This program focuses on the principles and applications of good statistical quality control (QC) practices. Its aim is to explain how QC works, explain what errors interpretive rules are designed to detect, and suggests appropriate investigations for QC failures. Minimal mathematical or statistical theories are presented, as the emphasis is on practical and implementable practices and an explanation of why these practices are suggested.
The content is primarily geared towards laboratory supervisors, managers and technologists in clinical laboratory settings. Trainees in laboratory medicine and laboratory directors may also benefit from this program.
Quality control topics not expressly covered in this certificate program include: an Individual Quality Control Plan (IQCP), control processes that may be "built in" to a measurement system, preparing in-house QC material, using patient results as a QC parameter, or assessing if a measurement procedure is fit for its intended use in clinical care.
The program is composed of seven courses, listed below. Each course can be completed online in approximately 1-2 hours and contains a lecture, readings, resources, and a quiz
Overview and Basic Concepts of Quality Control
Greg Miller, PhD, DABCC. Virginia Commonwealth University, Richmond, VA
Explains the basic terminology used in QC, discusses how the medical requirements of test results are established, describes measurement procedure performance, and explains the statistical quality control process.
Establishing QC Parameters for a Test Procedure
M. Laura Parnas, PhD, DABCC, FACB. Sutter Health, Livermore, CA
Discusses considerations to select QC materials, explains the process to establish control values, lists five sources of variability when establishing control values, and describes the steps necessary to evaluate a new reagent lot.
Using QC to Assess Performance of a Test Procedure
David G. Grenache, PhD, DABCC, FACB. University of Utah and ARUP Laboratories, Salt Lake City, UT
Lists four possible outcomes of a QC measurement, describes common QC rules, explains a power function graph and how it can identify appropriate QC rules, and describes the cumulative sum procedure which can be used to identify analytical trends.
Frequency to Measure QC Samples
Curtis Parvin, PhD. Bio-Rad Laboratories, El Paso, TX
Explains how to implement QC strategy design principles, explains QC strategy considerations, explains QC scheduling risk and the risk of unacceptable patient results, and describes how to implement effective QC scheduling practices.
Responding to Out of Control Situations
Nikola Baumann, PhD, DABCC. Mayo Clinic, Rochester, MN
Investigates QC failures, establishes a process for responding to QC failures, and identifies patient results that need to be corrected after a QC failure.
Reviewing QC Data
David G. Grenache, PhD, DABCC, FACB & Nikola Baumann, PhD, DABCC
Describes the parameters that should be included in the daily, weekly, and monthly review of QC data, explains the management of QC for multiple analyzers, and explains how to utilize ongoing assessment of a QC program to optimize error detection.
External Quality Assessment/Proficiency Testing
Greg Miller, PhD, DABCC
Explains the value of external assessment, explains the limitations of proficiency testing, describes how to appropriately measure and report results from EQA/PT, and explains how to correctly interpret results from an EQA/PT program.
Developed in cooperation with the AACC Management Sciences and Patient Safety Division, and supported by Bio-Rad Laboratories.
Program Launch Year: 2014
Course Number: 12740