ABSTRACT
The quality of data in electronic healthcare databases is a critical component when used for research and health practice. The aim of the present study was to assess the data quality in the Paulista Cardiovascular Surgery Registry II (REPLICCAR II) using two different audit methods, direct and indirect. The REPLICCAR II database contains data from 9 hospitals in São Paulo State with over 700 variables for 2229 surgical patients. The data collection was performed in REDCap platform using trained data managers to abstract information. We directly audited a random sample (n = 107) of the data collected after 6 months and indirectly audited the entire sample after 1 year of data collection. The indirect audit was performed using the data management tools in REDCap platform. We computed a modified Aggregate Data Quality Score (ADQ) previously reported by Salati et al. (2015). The agreement between data elements was good for categorical data (Cohen κ = 0.7, 95%CI = 0.59-0.83). For continuous data, the intraclass coefficient (ICC) for only 2 out of 15 continuous variables had an ICC < 0.9. In the indirect audit, 77% of the selected variables (n = 23) had a good ADQ score for completeness and accuracy. Data entry in the REPLICCAR II database proved to be satisfactory and showed competence and reliable data for research in cardiovascular surgery in Brazil.
Subject(s)
Databases, Factual , Brazil , Cardiovascular Surgical Procedures , Data Accuracy , Humans , RegistriesABSTRACT
The quality of data in electronic healthcare databases is a critical component when used for research and health practice. The aim of the present study was to assess the data quality in the Paulista Cardiovascular Surgery Registry II (REPLICCAR II) using two different audit methods, direct and indirect. The REPLICCAR II database contains data from 9 hospitals in São Paulo State with over 700 variables for 2229 surgical patients. The data collection was performed in REDCap platform using trained data managers to abstract information. We directly audited a random sample (n = 107) of the data collected after 6 months and indirectly audited the entire sample after 1 year of data collection. The indirect audit was performed using the data management tools in REDCap platform. We computed a modified Aggregate Data Quality Score (ADQ) previously reported by Salati et al. (2015). The agreement between data elements was good for categorical data (Cohen κ = 0.7, 95%CI = 0.59-0.83). For continuous data, the intraclass coefficient (ICC) for only 2 out of 15 continuous variables had an ICC < 0.9. In the indirect audit, 77% of the selected variables (n = 23) had a good ADQ score for completeness and accuracy. Data entry in the REPLICCAR II database proved to be satisfactory and showed competence and reliable data for research in cardiovascular surgery in Brazil.