Development and validation of a rule-based tool for quality management reporting in a genetics laboratory

Introduction: Quality management systems are essential in clinical laboratories to ensure optimal operational output. However, report generation still frequently relies on manual processes which are time-consuming and prone to errors. Methods: A rule-based artificial intelligence tool was internally developed to automate quality management report generation by directly extracting and processing electronic laboratory records from the health information system using pre-defined formulas and logic. Results: Implementation of this tool in a Medical Genetics laboratory reduced report preparation time by 90% and eliminated discrepancies compared to manual reports, alleviating the need for extensive secondary reviews. Conclusion: This AI-assisted approach streamlines quality management reporting, enhancing efficiency and data consistency. The successful development and implementation of those tools require continuous communication and validation between the different stakeholders for effective system refinement.