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Statistical quality control: error identification and control procedure complexity

In this fifth article in a series on internal quality control, Stephen MacDonald focuses on the importance of the methods to detect, at an early stage, potentially medically important errors, which is the cornerstone of what is hoped to be achieved.

In the first four articles of this series, statistical quality control (SQC) was introduced along with model properties of internal quality control (IQC) materials. Performance specifications were defined, chosen and implemented. In the next stage of design, we must ensure we detect non-conformity to expected performance. There is a fine balance between error detection and false alarms. This balance is maintained using quality control (QC) rules. The challenge we face in the laboratory is deciding what rules best suit each process. We need to:

  • calculate clinically significant error required to be detected
  • calculate analytically significant error required to be detected (imprecision and bias)
  • determine the extent of control that is required to detect the above
  • apply rules to that process to maximise detection
  • determine current performance.

During process stability, we expect our IQC to perform as show

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IBMS Biomedical Science Congress 2022

International Convention Centre, Birmingham
14-17 March 2022

Access the latest issue of Pathology In Practice on your mobile device together with an archive of back issues.

Download the FREE Pathology In Practice app from your device's App store

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