The impact of data quality assurance and control solutions on the completeness, accuracy, and consistency of data in a national spinal cord injury registry of Iran (NSCIR-IR)

Abstract

Study design: Descriptive study. Objective: This study aimed to develop and evaluate a systematic arrangement for improvement and monitoring of data quality of the National Spinal Cord (and Column) Injury Registry of Iran (NSCIR-IR)—a multicenter hospital-based registry. Setting: SCI community in Iran. Methods: Quality assurance and quality control were the primary objectives in improving overall quality of data that were considered in designing a paper-based and computerized case report. To prevent incorrect data entry, we implemented several validation algorithms, including 70 semantic rules, 18 syntactic rules, seven temporal rules, and 13 rules for acceptable value range. Qualified and trained staff members were also employed to review and identify any defect, inaccuracy, or inconsistency in the data to improve data quality. A set of functions were implemented in the software to cross-validate, and feedback on data was provided by reviewers and registrars. Results: Socio-demographic data items were 100% complete, except for national ID and education level, which were 97% and 92.3% complete, respectively. Completeness of admission data and emergency medical services data were 100% except for arrival and transfer time (99.4%) and oxygen saturation (48.9%). Evaluation of data received from two centers located in Tehran proved to be 100% accurate following validation by quality reviewers. All data was also found to be 100% consistent. Conclusions: This approach to quality assurance and consistency validation proved to be effective. Our solutions resulted in a significant decrease in the number of missing data.