8. Quality control

established or accepted reference values. Too many methodical problems arise during the determination of the outliers; some methods developed in 2005 seems to be the solution for it [8].

Universally, healthy patients depict entirely different lab values from the patients having epidemics. Nonetheless, lab values typically overlap, especially between various populations. Diagnostic efficiency is the key determinant regarding the appropriateness of a test at detecting and foretelling the prevalence of a disease. Diagnostic efficiency can encompass predictive values, specificity, and sensitivity. Diagnostic sensitivity entails the potential of a test regarding detection of a certain condition, whereas diagnostic specificity involves a test's potential to correctly detect the absence accruing to a certain condition or disease [10]. A positive predictive value depicts the probability of a person having a certain disease or condition once the test is not normal, whereas negative predictive value depicts a chance for an individual not having a certain condition or disease once the test is in the reference interval. The measures of diagnostic efficiency quantify the usefulness of a test regarding a certain condition or disease. Analytical sensitivity entails the lower extent of detection regarding a certain analyte, while clinical sensitivity encompasses proportion of people who test positive to show the presence of the underlying disease. True positives (TPs) are the patients confirmed by the test to have a certain disease, while those classified as not having the condition are false negatives (FNs). Contrary to specificity and sensitivity, predictive values rely on the condition's prevalence in the population under study. Measures of the diagnostic efficiency entirely rely on the distribution accruing to test outcomes for the TPs and FNs and the cutoff utilized in defining abnormal extents. Definition of effective cutoff necessitates laboratorians to fre-

Many parametric and nonparametric methods are available for this [6–9].

quently deploy a graphical tool—the ROC (receiver operator characteristic) [11].

The value accruing to medical lab service depends on its potential to offer accurate and reliable test outcomes. Method evaluation targets at the production of outcomes within clinically acceptable error to assist physicians to optimally merit their patients. Regarding the regulatory issues of method evaluation, the Centers for Medicare and Medicaid Services (CMS) and the FDA outstand as the key government agencies influencing lab testing approaches in the USA. Invariably, the FDA controls lab reagents and instruments, while the CMS controls the Clinical Lab Improvement Amendments (CLIA) [12]. Nevertheless, method selection entails gathering the technical information linked to the test, its scientific literature, and presentations. Key reasons for selecting a new approach to entail a reduction of costs, improving efficiency and quality of outcomes besides amplifying client satisfaction. A method pre-evaluation follows

5.3.4. Establishment of the reference limits

6. Diagnostic efficiency

16 Quality Control in Laboratory

7. Method evaluation

QC entails the systematic tracking of the analytic procedures in the lab to detect the analytic errors that prevail during analysis and finally curb reporting of incorrect test outcomes. An analytic approach is functioning optimally if the expected values lie within the underlying control limits. QC materials entail the specimens that are analyzed for QC functionality, and they ought to be of the similarity matrix as the tested specimens. Additionally, QC charts graphically denote the control material's observed values over time within the control limits. Multi-rule simplifies the various control rules to judge if an analytic approach is within the control or not. Proficiency testing is key to validating key measurement processes.
