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.
Opaliński et al. (Wed,) studied this question.