Building an effective QC system

June 24, 2024
Applying workforce considerations and asking the right questions are key to establishing lab QC protocols.

Establishing overall quality goals for analytical performance is the first step toward assuring the quality of the analytical process and building an effective internal quality control (QC) system for your laboratory. This strategy sets the stage for creating a meaningful QC plan designed to meet basic accreditation requirements for quantitative tests. It is worth noting that many of the same principles will also apply to qualitative testing.

The laboratory should establish the level of risk they are willing to accept for reporting an erroneous patient test result. This goal should be the cornerstone of the quality plan for QC, and all aspects of the plan should be based on this.1

The laboratory should also define, in general terms, what it strives to achieve for analytical quality. These quality goals could be based on analyte-specific performance goals, such as total error, imprecision, and/or bias. To monitor these performance goals, the laboratory should establish policies on quality control testing, including the control materials and the process control system to be used.2

Analyte-specific performance goals

In developing analyte-specific performance goals, the laboratory may consider the following:

1. Which tests in the laboratory pose a higher risk of harm to the patient if an erroneous result is reported?

2. Should the laboratory plan make special provisions for higher-risk tests? 

3. Is the laboratory aware of any tests that might be considered inconsistent performers requiring tighter control?

4. What is the expected frequency/probability of failure or malfunction (i.e., reliability) of the instrument, kit, or method?

5. How important is it to be alerted when a medically relevant analytical error occurs?

Develop a plan by defining laboratory policies

Once quality goals are established, a tactical plan designed to meet these goals should be prepared. The plan should be specific and identify QC measures for each test based on risk of reporting an erroneous patient test result and the severity of the outcome if one is reported. The plan should also consider assay limitations, the probability of device failure, and the level of technical expertise required to perform the test. In accordance with good laboratory practice, the plan should ensure that control materials are treated like patient samples during testing.

Elements of the plan may include the following:

1. Analyte-specific performance goals with the following characteristics:3

·        Unique and defined by the individual laboratory, based on clinical outcomes

·        Acceptable limits for bias, imprecision, and total error based on biological variation

·        Based on best practices and

o   Represented by long-term, between-run imprecision, as reported by the manufacturer or as measured by the laboratory

o   Derived from regulatory agencies, professional organizations, or a proficiency testing organization

o   Total error (TE) for the test, as determined by the manufacturer or laboratory

2. Frequency of including quality control materials for each analyte tested, based on a risk assessment. If electronic controls are used, both the use of electronic controls and the frequency of testing should be based on the risk assessment.

3. Concentrations (or levels) of quality control materials based on a risk assessment. Some countries support testing at least two different concentrations (usually a normal and abnormal concentration) depending on assay limits and the range of patient test results commonly reported. Other countries require controls covering the analytical range of the test.

4. An effective process control system for each analyte using appropriate statistical QC rules. The laboratory should avoid:

·        Indiscriminate use of the 1:2s rule for run rejection

·        Setting the same process control rule, or multi-rule, for all tests regardless of test capability or clinical utility

5. Statistical parameters for control materials — mean, median, standard deviation, CV%, total error — as established by the laboratory through repetitive testing. The plan should discourage long-term use of product insert values for establishing acceptable performance. Procedures should describe how to calculate a valid and reliable mean and standard deviation, i.e., setting target values and ranges of acceptable performance.2

6. Requirements for parallel testing of all new lots of controls alongside current validated lots to establish new target values and ranges of acceptable performance.

7. Specific intervals at which the laboratory will reassess the relevance and appropriateness of all statistical parameters used by the laboratory, with attention given to each test's mean and standard deviation.

8. A comprehensive training program that covers the following:

·        Basic QC statistics and interpretation

·        How to handle control materials and prepare them for use: storage, reconstitution, or thawing

·        How to interpret QC patterns: trends, shifts, random error, systematic error, error that requires action, and error that does not require immediate action

·        How to resolve out-of-control situations

·        How to log and maintain QC results and document that QC was performed

·        Where to go to for additional troubleshooting assistance, if necessary

9. Participation in an external interlaboratory comparison program for all parameters tested in the laboratory. Such programs include those provided by commercial companies, government-run initiatives, and private individuals or organizations. If no comparison program is available for certain tests, the laboratory should have some other means of demonstrating the competency of laboratory staff and the reliability of test results.2

10. The nature of the control materials to be used. Many options are available, including electronic controls, commercial products, and patient pools. It may be appropriate to use a combination of different types of QC materials throughout the laboratory.

Control material selection

Electronic controls: The plan should identify which tests may be monitored using electronic controls and any additional measures needed to assure quality of patient test results.

If electronic controls are used, the laboratory should understand what portion of the analytical process is being monitored and if there is a need for additional controls to sufficiently mitigate the risk of reporting patient test results with medically important error.

Commercial control products: These include in-kit controls, instrument manufacturer controls, and independent third-party controls. The plan should describe when commercial control products are suitable materials for controlling the analytical process.

The laboratory should compare the effectiveness of in-kit, instrument manufacturer, and third-party controls at detecting trends, shifts, and medically important errors. Consideration should be given to control matrix (human versus non-human). Some accreditors/regulators may require the laboratory to know whether matrix effects are present that could potentially mask analytical errors.

In-kit or instrument manufacturer controls designed for specific test methods may not be suitable for other test methods or instruments. When in-kit or manufacturer controls are used to calculate assay cut-off ranges, some regulatory bodies may recommend the use of independent control materials to monitor the analytical process.

The plan should discourage use of control materials as calibrators and vice versa. This is not considered good laboratory practice. Sensitivity of the control product for detecting changes in the
test system can be an issue for in-kit or instrument manufacturer controls when they are manufactured at the same time and from the same raw materials as the calibrator(s).

Note: Almost all commercially available control products are neither intended nor labeled for “trueness of measurement” and therefore the laboratory does not have to document traceability.  However, if the control material chosen by the laboratory is labeled by the manufacturer as intended for “trueness of measurement,” the laboratory should document the metrological traceability of the product.4

Patient Pools

The plan should describe when patient pools are suitable materials for controlling the analytical process. Some questions that might need to be answered include the following:

·        Should all patient samples be tested for infectious diseases before mixing with the pool?

·        Does the plan address national ethics’ regulations regarding patient consent before using a patient’s sample as part of a patient pool?

·        Is it important to have pools with analyte concentrations
at the medical decision points? Is it possible to achieve these concentrations?

·        How will the laboratory achieve and maintain homogeneity of the material?

·        How will the pool be stabilized and stored?

·        What is the stability of the pool?

Conduct reviews to assess relevance & effectiveness

Process control systems should reflect current laboratory conditions and requirements. Conditions may change, which might require a reassessment of the QC plan for ongoing relevance, appropriateness, and effectiveness. Laboratory staff must understand their individual roles and responsibilities in implementing, maintaining, and modifying the plan as external factors in the laboratory change. Conducting reviews is the best way to evaluate relevance and effectiveness. Before a review is conducted, determine the frequency at which they will occur, and at what level(s): bench review, supervisor review, or departmental review.

Particular attention should be paid to the sensitivity of the system. If the system is too sensitive, it will likely generate an unacceptable number of false positive alerts leading to costly and unnecessary troubleshooting and repeats. Conversely, an insensitive system may miss important analytical errors. Consequently, the laboratory should have a feedback mechanism that provides data relative to the relevance and effectiveness of the process control system in use. It is critical that both laboratory staff and clinicians understand the performance characteristics of each test method.


1. CLSI. Laboratory Quality Control Based on Risk Management. 2nd ed. CLSI guideline EP23. Clinical and Laboratory Standards Institute; 2023.

2. CLSI. Statistical Quality Control for Quantitative Measurement Procedures: Principles and Definitions. 4th ed. CLSI guideline C24. Clinical and Laboratory Standards Institute; 2016.

3. Sandberg S, Fraser CG, Horvath AR, et al. Defining analytical performance specifications: Consensus Statement from the 1st Strategic Conference of the European Federation of Clinical Chemistry and Laboratory Medicine. Clin Chem Lab Med. 2015;53(6):833-5. doi:10.1515/cclm-2015-0067.

4. In Vitro Diagnostic Medical Devices -Measurement of Quantities in Biological Samples-Metrological Traceability of Values Assigned to Calibrators and Control Materials.