Data Quality Assessment and Recommendations to Improve the Quality of Hemodialysis Database
Journal Title: Journal of Health Management and Informatics - Year 2018, Vol 5, Issue 1
Abstract
Introduction: Since clinical data contain abnormalities, quality assessment and reporting of data errors are necessary. Data quality analysis consists of developing strategies, making recommendations to avoid future errors and improving the quality of data entry by identifying error types and their causes. Therefore, this approach can be extremely useful to improve the quality of the databases. The aim of this study was to analyze hemodialysis (HD) patients’ data in order to improve the quality of data entry and avoid future errors. Method: The study was done on Shiraz University of Medical Sciences HD database in 2015. The database consists of 2367 patients who had at least 12 months follow up (22.34±11.52 months) in 2012-2014. Duplicated data were removed; outliers were detected based on statistical methods, expert opinion and the relationship between variables; then, the missing values were handled in 72 variables by using IBM SPSS Statistics 22 in order to improve the quality of the database. According to the results, some recommendations were given to improve the data entry process. Results: The variables had outliers in the range of 0-9.28 percent. Seven variables had missing values over 20 percent and in the others they were between 0 and 19.73 percent. The majority of missing values belong to serum alkaline phosphatase, uric acid, high and low density lipoprotein, total iron binding capacity, hepatitis B surface antibody titer, and parathyroid hormone. The variables with displacement (the values of two or more variables were recorded in the wrong attribute) were weight, serum creatinine, blood urea nitrogen, systolic and diastolic blood pressure. These variables may lead to decreased data quality. Conclusion: According to the results and expert opinion, applying some data entry principles, such as defining ranges of values, using the relationship between hemodialysis features, developing alert systems about empty or duplicated data and entering directly HD data or lab results into the database can improve the data quality drastically. Experts' opinion in detecting outliers as a complement to statistical methods can have an effective role in detection of real outliers. For the analysis of HD databases, the relationship between the variables because of their effect on the quality should be focused more to improve the quality of the database.
Authors and Affiliations
Neda Firouraghi, Shahrokh Ezzatzadegan Jahromi, Ashkan Sami, Mohamad Reza Morvaridi, Roxana Sharifian
Keywords
Related Articles
- EP ID EP305154
- DOI -
- Views 130
- Downloads 0
The Prevalence of Ta’zir Medical Offences before and after the Implementation of Healthcare Reform Program (2013-2016): The Case of Shiraz University of Medical Sciences
Introduction: There are different problem-solving courts for prosecuting medical offences due to the broadness of healthcare sector as well as the variety of offences in this sector. One of these courts in Iran is the Co...
National registry of myocardial infarction
The Registry of Myocardial Infarctions (MI Registry) is a national registry in Iran that collects and reports the data on myocardial infarctions. Its main advantage is that it covers the whole country and is mandatory fo...
The relationship between budget emphasis, budget planning models and performance
Introduction: In order to provide a new approach about the budget planning models, this study examined the relationship between budget emphasis, budget planning models and performance of Fars Province University of Medic...
Founder’s Syndrome and Firm Performance of Small and Medium Scale Enterprises in Nigeria
Introduction: Founder’s syndromes have become a significant issue in SMEs performance. This study examined the impact of founder’s syndrome on firm performance of small and medium scale enterprises in Nigeria. Method: Th...
Adjustment and Development of Health User’s Mental Model Completeness Scale in Search Engines
Introduction: Users’ performance and their interaction with information retrieval systems can be observed in development of their mental models. Users, especially users of health, use mental models to facilitate their in...