Data Quality Improvement and Internal Data Audit of the Chinese Neonatal Network Data Collection System

Background The Chinese Neonatal Network (CHNN) is a nationwide neonatal network that aims to improve clinical neonatal care quality and short and long-term health outcomes of infants. This study aims to assess the quality of the Chinese Neonatal Network database by conducting an internal audit of data extraction. Methods A data audit was performed by independently replicating the data collection and entry process in all 58 tertiary neonatal intensive care units (NICU) participating in the CHNN. Eighty-eight data elements selected for re-abstraction were classified into 3 categories (critical, important, less important) and agreement rates for original and re-abstracted data were predefined. Three to five records were randomly selected at each site for re-abstraction, including one short (0–7 days), two medium (8–28 days), and two long (more than 28 days) stay cases. Agreement rates for each data item were calculated for individual NICUs and across the network, respectively. Results A total of 283 cases and 24,904 data fields were re-abstracted. The agreement rates for original and re-abstracted data elements were 96.1% overall, and 97.2%, 94.3%, and 96.6% for critical, important, and less important data elements respectively. Individual site variation for discrepancies ranged between 0.0% and 18.4% for all collected data elements. Conclusion The completeness, precision and quality of data in the CHNN database are high, providing assurance for multi-purpose use, including health service evaluation, quality improvement, clinical trials and other research.