Optimising data quality in a national clinical quality registry: Insights from the Australian Stroke Clinical Registry

Abstract

Background: Clinical Quality Registries (CQRs) capture clinical practice data to monitor the performance of health services against agreed standards of care. Ensuring data timeliness, completeness and reliability are challenges for CQRs, as data are prospectively collected and time sensitive. The Australian Stroke Clinical Registry (AuSCR) commenced in 2009 and includes 67 hospitals voluntarily collecting data on patients with acute stroke (at December 2024). Objective: To describe the methods used to ensure data quality in a national CQR, using the AuSCR as a case study. Method: Methods from the AuSCR were described against The Australian Framework for CQRs (2024), focusing on three operating principles for data quality: “Data collection,” “Data elements” and “Ensuring data quality.” Results: The AuSCR meets these principles through: (1) an online data platform to import data from primary sources and perform logic checks; (2) provision of comprehensive training, a data dictionary and user manuals for contributors; (3) medical record audits; (4) bi-annual hospital data quality reports and near real-time dashboards including data discrepancies; (5) cross-referencing data against government admissions data. Our processes extend to patient-reported follow-up data collected within 90–180 days of admission. Conclusion: Managing health information in a national CQR involves multiple methods to ensure data quality and minimise clinician data entry time. The AuSCR is an exemplar program to guide the field. Implications for health information management practice: CQRs are rapidly adopting streamlined processes to collect, manage and validate data to maximise the quality of health information for clinical practice improvement.