Machine Learning for Patient-Based Real-Time Quality Control (PBRTQC), Analytical and Preanalytical Error Detection in Clinical Laboratory

Abstract

The rapidly evolving field of machine learning (ML), along with artificial intelligence in a broad sense, is revolutionising many areas of healthcare, including laboratory medicine. The amalgamation of the fields of ML and patient-based real-time quality control (PBRTQC) processes could improve the traditional PBRTQC and error detection algorithms in the laboratory. This narrative review discusses published studies on using ML for the detection of systematic errors, non-systematic errors, and combinations of different types of errors in clinical laboratories. The studies discussed used ML for detecting bias, the requirement for re-calibration, samples contaminated with intravenous fluid or EDTA, delayed sample analysis, wrong-blood-in-tube errors, interference or a combination of different types of errors, by comparing the performance of ML models with human validators or traditional PBRTQC algorithms. Advantages, limitations, the creation of standardised ML models, ethical and regulatory aspects and potential future developments have also been discussed in brief.

Keywords: PBRTQC; artificial intelligence; bias; laboratory error; machine learning; quality control.

Figure 1

(a) Fewer analysers and fewer samples in a smaller laboratory, making pattern recognition and error detection by human technical or clinical validator easier. (b) Multiple data streams from numerous analysers and large amounts of data present challenges for the human validator in pattern recognition or error detection.

Figure 2

Contribution to analytical quality by traditional IQC methods, result validation by humans, traditional PBRTQC, and potential future ML model-based pattern recognition.