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

Area of Science:

• Clinical Laboratory Science
• Health Informatics
• Artificial Intelligence in Healthcare

Background:

• Quality management systems are crucial for clinical laboratory operations.
• Manual report generation is time-consuming and error-prone, impacting efficiency and accuracy.

Purpose of the Study:

• To develop and implement a rule-based AI tool for automating quality management report generation.
• To assess the impact of AI on report preparation time and data consistency in a Medical Genetics laboratory.

Main Methods:

• An AI tool was developed to extract and process electronic laboratory records.
• The tool utilizes pre-defined formulas and logic for automated report generation.
• The AI tool was implemented in a Medical Genetics laboratory setting.

Main Results:

• Report preparation time was reduced by 90%.
• Discrepancies compared to manual reports were eliminated.
• The need for extensive secondary reviews was reduced.

Conclusions:

• AI-assisted quality management reporting streamlines processes and enhances efficiency.
• Data consistency is improved, reducing errors in laboratory reports.
• Effective system refinement requires continuous stakeholder communication and validation.