The release of clinically useful results that reflect the reality of individuals, is a fundamental pillar in the mission of clinical laboratories to ensure timely and accurate decision making, reducing the investment of economic resources and time in patient care; aimed at this purpose, the implementation of control tools that allow continuous monitoring of processes is a constant need in the laboratory, which is why, at present, different tools and concepts developed in the manufacturing industry have been applied in the clinical laboratory environment.

Six sigma is a process improvement methodology developed by Motorola in 1986, with the objective of reducing process variability. (Prabhushankar GV, 2008)This methodology has two fundamental approaches, which allow its application in the different process phases in the laboratory (pre-pre-analytical, pre-analytical analytical, post-analytical and post-post-analytical): the first of these approaches is through the counting of defects per million opportunities and its expression in the appropriate sigma metric, which can be applied at the pre- and post-sigma stages. post-analytical and the second, the estimation of sigma metrics in the analytical phase, by determining the number of standard deviations for each of the analytes evaluated, based on the analytical specifications previously established, through the implementation of the maximum permissible error.

Six sigma in the pre- and post-analytical stage.

It is estimated that about 60% – 70% of medical decisions made during the process of diagnosis and treatment of patients are based on the results issued in the clinical laboratory (González & Gómez, 2018), which is why, the control and continuous improvement of processes at all stages of the laboratory must be ensured, in order, to guarantee reliable results.

In this context, the implementation of quality indicators facilitates the identification of deviations in the process and timely intervention, in order to minimize the impact of errors; for qualitative variables, such as errors in the identification of samples, poorly taken samples or transcription errors, the six sigma methodology allows determining the variability of the processes of each of the implemented indicators, and representing the number of errors (or defects) per million opportunities, and correlating it with the sigma metric. In this process, the defects per million opportunities (DPMO) must first be determined using the following formula:

Subsequently, DPMO is correlated with the corresponding sigma metric level (Table 1), considering a process susceptible to improvement, those in which a sigma metric lower than four (4) is obtained , being the goal six (6) sigma, where a maximum of 3.4 defects per million opportunities is expected.

Table 1. Sigma metric, associated with defects per million opportunities (DPMO).

This approach provides the laboratory with the opportunity to standardize the evaluated processes, facilitate the analysis of results and the implementation of strategies aimed at process improvement.

Six sigma in the analytical stage of the laboratory.

Currently, the analytical phase of the laboratory is considered one of the best monitored phases of the process, in which different control points are implemented to evaluate the performance of the different components involved in obtaining the result, such as: the correct functioning of the measuring system and reagents, and the skills of the personnel, through the processing of internal quality control materials.

For the implementation of six sigma in this phase, laboratory personnel must first determine the attributes of accuracy and trueness. (Table 2); as well as defining the analytical quality specifications under which the performance of the process will be evaluated. (ETa); these specifications represent the error rate that can be allowed without affecting the results issued (that are useful for the patient), considering precision and bias; at present, there are different models to determine these specifications: (1) based on the effect of analytical variation on the results, (2) based on biological variability components, and (3) in the state of the art (MarisCarchio, Cappella, Goedelmann, Pandolfo, & Bustos, 2019); within these three models there are different query sources that can be implemented for each of the analytes evaluated, such as biological variability or CLIA.

It is important to note that the laboratory is free to implement different sources for the evaluation of its performance, which may vary between analytes and must be the same for the different levels evaluated, in order to correctly evaluate performance.

*Not required for sigma metric calculation.

Table 2. Attributes necessary for performance evaluation and determination of sigma metrics.

Once these variables have been established, the sigma metric calculation is performed using the formula:

According to the results obtained, the laboratory can determine its level of compliance in terms of total error and sigma metric, being acceptable performance when the ET of each of the evaluated levels is less than or equal to the ETa, and in relation to the sigma metric, there are different scales to evaluate performance: unacceptable when the sigma metric is lower than the Z constant used in the laboratory (1.65 – 1.96 or 2.33) marginal, between the value of the Z constant and 3, acceptable between 3 and 3.99, good between 4 and 5.99 and excellent if greater than 6.

Conclusion.

It is important to emphasize that nowadays the clinical laboratory has different tools that allow constant monitoring of the different stages or phases of the laboratory, such as the application of six sigma, which contributes to the release of clinically useful results, facilitating decision making and intervention in a timely manner, in order to minimize the impact of errors, contributing to continuous improvement and the release of clinically useful results.

References:

González, M. S., & Gómez, N. G. (2018). EVIDENCE-BASED LABORATORY. CHALLENGES AND OPPORTUNITIES. CONTINUING EDUCATION COURSE IN THE CLINICAL LABORATORY – SEQC.

MarisCarchio, S., Cappella, A. C., Goedelmann, C., Pandolfo, M., & Bustos, D. (2019). Application of Six Sigma in the Laboratory. Acta Bioquímica Clínica Latinoamericana.

Prabhushankar GV, D. S. (2008). The origin, history and definition of Six Sigma:. IJSSCA, 4.

Written by

Leidy Paola González Camacho
Bacteriologist and clinical laboratorist. Master in Microbiology. Diagnostic marketing.