## 2 Materials and Methods
Therefore, we can only rely on time series analysis and autocorrelation. Exponential smoothing models were utilized to analyze time series data related to the overall number of orders. This enabled the development of prediction models for the global number of orders in patient groups, with an accuracy range of 91-99% Determination Coefficient (R2), depending on the parameter. The analysis of prediction accuracy was performed on a dataset concerning 11 analytes, totaling 1,388,845 orders from patients over six months (calculated from a period of at least 3 months from the two previous years from the same time periods). The means and standard deviations of these samples were compared. 3. By having datasets of results for a specific test parameter, meta-characteristics can be observed based on all the results. Changes in their distribution may indicate disturbances related to the calibration of devices such as analyzers. This approach to quality control, based on measurement data, allows for the effective detection of the necessary calibration moment and constitutes another machine learning task. Multi-layer perceptrons (MLPs), which are a type of neural network, were used and found to be effective. For the analysis, 16.5 thousand measurement results and 13 calibration points were utilized. As a result of the study, 9 parameters which, in a combined model, enable the assessment of measurement effectiveness were developed. 4. The last task involved predicting analyzer overload. Diagnostic laboratories have a cyclic daily pattern of work, with the testing of samples from hospital patients and medical units continuing throughout the day. However, the largest quantity of samples is collected during the peak period, when material is collected from partners serving as blood collection points. This peak is associated with the transfer of material to couriers, which can occur once or twice within an hour of operation. If all the material flows into the device simultaneously, the queue may exceed the analyzer's capacity. By anticipating the moment of overflow, we can direct the material to other less burdened devices to avoid queuing and increase overall daily testing efficiency. Logistic regression and CART classification trees were used to create effective models for predicting maximum load. In the case of analyzer overload, data pertained to the six most burdened laboratory centers and covered the period of 2020 and 2021. Through aggregating information on the central server, data from a total of 434 analyzers processing data in these laboratory centers were obtained. Taking into account their cumulative load, this number can be reduced to 201 analyzers, assuming that optimization will only affect analyzers performing an average of at least 21 tests per day, and similarly, 88 analyzers performing at least 100 tests per day and 10 analyzers performing at least 100 tests per day. The group of the most burdened analyzers was considered (as the benefit from their optimization is the greatest). For testing, to avoid systematic errors associated with seasonal load, a sample of 30 daily runs was randomly selected from 5 random analyzers from the group of the most burdened (data concerning one day of work for 5 random analyzers) from a period of at least 3 months of work (the years 2020 and 2021 were considered). As a control group, 30 random daily runs were selected from the two previous years and the same time period (appropriate 3 months, appropriate 5 random analyzers). On average, this group of analyzers performs approximately 23,000 tests per day.