急诊医疗服务数据质量评估综述

The open medical informatics journal Pub Date : 2018-05-31 eCollection Date: 2018-01-01 DOI:10.2174/1874431101812010019
Mehrnaz Mashoufi, Haleh Ayatollahi, Davoud Khorasani-Zavareh
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引用次数: 20

摘要

数据质量是急诊医学的一个重要问题。紧急护理服务的独特特点,如高周转率和工作速度,可能会增加在相关设置中犯错误的可能性。因此,定期进行数据质量评估是必要的,以避免数据质量低的后果。本研究旨在确定已评估的数据质量的主要方面、评估方法以及急诊医疗服务中数据质量的总体状况。方法:回顾于2016年进行。通过检索Scopus、Science Direct、PubMed和Web of Science等数据库确定相关文章。2000年至2015年间发表的所有与急诊护理服务数据质量评估相关的综述和研究论文(n=34)被纳入本研究。结果:研究结果表明:数据质量的五个维度;即,在紧急医疗服务领域调查了数据的完整性、准确性、一致性、可及性和及时性。在评价方法上,多采用定量研究方法,而不是定性或混合方法。总的来说,这些研究的结果表明,数据的完整性和数据的准确性需要更多的关注和提高。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广泛地了解数据质量差的原因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A Review of Data Quality Assessment in Emergency Medical Services.

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.

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