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Internal quality control: best practice
  1. Helen Kinns1,
  2. Sarah Pitkin2,
  3. David Housley1,
  4. Danielle B Freedman1
  1. 1Department of Clinical Biochemistry, Luton and Dunstable University Hospital NHS Trust, Luton, UK
  2. 2Department of Clinical Biochemistry, Barts Health NHS Trust, London, UK
  1. Correspondence to Dr Helen Kinns, Department of Clinical Biochemistry, Luton and Dunstable University Hospital NHS Trust, Lewsey Road, Luton, LU4 ODZ, UK; helen.kinns{at}ldh.nhs.uk/David.Housley{at}ldh.nhs.uk

Abstract

There is a wide variation in laboratory practice with regard to implementation and review of internal quality control (IQC). A poor approach can lead to a spectrum of scenarios from validation of incorrect patient results to over investigation of falsely rejected analytical runs. This article will provide a practical approach for the routine clinical biochemistry laboratory to introduce an efficient quality control system that will optimise error detection and reduce the rate of false rejection. Each stage of the IQC system is considered, from selection of IQC material to selection of IQC rules, and finally the appropriate action to follow when a rejection signal has been obtained. The main objective of IQC is to ensure day-to-day consistency of an analytical process and thus help to determine whether patient results are reliable enough to be released. The required quality and assay performance varies between analytes as does the definition of a clinically significant error. Unfortunately many laboratories currently decide what is clinically significant at the troubleshooting stage. Assay-specific IQC systems will reduce the number of inappropriate sample-run rejections compared with the blanket use of one IQC rule. In practice, only three or four different IQC rules are required for the whole of the routine biochemistry repertoire as assays are assigned into groups based on performance. The tools to categorise performance and assign IQC rules based on that performance are presented. Although significant investment of time and education is required prior to implementation, laboratories have shown that such systems achieve considerable reductions in cost and labour.

  • Quality Control
  • Quality Assurance
  • Laboratory Management

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