6 sigma image on a screen being held by fingers

Implementing a quality management system (QMS) is an important indicator of a business’s dedication to quality for its product or results. Quality control (QC) is one component of QMS that clinical labs have been using for decades to evaluate the analytical quality of lab results, predominantly through testing QC materials and using Westgard Rules to assess whether the quality is met. However, the Six Sigma quality management method has emerged over the past 2 decades as a means for providing additional, valuable information to assess quality in clinical labs. One incredibly useful, but in my view underutilized, aspect of Six Sigma involves calculating the Sigma metric.

Defining the Metric

The Six Sigma methodology, developed by a Motorola employee in the 1980s, measures process capability relative to quality requirements with the goal of only 3.4 defects per million products or results produced. The Sigma metric is a simple calculation using the allowable total error (ATE) or total (analytical) error allowable (TEa), bias, and imprecision for a particular process:


Sigma =                                   ATE or TEa – [Bias]                                   .

                    Imprecision (standard deviation or coefficient of variation)


ATE/TEa, bias, and imprecision are in the same units, which is either the measurement unit or percent. Percent may be the easiest unit with which to work. A higher Sigma indicates a higher-quality process because it is achieved through lower bias and/or imprecision. The desired minimum Sigma metric for a clinical lab assay is 3.0. Lower values indicate a need for more QC materials or runs for proper quality monitoring or for assay or process improvement to reduce bias and imprecision.

Choosing Data

Determining which data to use for the Sigma metric calculation might be the greatest barrier to incorporating Six Sigma into a QMS. Many methods can be used to calculate bias: comparing to a reference method, or assayed reference, QC, or linearity material; averaging bias compared to the peer group or all-methods means for multiple proficiency test surveys; or calculating deviation from the target concentration of spiked controls. Bias is different at different concentrations, so laboratorians have options like selecting the average bias to represent an average Sigma, using the range of biases to represent the range of Sigma, or calculating Sigma targeting medical decision limits.

Imprecision is usually determined from QC results, but as with bias, laboratorians will need to decide which imprecision value to use. Several resources are available to obtain evidence-based ATE/TEa values. For measurands without a reported ATE/TEa, selecting a close approximation using a similar measurand or a reasonable, arbitrary value may be needed. A lab’s quality goals can steer these decisions. 

Westgard has incorporated the Sigma metric for selecting a QC plan with associated Westgard Rules. Using either Power Function Graphs or OPSpec Charts, a QC plan suitable for the capabilities of a clinical lab assay can be chosen inclusive of the number of QC materials and runs, to achieve the desired probabilities of error detection (0.09 or higher) and false rejection (0.05 or lower). Power Function Graphs and normalized OPSpec Charts are available on the Westgard website.

The Power of the Sigma Metric

The Sigma metric empowers clinical laboratorians to better understand the quality in their labs and to select quality products. The Sigma calculation reveals whether bias, imprecision, or both are contributing to a lower Sigma metric for an assay or analyzer currently in use. With this valuable information, the associated processes can be thoroughly evaluated for improvements to reduce bias or imprecision, thus improving quality and subsequently reducing laboratory costs.

Additionally, calculating the Sigma for an assay or analyzer under evaluation for purchase can highlight products with better quality, enabling clinical laboratorians to make educated decisions on which purchases will maintain a high degree of quality for their labs. Moreover, clinical laboratorians making comparisons between products will likely motivate manufacturers to produce higher-quality products.

I urge clinical labs that haven’t already done so to embrace the Six Sigma metric, because it can be a powerful and valuable addition to any QMS and the start of a larger repertoire for quality improvement.


  1. Westgard S. Abbott Diagnostics. Quantitating quality: Best practices for estimating the Sigma-metric. https://www.corelaboratory.abbott/sal/whitePaper/Six%20Sigma_WP_Quantitating%20Quality_ADD-00058829.pdf (Accessed February 19, 2020).
  2. Westgard S, Bayat H, and Westgard JO. Analytical Sigma metrics: A review of Six Sigma implementation tools for medical laboratories. Biochem Med (Zagreb) 2018;28:020502.
  3. Westgard JO and Westgard S. Abbott. Six Sigma-based quality control learning guide series. https://www.corelaboratory.abbott/sal/learningGuide/ADD-00058819_Six_Sigma_Learning_Guide.pdf (Accessed February 19, 2020).
  4. Wheeler DJ. Twenty things you need to know. Knoxville, Tennessee: SPC Press 2009.

Laura Smy, PhD, MLS (CSMLS), is an assistant professor of pathology at the Medical College of Wisconsin in Milwaukee and co-director of clinical chemistry/toxicology and point-of-care section director at Wisconsin Diagnostic Laboratories. +Email: l.smy@mail.utoronto.ca