Although similar, there are distinct differences between the two concepts. This page will explain the differences between quality control and quality management, and provide definitions and examples of each.
Quality assurance and quality control are two aspects of quality management. While some quality assurance and quality control activities are interrelated, the two are defined differently. Typically, QA activities and responsibilities cover virtually all of the quality system in one fashion or another, while QC is a subset of the QA activities.
Purchase ISO Published hard copy PDF e-standard. See training courses for these crucial functions:. Quality control training Quality assurance training. Quality assurance can be defined as "part of quality management focused on providing confidence that quality requirements will be fulfilled. An alternate definition is "all the planned and systematic activities implemented within the quality system that can be demonstrated to provide confidence that a product or service will fulfill requirements for quality.
Quality control can be defined as "part of quality management focused on fulfilling quality requirements. An alternate definition is "the operational techniques and activities used to fulfill requirements for quality. For some service organizations, the concept of quality control may be foreign because there is no tangible product to inspect and control.
The quality assurance function in a service organization may not include quality control of the service but may include quality control of any products involved in providing the service. In most laboratories, the QC program in the laboratory computer system will calculate the control data captured on-line or through manual entry. The QC programs incorporated in instrument systems and some Point-of-Care devices have similar capabilities. Stand alone QC programs on personal computers are also available and offer complete support for calculations, graphic displays of control charts, and storage of results.
Participants in external survey programs offered by instrument or control manufacturers can also submit their control data for analysis by the vendors, though the data analysis may require up to a month for return of the results. The mean value for a control material provides an estimate of the central tendency of the distribution that is expected if method performance remains stable. Any change in accuracy, such as a systematic shift or drift, would be reflected in a change in the mean value of the control, which would be shown by a shift or drift of the distribution of control results.
Always keep in mind that the mean is related to accuracy or systematic error and the standard deviation is related to precision or random error. See QC - The Idea for a review of how the mean of the distribution of control results is related to the mean and control limits on a control chart.
The standard deviation is determined by first calculating the mean, then taking the difference of each control result from the mean, squaring that difference, dividing by n-1, then taking the square root. All these operations are implied in the following equation:. For computerized calculations and for estimating the cumulative standard deviation, the form of the equation that is commonly used is:. It is easy to use a scientific calculator, an electronic spreadsheet, or a statistics program, all of which have built-in functions for calculating the standard deviation of a group of measurements.
This function for calculating the standard deviation is often labeled "SD". Specialized QC software in laboratory information systems, instruments, and personal computer workstations will automatically calculate the standard deviation for the data being accumulated. External quality assessment programs offered by manufacturers of instruments and control materials will also process the data of participants and provide reports that include the calculated results.
The standard deviation is related to the spread or distribution of control results about the expected mean. Whereas the mean is an indicator of central tendency and therefore related to accuracy or systematic error, the standard deviation is a measure of the width of the distribution and is related to imprecision or random error. The bigger the standard deviation, the wider the distribution, the greater the random error, and the poorer the precision of the method; the smaller the standard deviation, the narrower and sharper the distribution, the smaller the random error, and the better the precision of the method.
For a measurement procedure, it is generally expected that the distribution of control results will be normal or gaussian, as shown above. For a gaussian distribution, the percentage of results that are expected with certain limits can be predicted. For example, for control results that fit a gaussian distribution, it would be expected that CV refers to the "coefficient of variation," which describes the standard deviation as a percentage of the mean, as shown in the following equation:.
The standard deviation of a method often changes with concentration, i. Because the CV reflects a ratio of the standard deviation to the concentration, it is often provides a better estimate of method performance over a range of concentrations. This is the reason that QC planning applications with the QC Validator program use a percentage figure for the imprecision of the method. Given the mean and standard deviation for a control material, control limits are calculated as the mean plus and minus a certain multiple of the standard deviation, such as 2s or 3s.
As a rule of thumb, the control results and the calculated standard deviation should have at least one more significant figure than needed for clinical significance of the patient test result; the mean of a control material should include at least two more significant figures than needed for clinical signficance of the patient test result. When in doubt, carry more significant figures than necessary and round at the end when the control limits have been calculated.
Most calculators and computers carry plenty of extra figures so you can round at the end. Typically, control results are summarized by calculating the mean, standard deviation, CV, and N on a monthly basis. In order to establish longer term estimates of the mean and standard deviation, the control data or calculated results need to be accumulated to describe performance observed over a longer periods of time.
Longer term limits are often described as "cumulative limits," which indicates they have been calculated from cumulative means and standard deviations. These may also be referred to as "lot to date" limits when these calculated values are provided by a manufacturer or supplier who processes the control data for a group of laboratories in order to provide information about the comparative performance between laboratories and between methods. This is a long term estimate of a method's precision performance based on a large number of control measurements collected over a long period of time.
A long period here is at least two months and could be several months, even a year. These calculations are often automatically performed by the QC programs in laboratory computer systems, personal computer work stations, and in many automated instruments and even some point-of-care devices.
If you need to perform these calculations yourself, one practical approach is to calculate monthly statistics, then tabulate the month n's, x i and x i 2 , which can then be totaled and used in the equation below to provide the cumulative estimate:. This is a long term estimate of the central tendency observed for a control material based on a large a number of control measurements collected over a long period of time.
Changes in the accuracy of a method could lead to shifts or drifts in the mean observed for a control material. From the monthly statistics that are calculated, tabulate the monthly n's and x i 's, which can then be totaled for the period of interest two months, several months , and used in the equation below to provide the cumulative mean:.
While poor-quality test results are not attributed directly to medical errors, laboratory results certainly are part of the problem. It is estimated that as many as three-quarters of clinician decisions are based on laboratory tests [7]. Because quality test results are such an important component of healthcare, many governments and professional laboratory organizations around the world specify a series of good laboratory practices to ensure quality laboratory results.
Especially two ISO standards are relevant: 1 ISO , Medical laboratories — particular requirements for quality and competence, and 2 ISO , Point-of-care testing - requirements for quality and competence [12, 13]. Some countries even have made local adaptations of these two standards mandatory for test sites to follow.
The ISO standards were developed by experts from 33 countries and reflect worldwide opinion on what is essential to ensure quality specifically for clinical laboratory testing. The characteristics of useful, accurate, precise, reliable, and timely apply to all quality test results including blood gas and critical care results. Ultimately for laboratories to meet all these demands, QC assessment for ongoing quality assurance is essential!
A recent essay by Dr. Westgard discusses how laboratories often operate on false assumptions [15]. Despite their desire for the perfect, error-proof instrument that always yields perfect results, such an instrument does not exist!
What follows focuses on the importance of routine QC to ensure the quality of the analytical phase of testing 9 of the essential elements in Table I for blood gas and critical care measurements. Typically laboratories statistically evaluate QC data to determine whether instrument performance is within the expected variation [16]. While QC is essential for all laboratory measurements, QC assessments for blood gas and critical care measurements are particularly important because patients requiring these measurements are critically ill and in need of immediate treatment based on these test results.
Wrong results can be fatal! Consequently QC is absolutely necessary and should prequalify the instrument to ensure proper performance before the patient sample is analyzed. For most quantitative tests, CLIA requires the analysis of at least two different concentrations of QC materials on days when patient testing is conducted. CLIA also requires analysts to review the QC results before reporting patient results to ensure that only patient results within quality specifications are reported.
All unacceptable QC results must be investigated and appropriate corrective actions taken before reanalyzing samples and reporting patient results. ISO , in section 5. Test sites must design QC practices to ensure that all patient results meet the stated quality goal. Both ISO standards require corrective actions when QC results are unacceptable and mandate the review of QC data as part of ongoing quality assurance activities to detect and prevent potential errors. The analysis of three levels of QC verifies the position of the calibration curves across the measurement ranges.
Because these QC and calibration practices are requirements and reflect recognized good laboratory practices, several manufacturers now design instruments to automatically perform calibration at specified intervals and analyze, typically, three levels of QC each day [].
While all laboratories in the U. Accreditation will be mandatory by the end of The French standards mandate each laboratory to implement an internal quality program section 5.
The analysis of QC materials is mandatory for blood gas testing and two levels per day are recommended. Additionally, many laboratories throughout the world voluntarily seek formal accreditation from professional organizations for further recognition of their ability to provide quality testing. Over a 14 month period, up to questionable HIV and hepatitis test results were reported despite QC results indicting analytical errors.
The safety of all these patients was jeopardized. Many of the patients tested during this period were misdiagnosed based on the erroneous results and all of these patients required reassessment. As a consequence for not following testing requirements, numerous personnel were prosecuted and sanctions were placed on the hospital. Quality — useful, accurate, precise, reliable, and timely tests results — is essential for providing patients with the best possible healthcare. However, even under the best conditions, errors can and do happen!
Consequently, laboratories must plan for quality. All of these standards mandate ongoing QC measurements to evaluate analytical quality. Quality test results are not automatic. Because of the criticality of blood gas and critical care measurements, QC must prequalify the instrument before patient samples are analyzed to avoid delays due to instrument problems, reporting incorrect results, and collecting additional patient samples for reanalysis.
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