A Review of Data Quality Assessment in Emergency Medical Services.
A Review of Data Quality Assessment in Emergency Medical Services.
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DOI：
10.2174/1874431101812010019
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发表时间：
2018-01-01
期刊：
影响因子：
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通讯作者：
Khorasani-Zavareh, Davoud
中科院分区：
文献类型：
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作者：
Mashoufi, Mehrnaz;Ayatollahi, Haleh;Khorasani-Zavareh, Davoud
简介：数据质量是急诊医学中的一个重要问题。紧急护理服务的独特特征，例如人员流动率高和工作速度快，可能会增加相关设置中出错的可能性。因此，有必要定期进行数据质量评估，以避免低质量数据带来的后果。本研究旨在明确急诊医疗服务中数据质量评估的主要维度、评估方法以及总体数据质量状况。方法：于2016年进行综述，通过检索Scopus、Science Direct、PubMed和Web of Science等数据库找到相关文章。本研究纳入了2000年至2015年间发表的所有与急诊服务数据质量评估相关的综述和研究论文（n=34）。 结果：研究结果表明，数据质量的五个维度：即对应急医疗服务领域数据的完整性、准确性、一致性、可获取性和及时性进行了调查。在评估方法上，定量研究方法的使用较多，定性或混合方法较多。总的来说，这些研究的结果表明，数据完整性和数据准确性需要更多的关注来提高。结论：在未来的研究中，需要选择一个清晰且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广泛地了解数据质量差的原因。
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.