Internal laboratory quality control is part of the necessary routine to monitor the performance of analytical processes and verify that the results remain within the criteria defined by the laboratory. More than just fulfilling a requirement, it helps to identify changes before they compromise the reliability of the tests.
However, simply running control samples is not enough. It is necessary to analyze the results, identify outliers, investigate possible causes, and record the decisions made. When these steps depend on manual controls and scattered information, the team may have more difficulty identifying trends and acting at the right time.
What is internal laboratory quality control?
Internal Quality Control, also called IQC, is a procedure performed in conjunction with tests to evaluate the accuracy of the analytical system and verify that it operates within predefined tolerance limits.
In practice, the laboratory analyzes control materials with known or expected values. The results obtained are compared to the established criteria for each analyte and methodology. In this way, the team can monitor the stability of the process and recognize signs of variation that need to be evaluated.
Internal Quality Control (IQC) should not be confused with External Quality Control (EQC). While internal control continuously monitors routine performance within the laboratory itself, external control evaluates accuracy and performance through interlaboratory comparisons conducted by proficiency testing providers. The two are complementary.
What does RDC 978/2025 establish regarding CIQ?
A RDC 978/2025 from Anvisa It stipulates that Quality Control Management must consist, at a minimum, of Internal Quality Control (IQC) and Internal Quality Control (IQC). The standard also establishes that internal control must be performed on all equipment in use and for all analytes processed by the service.
Furthermore, the laboratory must define acceptance and rejection criteria according to the analyte and the methodology used. It also needs to record the results, analyze the data, investigate the causes of inadequacies, and document the actions taken when a control sample is rejected.
Therefore, quality control is not simply about generating value. It involves evaluation, decision-making, traceability, and continuous improvement of the analytical process.
What deviations might indicate that the process needs attention?
An isolated result outside the limit is a clear warning sign. However, other behaviors can also show that the process is deviating from expected performance, even before a clearer rejection occurs.
Among the signs that deserve attention are:
- Points outside the defined control limits;
- Consecutive results concentrated on the same side of the average;
- Continuous upward or downward trend;
- Sudden change in the usual level of results;
- Increased dispersion between measurements;
- Repetition of alerts related to the same analyte, equipment, batch, or control level.
These behaviors may be associated with different factors, such as changes in reagents and calibrators, batch changes, environmental conditions, equipment wear or maintenance, variations in material preparation, and execution errors.
Therefore, the alert should not be interpreted in isolation. It should initiate an investigation guided by laboratory procedures and manufacturer instructions.
How does the Levey-Jennings chart help visualize performance?
The Levey-Jennings chart organizes the results of the control samples over time. Typically, it presents the mean and ranges calculated from the standard deviation, allowing one to observe the position of each result in relation to the expected behavior.
This visual representation makes it easier to identify outliers, level changes, dispersions, and trends. Thus, the team moves beyond evaluating only the most recent result and begins to see the history of the analytical process.
This perspective is important because some problems develop gradually. A set of results may still be within individual limits and yet exhibit behavior that indicates a loss of stability. By visualizing the sequence, the laboratory gains more elements to act preventively.
What is the function of the rules of Westgard?
Westgard rules use statistical criteria to support the evaluation of control results. Applied together, they help decide whether an analytical run is under control or should be rejected and investigated.
Among the best-known rules are 1-2s, 1-3s, 2-2s, R-4s, 4-1s, and 10x. Each one observes a specific pattern related to the position and sequence of results on the graph.
The 1-2s rule, for example, often serves as a warning for more careful analysis, while others may indicate rejection, depending on the control strategy adopted.
However, the rules should not be applied indiscriminately. The selection must consider the methodology, the quantity and levels of control materials, the analytical performance, and the criteria defined by the laboratory. The goal is to balance the ability to detect errors with the risk of false rejections.
What to do when a control is outside acceptable criteria?
When a result violates the defined criteria, automatically repeating the check until an acceptable value is obtained does not resolve the root cause of the problem. This practice may only mask an instability that will continue to be present in the routine.
The right path includes:
- Stop releasing potentially affected results, in accordance with laboratory procedures;
- Confirm the alert and evaluate the control data, the graph, and the rules applied;
- Investigate possible causes related to reagents, batches, calibration, equipment, maintenance, environment, and execution;
- Perform the planned corrective actions and verify if the system has returned to the expected performance;
- Assess the need to review patient results processed during the affected period;
- To document the inadequacy, the investigation, the decisions, and the actions taken.
These steps must follow internal procedures, manufacturer instructions, and the assessment of the responsible technician. Technology can organize and streamline the process, but the technical decision remains the responsibility of qualified professionals.
Why can manual control hinder quality management?
Spreadsheets and isolated records can handle very simple routines. However, as the number of analytes, equipment, batches, and control levels increases, manual management becomes more laborious and vulnerable to errors.
Among the most common difficulties are:
- Repetitive typing of results;
- Information distributed across different files;
- It takes time to identify trends and violations;
- Difficulty in retrieving the history of a batch or piece of equipment;
- Lack of standardization in the analysis of controls;
- Greater effort is needed to gather evidence in audits;
- Risk of releasing test results before a complete assessment of the patient’s condition.
In this scenario, the team spends more time organizing the data and less time on critical analysis. Furthermore, important information may only be noticed after several results have already been processed.
How does technology strengthen laboratory quality control?
A specialized system centralizes results and applies defined criteria in a standardized way. This allows the laboratory to monitor performance by analyte, equipment, sector, batch, and control level, as well as quickly view alerts and violations.
Automation also contributes to:
- Apply the control rules consistently;
- Reduce manual typing and the risk of transcription errors;
- Identify trends and deviations more quickly;
- Maintain a record of analyses and decisions;
- Generate reports for monitoring and auditing purposes;
- To facilitate the traceability of user actions;
- Support the preventive blocking of tests that do not meet the established criteria.
These resources do not replace professional analysis. They offer more organized information and faster alerts so that the team can make technical decisions with greater confidence.
How does TMQuality support this routine?
THE TMQuality This is a solution developed by TM Tecnologia to support internal quality control in clinical analysis laboratories.
The tool applies Westgard rules and presents the data in a Levey-Jennings plot, making it easier to identify trends, variations, and points outside acceptable limits.
It also provides coefficient of variation reports by equipment, sector, batch, and control level. Furthermore, it allows for managing tests with violated parameters, blocking tests outside acceptable criteria, and maintaining traceability of actions such as control validations and changes to graph data.
Integrated with TMInterface, TMQuality can connect to the laboratory system used by the lab or to LISNet. In this way, quality control data becomes part of a more connected workflow, reducing manual tasks and making quality management more agile.
Analytical quality requires continuous monitoring.
Internal laboratory quality control should not be seen merely as a mandatory step before releasing test results. It is a continuous source of information about process stability, equipment performance, and the need for preventive or corrective actions.
When data is tracked in a structured way, the laboratory can identify deviations earlier, document its decisions, and strengthen the reliability of the results.
With skilled professionals, well-defined procedures, and appropriate technology, quality control ceases to be just a daily check and transforms into a management tool.
Would you like to learn about a solution that centralizes information, automates rules, and facilitates the traceability of internal controls? Learn more about TMQuality or speak to the TM Tecnologia team by WhatsApp (11) 94075-1513.
