Introduction To Statistical Quality Control Ed ...
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Once solely the domain of engineers, quality control has become a vital business operation used to increase productivity and secure competitive advantage. Introduction to Statistical Quality Control offers a detailed presentation of the modern statistical methods for quality control and improvement. Thorough coverage of statistical process control (SPC) demonstrates the efficacy of statistically-oriented experiments in the context of process characterization, optimization, and acceptance sampling, while examination of the implementation process provides context to real-world applications. Emphasis on Six Sigma DMAIC (Define, Measure, Analyze, Improve and Control) provides a strategic problem-solving framework that can be applied across a variety of disciplines.
A major tool for quality control and management, statistical process control (SPC) monitors sequential processes, such as production lines and Internet traffic, to ensure that they work stably and satisfactorily. Along with covering traditional methods, Introduction to Statistical Process Control describes many recent SPC methods that improve upon
Previous articles1,2,3,4 in this series on quality management for in-clinic laboratories have introduced the need for a total quality management system for in-clinic laboratory testing, quality planning, and a quality plan; discussed some aspects of facilities, instrumentation, health and safety, training, and improvement opportunities; discussed standard operating procedures; and considered various aspects of equipment or instrument maintenance and analytic performance assessment. The purpose of this final article is to provide additional information and examples regarding statistical QC for in-clinic laboratory testing and to expand on the quality-assurance concepts introduced in the fourth article4 of the series.
Laboratory error can occur in any phase of in-clinic laboratory testing, whether the preanalytic (from test ordering through sample transit and preparation), analytic (sample analysis), or postanalytic (from results reporting to interpretation) phase.1 Statistical QC refers to the use of statistical methods in the monitoring and maintenance of the quality of products and services.5 Within the context of veterinary clinical laboratory testing, statistical QC applies to the analytic phase. Traditionally, such QC has been used in laboratories to provide peace of mind that a laboratory system (ie, the instrument, reagents, and operator) is performing stably prior to testing patient samples. It can be applied to many of the testing systems within the laboratory and is commonly used for hematologic and biochemical analysis and dipstick or automated urinalysis.
It is difficult to look at any single test result and know whether that number or result is accurate and reliable. Statistical QC helps ensure that laboratory results can be used for diagnosis, identification of patients that may require further diagnostic investigation, and monitoring disease progression or response to treatment. For a laboratory result to be clinically useful, the measurement error needs to be smaller than the decision threshold used to determine whether the result is within reference limits or abnormal; that is, the result should be able to discriminate between health and disease.8 Once a result has been shown to be clinically useful, ongoing statistical QC helps to ensure that results are the product of a stable laboratory system that can be relied on for continued use. Statistical QC is but a single tool in a total quality management system.1 Adherence to statistical QC can ensure that the TEobs remains less than the TEa, which are concepts explained in the fourth article4 of this series. Statistical QC is a more advanced but also a more reliable way of monitoring instrument performance, compared with methods involving manufacturer-supplied target means and ranges.
Designing an in-clinic statistical QC strategy involves assessment of instrument performance, deciding whether the 1-3s control rule is suitable for each measurand, calculating control limits, and applying the new control limits to future (daily or routine) QCM measurements (Figure 2).
The first step in the statistical QC process is to assess the performance of the instrument, specifically the bias and CV associated with each measurand. This assessment should be performed on acquisition of the instrument and then annually thereafter or more frequently to coincide with any software updates. The purpose of this first step is to not only evaluate the performance of the instrument but also to gather the data that will be used to generate control limits. This assessment requires at least 5 repeated analyses of 1 or more assayed commercially available QCMs, over 5 days,12,13 to yield sufficient data for calculation of mean, SD, and CV values, as described in the previous article.4 This is the simplest and most common approach to statistical QC. Although the target mean provided by the manufacturer of the QCM is used to determine the bias of the instrument for each assessed level of QCM, the instrument mean, SD, and CV are derived solely from in-clinic measurements.
As noted previously, when performing statistical QC, control rules are chosen that will provide a high (at least 85%) probability of error detection and low (< 5%) probability of false rejection of a valid test result. Use of the 1-3s control rule has been recommended for point-of-care instruments and will be used in the procedures described here as an assumed starting point for the statistical QC of in-clinic instruments.10,13 The 1-3s control rule, based on the use of control limits that represent 3 SDs from the calculated mean of a single QCM, provides a probability of error detection > 85% and a probability of false rejection of 0%.
To use this table, identify the TEa closest to the recommended value for the applicable measurand.15,16 Then compare the bias and CV, as calculated from QCM results for the most clinically relevant level of QCM for that measurand, with the (absolute value) bias and CV percentages provided in this table. If both the calculated CV and bias are within the ranges given in any 1 row for that TEa, then the 1-3s rule can be used and control limits can be calculated. If the CV and bias are not within the ranges of any of the rows provided for that TEa (both must conform in a single row), then an alternative analytical method must be used, the TEa must be relaxed, or the measurand should be controlled through use of nonstatistical QC.14
If the 1-3s rule is found to be suitable as per Table 1, then the next step in statistical QC is to calculate the control limit range for each measurand by use of the previously calculated mean and SD as follows:
Once control limits have been determined for a given measurand, they should be used to replace the acceptable ranges supplied by the manufacturer. This is because the manufacturer's acceptable ranges are often derived from data for multiple instruments, whereas control limits derived from an individual instrument are more specific to that instrument. Subsequent daily QCM measurements should fall within these limits. In some situations, in-clinic laboratory personnel may need to consult with a veterinary clinical pathologist or laboratory expert with experience in statistical QC if there are questions about the appropriateness of the chosen control limits. These experts may include members of the Quality Assurance and Laboratory Standards Committee of the American Society for Veterinary Clinical Pathology, members of the Laboratory Standards Committee of the European College of Veterinary Clinical Pathology, or professional laboratory personnel at various commercial, university, or government laboratories.
Once control limits for laboratory measurands have been established, routine recording of QCM data should be performed each day that patient samples are run for that test. Some instruments have a QC mode, into which QCM target values are uploaded by the user for each new QCM lot, either manually or by barcode transfer. The QCM measurements are recorded within this mode and presented as a chart (eg, Levey-Jennings chart) that usually displays the QCM target mean, manufacturer's acceptable range, and individual data points at each level of QCM tested (Figure 3). The measured QCM data mean, SD, and CV are often also reported by the instrument. It is important to note that for many point-of-care devices, and even for many reference laboratory instruments, the manufacturer's acceptable ranges programmed into the instrument cannot be replaced, necessitating the creation of novel charts or spreadsheets for statistical QC of every measurand by use of the more accurate calculated control limits. Online tools,17 spreadsheets (eg, Supplementary Appendix S1), or manual forms can be used to generate Levey-Jennings charts. A single chart typically records 1 week or 1 month of control data with a graphic representation of the measured results, compared with the determined control rule. A Levey-Jennings chart of the sample level 1 QCM data for serum albumin concentration, as generated by use of an online tool,17 is provided (Supplementary Figure S1, available at: avmajournals.avma.org/doi/suppl/10.2460/javma.258.7.733).
Statistical QC is a valuable tool in a total quality management system for any medical laboratory. It provides an evidence-based method to show that laboratory system performance is stable and trustworthy over time. Many aspects of statistical QC that have been described in this article can be implemented in the in-clinic laboratory and are further elaborated in the cited references. Use of a checklist may be helpful in ensuring that various aspects of statistical QC have been addressed (Appendix; Supplementary Appendix S2, available at: avmajournals.avma.org/doi/suppl/10.2460/javma.258.7.733).
Offering a systematic description of both traditional and newer SPC methods, this book is ideal as a primary textbook for a one-semester course in disciplines concerned with process quality control, such as statistics, industrial and systems engineering, and management sciences. It can also be used as a supplemental textbook for courses on quality improvement and system management. In addition, the book provides researchers with many useful, recent research results on SPC and gives quality control practitioners helpful guidelines on implementing up-to-date SPC techniques. 59ce067264