The Open Medical Informatics Journal
2018, 12 : 19-32Published online 2018 May 31. DOI: 10.2174/1874431101812010019
Publisher ID: TOMINFOJ-12-19
REVIEW ARTICLE
A Review of Data Quality Assessment in Emergency Medical Services
2 School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
3 Safety Promotion and Injury Prevention Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4 Department of Health in Disaster and Emergency, School of HSE, Shahid Beheshti University of Medical Sciences, Tehran, Iran
* Address correspondence to this author at the School of Health Management and Information Sciences, Iran University of Medical Sciences, No.6, Vali-e-Asr St., Yasami St., Tehran, Iran; Tel: +982188794301; E-mail: ayatollahi.h@iums.ac.ir
ABSTRACT
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.