Related Experiment Video
Updated: Aug 14, 2026

Procedure and Key Optimization Strategies for an Automated Capillary Electrophoretic-based Immunoassay Method
Published on: September 10, 2017
Selection of medically useful quality-control procedures for individual tests done in a multitest analytical system
D D Koch1, J J Oryall, E F Quam
1Department of Pathology and Laboratory Medicine, Medical School, University of Wisconsin, Madison 53792.
Abstract:
Quality-control (QC) procedures (i.e., decision rules used, numbers of control measurements collected per run) have been selected for individual tests of a multitest analyzer, to see that clinical or "medical usefulness" requirements for quality are met. The approach for designing appropriate QC procedures includes the following steps: (a) defining requirements for quality in the form of the "total allowable analytical error" for each test, (b) determining the imprecision of each measurement procedure, (c) calculating the medically important systematic and random errors for each test, and (d) assessing the probabilities for error detection and false rejection for candidate control procedures. In applying this approach to the Hitachi 737 analyzer, a design objective of 90% (or greater) detection of systematic errors was met for most tests (sodium, potassium, glucose, urea nitrogen, creatinine, phosphorus, uric acid, cholesterol, total protein, total bilirubin, gamma-glutamyltransferase, alkaline phosphatase, aspartate aminotransferase, lactate dehydrogenase) by use of 3.5s control limits with two control measurements per run (N). For the remaining tests (albumin, chloride, total CO2, calcium), requirements for QC procedures were more stringent, and 2.5s limits (with N = 2) were selected.
Related Concept Videos
Development of Analytical Methods
Quality Control
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
Data Validation
Key parameters for method validation include:
Clinically Relevant Drug Product Specifications: Methods of Establishment
Automated Microbial Diagnostics

