also reflect the proportion of medical instruments that have been validated using those particular methods in current clinical practice.
    Matched MeSH terms: Diagnostic Techniques and Procedures/standards
  2. Yusof M, Sahroni MN
    Int J Health Care Qual Assur, 2018 Oct 08;31(8):1014-1029.
    PMID: 30415623 DOI: 10.1108/IJHCQA-07-2017-0125
    PURPOSE: The purpose of this paper is to present a review of health information system (HIS)-induced errors and its management. This paper concludes that the occurrence of errors is inevitable but it can be minimised with preventive measures. The review of classifications can be used to evaluate medical errors related to HISs using a socio-technical approach. The evaluation could provide an understanding of errors as a learning process in managing medical errors.

    DESIGN/METHODOLOGY/APPROACH: A literature review was performed on issues, sources, management and approaches to HISs-induced errors. A critical review of selected models was performed in order to identify medical error dimensions and elements based on human, process, technology and organisation factors.

    FINDINGS: Various error classifications have resulted in the difficulty to understand the overall error incidents. Most classifications are based on clinical processes and settings. Medical errors are attributed to human, process, technology and organisation factors that influenced and need to be aligned with each other. Although most medical errors are caused by humans, they also originate from other latent factors such as poor system design and training. Existing evaluation models emphasise different aspects of medical errors and could be combined into a comprehensive evaluation model.

    RESEARCH LIMITATIONS/IMPLICATIONS: Overview of the issues and discourses in HIS-induced errors could divulge its complexity and enable its causal analysis.

    PRACTICAL IMPLICATIONS: This paper helps in understanding various types of HIS-induced errors and promising prevention and management approaches that call for further studies and improvement leading to good practices that help prevent medical errors.

    ORIGINALITY/VALUE: Classification of HIS-induced errors and its management, which incorporates a socio-technical and multi-disciplinary approach, could guide researchers and practitioners to conduct a holistic and systematic evaluation.

    Matched MeSH terms: Diagnostic Techniques and Procedures/standards
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