The article presents an outline of the concept of a prototype system allowing for the optimization of inventory management in a diagnostic laboratory on the basis of patients results. The effectiveness of laboratory diagnostics depends largely on the appropriate management of resources and the quality of tests. A functional quality management system is an integral element of every diagnostic laboratory, ensuring reliability and appropriate work standards. This system includes maintaining correct and reliable analytical test results as well as the optimal use of the laboratory equipment’s processing capacity and the appropriate organization of the supply chain—both analytical material and reagents. It is extremely important to avoid situations in which tests cannot be performed due to a lack of reagents, the overloading of analyzers, or improper calibration. Therefore, the accurate prediction of the number of orders is crucial to optimize the laboratory’s operations, both in the short term—for the next few hours and minutes—and in the longer term, even monthly, which will allow for the appropriate planning of reagent stock. As part of the research presented in this article, machine learning methods were used to implement the above functionalities, which allowed for the development of a prototype of a laboratory optimization system using patient test results as a basis.

Efficient management of medical diagnostic laboratories is critical for ensuring high-quality test results, optimal utilization of analytical resources, and continuity of services, particularly under conditions of increased and variable demand. This study addresses the problem of forecasting laboratory workload and supporting quality control processes using data-driven methods. A large-scale dataset comprising over 140 million laboratory tests performed on approximately 11 million patients was integrated into a unified analytical framework. Machine learning and time-series analysis methods were applied to model four key laboratory functions: prediction of the number of tests performed, estimation of reagent demand, detection of analyzer overloads, and assessment of the stability of measurement methods within quality control procedures. The proposed approach combines patient profiling, sequential pattern analysis, neural networks, and classical forecasting models such as ARIMA and exponential smoothing. The results demonstrate high predictive accuracy for both global test volumes and patient-based quality control indicators, with determination coefficients exceeding 90% for workload forecasting and classification accuracy above 98% for identifying unnecessary recalibrations. These findings indicate that the use of machine learning techniques enables effective optimization of laboratory operations by reducing reagent shortages, preventing analyzer overloads, and improving the efficiency of quality control processes. The presented methods support more reliable, cost-effective, and scalable management of modern diagnostic laboratories.