Measuring the performance of an artificial intelligence-based robot that classifies blood tubes and performs quality control in terms of preanalytical errors: A preliminary study.

OBJECTIVES: Artificial intelligence-based robotic systems are increasingly used in medical laboratories. This study aimed to test the performance of KANKA (Labenko), a stand-alone, artificial intelligence-based robot that performs sorting and preanalytical quality control of blood tubes. METHODS: KANKA is designed to perform preanalytical quality control with respect to error control and preanalytical sorting of blood tubes. To detect sorting errors and preanalytical inappropriateness within the routine work of the laboratory, a total of 1000 blood tubes were presented to the KANKA robot in 7 scenarios. These scenarios encompassed various days and runs, with 5 repetitions each, resulting in a total of 5000 instances of sorting and detection of preanalytical errors. As the gold standard, 2 experts working in the same laboratory identified and recorded the correct sorting and preanalytical errors. The success rate of KANKA was calculated for both the accurate tubes and those tubes with inappropriate identification. RESULTS: KANKA achieved an overall accuracy rate of 99.98% and 100% in detecting tubes with preanalytical errors. It was found that KANKA can perform the control and sorting of 311 blood tubes per hour in terms of preanalytical errors. CONCLUSIONS: KANKA categorizes and records problem-free tubes according to laboratory subunits while identifying and classifying tubes with preanalytical inappropriateness into the correct error sections. As a blood acceptance and tube sorting system, KANKA has the potential to save labor and enhance the quality of the preanalytical process.

Determining patient preferences for remote monitoring.

This paper presents the patient preferences for an application in remote health monitoring. The data was collected through a mobile service prototype. Analytical Hierarchy Process and Conjoint Analysis were used to extract the patient preferences. The study was limited to diabetes and obesity patients in Istanbul, Turkey. Results indicated that sending users' data automatically, availability of technical support, and price are key factors impacting patient's decisions. This implies that e-health service providers and designers should focus on the services that enable users to send measurement results automatically instead of manually.