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Measurement uncertainty (MU) of results is one of the basic recommended and accepted statistical methods in laboratory medicine, with which analytical and clinical evaluation of laboratory test quality is assessed. Literature data indicate that the calculation of MU is not a simple process, but that its assessment in daily laboratory practice should be reduced to routine and simple presentation, understandable to both laboratory professionals and physicians. In order to achieve this, it is necessary to understand the purpose of the test for which MU is to be determined. Various suggestions have been given for presentation of MU as a quantitative indicator of the quality of the final measurement result in the medical laboratory. Although MU refers to the final measurement result, this metrological concept reflects the entire laboratory measurement process. The data on estimated MU is used to interpret the measured numerical result, and represents quantitatively the quality of the measurement itself, i.e. how different are the results of multiple measurements of the analyte of interest in the same sample, as well as whether the method of determination itself is subjected to significant random and systematic deviation. Initially, in the metrological concept, the MU is viewed in relation to the true value of the analyte of interest. However, the true value of the analyte measured in the biological fluid matrix of the study population cannot be known. It is therefore considered the closest value obtained by the perfect method, for which the bias and inaccuracy, as measures of systematic and random error, are equal to zero, which is practically impossible to achieve in routine laboratory practice. Although current standards require accredited medical laboratories to estimate MU, none of these guidelines provide clear guidance on how this can be achieved in daily laboratory work. This review examines literary data and documents dealing with MU issues, but also highlights what additional terms and data should be considered when interpreting MU. This paper ultimately draws attention, and once again points out, that a simpler solution is needed for this universal concept to be formally and universally applicable in routine laboratory medicine practice.

Background Many of the mobile applications (apps) used for delivering health interventions involve laboratory medicine data. This survey was conducted to search the online market for health apps that manage laboratory medicine data. The aim was to review them and perform a quality evaluation. Methods Apps search criteria were "Lab results blood work", "Lab results", and "Health apps". After the stepwise exclusion process, 52 selected apps were downloaded and analyzed. For review and content analysis of the apps, a multidimensional tool for classifying and rating the quality of mobile health apps - Mobile App Rating Scale (MARS), was used. Results Selected apps were classified into five categories according to their intended use by patients or physicians, and the type of data engaged. Spearman's correlation analysis found significant correlations between MARS individual scoring items, as with the subjective quality and number of technical aspects. Kruskal-Wallis analysis showed a significant difference in the number of technical aspects employed, MARS engagement and informational quality score items, total score, and subjective quality. The lowest values for all of these items were in the category of apps designed for patients, and the post hoc test showed that the difference was statistically significant between this and the values in all other categories. Conclusions Apps designed for patients, are of the poorest quality, considering the total quality of the content and information they provide, estimated using the MARS tool. This estimation needs to be validated for laboratory medicine apps, and eventually modified after consideration of specific quality benchmarks.