No matter how exact and reliable a measurement is, there is always some degree of ambiguity. When the likelihood of an event happening is unclear, the situation is referred to as uncertain. Another way to describe it is a lack of confidence in something. The range of values assigned to a measured quantity is determined by a characteristic called measurement uncertainty, which is related to the results of measurement processes like testing and calibration. A qualitative indicator's measurement is subject to measurement uncertainty, which is a quantitative outcome. The thing that's supposed to be measured is referred to as a measurand.
Assuming that all measurements are subject to error, there are numerous potential sources of variation. Laboratories are required to list every factor, including materials and equipment, negligence, failure to account for a factor, and environmental factors, that contributes to measurement uncertainty (humidity, change in temperature, air pressure, etc.). Those components will be found by applying the proper analytical techniques. The quality of the measurement execution can occasionally affect the outcomes of the measurement. There are two different ways of measuring uncertainty:
- Type A (Top-down) – uses numerical probability distribution.
- Type B (Bottom-up) – uses presumed probability distribution.
The methods may be applied individually or together. However, these methods are frequently combined to provide a precise measurement. Because the measurement might be repeated and the uncertainty report will be shared with the client, laboratories should report and assess the outcomes of measurement uncertainty.
Recognize the consequences of laboratory errors
An incident known as a laboratory error has a detrimental effect on the lab and its operations. Numerous elements could result in a mistake. The most typical root causes of mistakes are:
- Inappropriate equipment uses
- Untrained workforce
- Unfollowed procedures
- Lack of reports/documents
- Incorrect testing results
- Others
The outcomes of laboratories are dependent on the accuracy and dependability of calibration and measurement findings since they work hard to create reliable results that are used by a clinic or any other public health institution. Additionally, they will experience serious repercussions if they get inaccurate data or outcomes. Complicated and inadequate therapy, delays in providing an accurate diagnosis, and recurrent testing are some examples of such effects (measurement will be repeated). Therefore, this has a negative impact on results, expenses, and most crucially, consumer confidence.
Every laboratory that intends to achieve the highest level of reliability and appropriateness of results/data must make sure to follow all the requirements of ISO/IEC 17025 and other established procedures. Since laboratories perform many activities, their system is complex; hence, to improve their performance, they should function in line with ISO/IEC 17025 requirements and established procedures. Additionally, Measurement Uncertainty Training can be helpful and aid in preventing all of the consequences of laboratory mistakes.
Why Measurement Uncertainty is Important?
Risk analysis and decision-making depend heavily on measurement uncertainty. Using reports with quantitative measurement data, organisations make decisions every day. The danger of making a bad judgement increases if measuring data is inaccurate. Inappropriate supplier selection could lead to a subpar product. Misdiagnosis in medicine could occur if the wrong laboratory is chosen. Financial goals may be harmed by poor investing choices. These instances show how measuring outcomes affect judgements in every one of them. Organizations and people would be more confident in their judgements if they could evaluate the calibre of the measurement outcomes.
The secret to lowering costs and managing risks is to improve quality. However, a factor that is frequently ignored is measurement uncertainty. It is a crucial component of measurement that has an impact on risks, decisions, prices, and quality. The requirement to measure quality is more crucial than improving accuracy. Only sufficient accuracy should be used to successfully meet each organization's set requirements. To evaluate if the results are of high enough quality to satisfy the established accuracy standards, measurement uncertainty should be taken into account. Organizations and individuals can benefit from greater quality while lowering cost and risk through awareness and education.
