Optimization and Validation of Limit Check Error-Detection Performance Using a Laboratory-Specific Data-Simulation Approach: A Prerequisite for an Evidence-Based Practice

Developing an evidence-based approach to quality control

Effective Quality Control remains one of the pillars of Clinical Biochemistry. An understanding of the possible analytical errors that may occur, how to detect them efficiently and how to prevent them from causing patient harm are critical components of a Quality System. For some time, there have been questions about the theoretical basis of the models used to describe and detect analytical error. The current theory recognises two types of error, systematic and random and a system based on sampling the analytical process using a synthetic material to detect these errors. However, there are at least two other errors that are present. One is related to the QC material and the other, irregular errors. In this Opinion Piece, some of the underlying assumptions of Quality Control systems are described and analysed.

Technical quality assurance and quality control for medical laboratories: a review and proposal of a new concept to obtain integrated and validated QA/QC plans

Technical quality assurance (QA) and quality control (QA/QC) are important activities within medical laboratories to ensure the adequate quality of obtained test results. QA/QC tools available at medical laboratories include external QC and internal QC, patient-based real-time quality control (PBRTQC) tools such as moving average quality control (MAQC), limit checks, delta checks, and multivariate checks, and finally, analyzer flagging. Recently, for PBRTQC tools, new optimization and validation methods based on error detection simulation have been developed to obtain laboratory-specific insights into PBRTQC error detection. These developments have enabled implementation and application of these individual tools in routine clinical practice. As a next step, they also enable performance comparison of the individual QA/QC tools and integration of all the individual QA/QC tools in order to obtain the most powerful and efficient QA/QC plans. In this review, a brief overview of the individual QA/QC tools and their characteristics is provided and the error detection simulation approaches are explained. Finally, a new concept entitled integrated quality assurance and control (IQAC) is presented. To enable IQAC, a conceptual framework is suggested and demonstrated for sodium, based on available published data. The proposed IQAC framework provides ways and tools by which the performance of different QA/QC tools can be compared in a so-called QA/QC error detection table to enable optimization and validation of the overall QA/QC plan in terms of alarm rate as well as pre-analytical, analytical, and post-analytical error detection performance.

The next wave of innovation in laboratory automation: systems for auto-verification, quality control and specimen quality assurance

Laboratory automation in clinical laboratories has made enormous differences in patient outcomes, with a wide range of tests now available that are accurate and have a rapid turnaround. Total laboratory automation (TLA) has mechanised tube handling, sample preparation and storage in general chemistry, immunoassay, haematology, and microbiology and removed most of the tedious tasks involved in those processes. However, there are still many tasks that must be performed by humans who monitor the automation lines. We are seeing an increase in the complexity of the automated laboratory through further platform consolidation and expansion of the reach of molecular genetics into the core laboratory space. This will likely require rapid implementation of enhanced real time quality control measures and these solutions will generate a significantly greater number of failure flags. To capitalise on the benefits that an improved quality control process can deliver, it will be important to ensure that an automation process is implemented simultaneously with enhanced, real time quality control measures and auto-verification of patient samples in middleware. Therefore, it appears that the best solution may be to automate those critical decisions that still require human intervention and therefore include quality control as an integral part of total laboratory automation.