The development and design of a highly discerning platform for data capture in a neonatal encephalopathy study

Abstract

Background: NESHIE is a clinical condition defined by a restricted supply of oxygen and blood flow around the time of labour and delivery. Some neonates diagnosed with moderate and severe cases die in the infantile stage while surviving neonates may develop neurodevelopmental disorders, with cerebral palsy being one of the most adverse outcomes. REDCap, an electronic data capture software, is utilised in the NESHIE study. However, database users are prone to data capturing errors. Therefore, we undertook a redesign of an existing REDCap database to reduce data capturing errors. The aims were to design and develop a national-level database capable of capturing and storing multiple data points from several sites. In addition, it will evaluate the data capture process to refine data quality assessments and enhance overall data accuracy, ensuring the database meets the needs of diverse users across all study sites. Method: Clinical data captured in REDCap was compared with corresponding case report forms to identify data discrepancies across all study sites. Key areas responsible for data discrepancies were identified; built-in and advanced REDCap features were leveraged to refine the database. To assess the impact of the adjustments, comparative analyses were conducted using Chi-squared analysis in R to compare variance across the original and adjusted databases. Post-hoc analysis with Bonferroni correction was also performed. Results and Discussion: Prior to implementing the refined database, error rates ranged between 6-20% (n=77). However, in the refined database across all sites (n=83), error rates were observed at 1-11%. These errors were mainly attributed to missing or unavailable data, as well as misinterpretation of case report forms. There was a noted significant difference in comparisons between the early amendments (A3 and A4) and the refined database (A7; p<0.05). showing a significant reduction in errors from the first database compared to the refined database. Conclusion: By leveraging multiple in-built features of REDCap, the aesthetics of the database were improved, and user error rates reduced. The enhancements ensure continued efficient usability, while simultaneously promoting the maintenance of high-quality data.