This introductory course focuses on how lab results impact patient care, what
can cause 'bad' results and how well-designed QC processes detect changes before
'bad' results are reported.
After successful completion, you will be able to describe, practice,
and demonstrate competence to:
- Categorize changes in method accuracy or precision as "clinically-significant" change that may cause patient harm or "clinically-insignificant" change that does not impact clinical decision
- Use a lookup table to predict the % of results
expected to fall within or beyond specified SD limits of the mean-- on a
Gaussian curve and Q.C. chart.
- Describe why Q.C. Charts are limited to the detection
of changes in accuracy or precision; why they do not assess method bias, or
ability to meet performance standards.
- Relate clinical consequences to patients to method
errors with Accuracy, Precision, Reference intervals, Reportable Ranges,
Specificity, and Sensitivity.
- List clinical consequences of false positive or false
negative patient results (too far below or above their true value to be
- Use a Q.C. Simulator to model the impact of changes in
method accuracy or precision.
- Create case studies to illustrate why a 2-SD shift
always looks the same on the QC chart but may be a change for the better or it
may create clinically-unacceptable results.
- Identify patterns on Q.C. charts as changes in
accuracy or precision.
- Describe probable causes of changes in method accuracy
- Use simple math you can do in your head to calculate
bias and total error.
- Compare Total Error to TEa Limits to assess ability of methods to meet
performance standards and acceptability to report patient