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A Review of Data Quality Assessment in Emergency Medical Services

机译:紧急医疗服务中数据质量评估的回顾

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Introduction:Data quality is an important issue in emergency medicine. The unique characteristics of emergency care services, such as high turn-over and the speed of work may increase the possibility of making errors in the related settings. Therefore, regular data quality assessment is necessary to avoid the consequences of low quality data. This study aimed to identify the main dimensions of data quality which had been assessed, the assessment approaches, and generally, the status of data quality in the emergency medical services.Methods:The review was conducted in 2016. Related articles were identified by searching databases, including Scopus, Science Direct, PubMed and Web of Science. All of the review and research papers related to data quality assessment in the emergency care services and published between 2000 and 2015 (n=34) were included in the study.Results:The findings showed that the five dimensions of data quality; namely, data completeness, accuracy, consistency, accessibility, and timeliness had been investigated in the field of emergency medical services. Regarding the assessment methods, quantitative research methods were used more than the qualitative or the mixed methods. Overall, the results of these studies showed that data completeness and data accuracy requires more attention to be improved.Conclusion:In the future studies, choosing a clear and a consistent definition of data quality is required. Moreover, the use of qualitative research methods or the mixed methods is suggested, as data users’ perspectives can provide a broader picture of the reasons for poor quality data.
机译:简介:数据质量是急诊医学中的重要问题。急诊服务的独特特征,例如高周转率和工作速度,可能会增加在相关设置中出错的可能性。因此,必须进行定期的数据质量评估,以避免产生质量低劣的数据。这项研究旨在确定已评估数据质量的主要方面,评估方法以及一般情况下紧急医疗服务中数据质量的状况。方法:该研究于2016年进行。相关文章通过搜索数据库进行识别,包括Scopus,Science Direct,PubMed和Web of Science。研究包括2000年至2015年间发表的与急救服务中数据质量评估有关的所有评论和研究论文(n = 34)。即,在紧急医疗服务领域已经研究了数据的完整性,准确性,一致性,可访问性和及时性。关于评估方法,定量研究方法比定性或混合方法更多。总体而言,这些研究的结果表明,数据完整性和数据准确性需要得到更多的关注。结论:在未来的研究中,需要选择一个清晰,一致的数据质量定义。此外,建议使用定性研究方法或混合方法,因为数据用户的观点可以更全面地了解数据质量差的原因。

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