LAB TEACHER AN INNOVATIVE APPROACH TO OPTIMIZING PREPARATION FOR LABORATORY TESTING AND REDUCING DIAGNOSTIC ERRORS BASED ON ARTIFICIAL INTELLIGENCE
Keywords:
Lab Teacher, laboratory diagnostics, pre-analytical phase, laboratory errors, artificial intelligence, digital healthcare, patient preparation, personalized approach, test accuracy, clinical decision-making, interactive systems, medical information technologies, diagnostic reliability, automation, healthcare efficiency.Abstract
In modern medical practice, laboratory testing represents one of the fundamental stages in clinical decision-making. At the same time, inadequate preparation for tests, errors occurring in the pre-analytical phase, and insufficient patient awareness significantly affect the reliability of results. This article scientifically substantiates the concept of an intelligent digital assistant called “LabTeacher.” This system is designed to provide patients with individualized, personalized, and interactive guidance for proper preparation for laboratory tests using elements of artificial intelligence. The study analyzes the role of digital healthcare technologies in reducing pre-analytical errors and examines the relationship between patient behavior and laboratory test outcomes. Through the “LabTeacher” system, patients receive real-time reminders and explanations regarding nutrition before testing, medication intake, physical activity, and other important factors. This contributes to minimizing human-related errors and improving the accuracy of laboratory results. The findings indicate that the use of digital assistant systems significantly enhances patients’ level of preparedness for laboratory testing and reduces the risk of incorrect diagnoses. In conclusion, the “LabTeacher” concept emerges as a promising innovative solution for improving the quality of laboratory diagnostics, optimizing clinical decision-making, and increasing overall efficiency within the healthcare system.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.











