Measuring the operational performance of an artificial intelligence-based blood tube-labeling robot, NESLI.

Abstract

Objectives: Laboratory testing, crucial for medical diagnosis, has 3 phases: preanalytical, analytical, and postanalytical. This study set out to demonstrate whether automating tube labeling through artificial intelligence (AI) support enhances efficiency, reduces errors, and improves outpatient phlebotomy services.

Methods: The NESLI tube-labeling robot (Labenko Informatics), which uses AI models for tube selection and handling, was used for the experiments. The study evaluated the NESLI robot's operational performance, including labelling time, technical problems, tube handling success, and critical stock alerts. The robot's label readability was also tested on various laboratory devices. This research will contribute to the field's understanding of the potential impact of automated tube-labeling systems on laboratory processes in the preanalytical phase.

Results: NESLI demonstrated high performance in labeling processes, achieving a success rate of 99.2% in labeling parameters and a success rate of 100% in other areas. For nonlabeling parameters, the average labeling time per tube was measured at 8.96 seconds, with a 100% success rate in tube handling and critical stock warnings. Technical issues were promptly resolved, affirming the NESLI robot's effectiveness and reliability in automating the tube-labeling processes.

Conclusions: Robotic systems using AI, such as NESLI, have the potential to increase process efficiency and reduce errors in the preanalytical phase of laboratory testing. Integration of such systems into comprehensive information systems is crucial for optimizing phlebotomy services and ensuring timely and accurate diagnostics.