The summary of ‘Levey-Jenning chart’

The video discusses the application of Gaussian curves in laboratory settings through Levey-Jennings (LJ) charts. It explains the principles of Gaussian distributions and control limits, emphasizing the importance of monitoring LJ graphs for error detection and quality control. The importance of setting accurate standards, calculating mean and standard deviation, and creating control charts for QC management is highlighted. The need for real-time observations and systematic documentation for accreditation purposes is emphasized. Training staff to identify system instabilities and taking remedial actions when QC failures occur are key aspects discussed in the video.

00:00:00

In this segment of the video, the speaker discusses the application of the Gaussian curve in the laboratory, particularly through the use of Levey-Jennings charts. They provide a brief history of Levey-Jennings charts and how they were developed based on statistical control charts proposed by W. Watts in 1931. The video also touches on the basic principles of Gaussian distribution, highlighting that 68% of data falls within one standard deviation, 95% within two, and 99.7% within three. Any deviation from these percentages indicates a shift in the mean. The charts graphically represent data points in the lab, with the x-axis representing the data points and the y-axis representing standard deviations.

00:03:00

In this segment of the video, the speaker discusses creating an LJ graph from a Gaussian curve by drawing lines on the y-axis at various standard deviations. They explain how data points falling within the normal distribution indicate a stable analytical system. The concept of control limits, such as the minus 3 SD and plus 3 SD control limits, is explained using an example with a mean of 190.5 and a standard deviation of 2. The video demonstrates how data points falling outside these limits on an LJ chart indicate an invalid run. The importance of interpreting LJ graphs to assess the validity of runs is highlighted, showing how data points outside of the normal distribution are easily identifiable.

00:06:00

In this segment of the video, the speaker discusses the concept of a Gaussian pattern, which represents a normal distribution of data points. They explain that data points usually fall within certain standard deviation limits, with outliers indicating random errors. The speaker demonstrates how shifts in data distribution can indicate systematic errors. They emphasize the importance of observing LJ graphs in terms of Gaussian curves to detect errors. Furthermore, they stress the need for systematic monitoring and evaluation of LJ graphs to ensure quality control and prompt corrective actions when needed.

00:09:00

In this segment of the video, the importance of understanding control limits in creating a normal Gaussian graph using LJ methodology is emphasized. It is explained that having sufficient data points is crucial for accurate interpretation, with clustering within one standard deviation being common. Multiple levels of quality controls should be charted, following specific rules. The LJ graph serves to display control results and assess procedure stability. To get started with LJ graphing, one needs to determine the mean and standard deviation, calculate decision limits, and plot data points against time to detect trends or errors in the system. Automated machines often have tools for LJ chart creation.

00:12:00

In this segment of the video, the speaker discusses setting quality control (QC) limits for equipment in laboratories. They explain that while many machines have 2 standard deviation (SD) limits set, using 3 standard deviation limits may be more beneficial to avoid unnecessary rejection of runs. It is mentioned that accurately setting standard deviations is crucial, especially since some people may not calculate them correctly. The video advises on creating control charts for each test and level of control, either manually using arithmetic graph paper or through computerized systems like Excel or specialized QC software. The importance of real-time observations for effective QC management is emphasized, with software programs like Westgard QC and Labs for Life QC being highlighted for their capabilities in LJ chart calculation.

00:15:00

In this part of the video, the speaker recommends using QC software if your analytical system doesn’t have built-in software. If doing it manually, they suggest using a graph paper to plot values, write dates and initials, and label analytes, lot numbers, expiry dates, etc. Documentation of LJ charts is emphasized for accreditation purposes. They discuss how to calculate mean and SD for control levels in AST, assign limits on a chart, and plot values. The importance of thorough and systematic documentation is highlighted.

00:18:00

In this part of the video, the key points discussed include starting a QC program by calculating mean and standard deviation of control levels, creating a graph to monitor data daily, emphasizing the need for close scrutiny of data for error detection, and the importance of documenting QC activities for accreditation purposes. It is highlighted that training staff to observe charts critically is vital for identifying instabilities in the analytical system. The frequency of reviewing QC data may vary based on criticality and stability of analytes, and daily reviews are compared to periodic reviews using a baby analogy. Stresses the importance of closely examining charts for error detection and adding value to laboratory processes.

00:21:00

In this segment of the video, the speaker emphasizes the importance of evidence of remedial action in case of quality control (QC) failures. They mention looking for corrective actions and providing proof of these actions when violations occur. Monthly LJ charts and CV tracking for at least six months are highlighted as important practices. Documenting QC-related activities is stressed as crucial for accreditation, with non-conformance in ensuring examination quality considered a major issue. Viewers are encouraged to refer to the QC module on the website for further understanding.