6. Diagnostic efficiency

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

which involves analysis of several standards with an aim of verifying the replicate analysis and linear range of two controls in order to acquire estimates regarding short-term imprecision. Inaccuracy and imprecision should be compared to the highest allowable error linked to medical criteria wherein acceptability prevails when the estimates are below the allowable highest error. After determination of imprecision, accuracy can be estimated via recovery, interference, and the patient-sample comparison. The key aspect regarding method evaluation entails determining whether the total error (systematic and random errors) does not exceed the allowable analytic error [13, 14]. The CLIA publishes the allowable analytic errors by the

Figure 2. Graphic representation of (A) imprecision and inaccurate, (B) accurate but imprecision, and (C) precise and

The Basic Concepts of Quality Control Reference: Interval Studies, Diagnostic Efficiency, and Method…

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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

Regarding quality improvement, Lean Six Sigma offers an infrastructure and methodology for quality enhancement. Additionally, define, measure, analyze, improve, and control (DMAIC) approach facilitates quality promotion. Regarding metrics, Lean Six Sigma targets at reducing cycle time, whereas Six Sigma targets at reducing error. Combining both ideologies yields a

control or not. Proficiency testing is key to validating key measurement processes.

synergetic positive influence on the quality and process performance [15].

federally mandated proficiency examination (Figure 2).

8. Quality control

accurate [1].

9. Quality management
