Course Description

This course focuses on the planning and application of appropriate quality control processes for laboratory analyses. The course asks the questions “How do you know what level of quality you have (and) what level of quality you want?” and then explores the parameters involved in identifying and monitoring the level of quality for any given laboratory. Core course content will explore the selection, implementation, strengths and weaknesses of appropriate QC rules/multirules and strategies to maintain desired quality goals. Proficiency Testing as it relates to Quality Control programs along with other benchmarks for setting quality standards such as biological variation and application of six sigma QC modelling for lab data, will be discussed. Student electronic exercises, assignments, case studies, spreadsheet templates and QC software will be used to apply the theoretical concepts discussed to practical laboratory data analysis situations.

Credits/Modes of Instruction

Credits:  3

Participants should anticipate spending approximately 135 hours on the course, or an average of 9-12 hours per week. This includes time spent logging on to units and participation in discussion threads. It also includes time needed to do reading, internet assignments, written assignments, courseware readings and self-assessments and time spent studying and taking quizzes and exams. Please note that the 9-12 hours a week is an average: some weeks you may need less time, other weeks more.

Prerequisites

Baccalaureate degree with previous course work in statistics and work experience in a clinical laboratory required.

Course Goals and Outcomes:

Goals

Provide students with a stable foundation with which to identify and implement a well- informed quality control program.
Stimulate the students to ask and to provide processes to answer the question: “What level of quality control is desired and attainable for the clinical laboratory?"
Enable students to understand and apply the concepts discussed in the course including: predictive value theory, method evaluation, performance-driven quality control, risk management, QC rules and strategies, proficiency testing, and benchmarking quality.
Familiarize students with applicable statistical and QC software programs.
Provide practical applications of the use of various statistical parameters so that students can more intelligently examine / evaluate the strengths and weaknesses of any laboratory's QC systems.
Equip students to be able to read and examine the statistics in a journal article.

Upon completion of the course, the student will be able to:
Appropriately perform and utilize calculations relating to clinical laboratory quality control data.
Correctly interpret in-control and out-of-control situations. Evaluate selected laboratory data in terms of clinical sensitivity, specificity and predictive value.
Understand the reasons for performing method validation experiments and be able to follow the process for each one.
Correctly apply QC rules and strategies to laboratory data.
Relate the concepts of bias, precision and total allowable error in developing QC criteria.
Select the best rules and strategies for optimum quality management of laboratory processes.
Describe how proficiency testing can be a vital component of quality management.
Explain the concept of biological variation (BV) and how it affects interpretation of results.
Describe how laboratory quality can be benchmarked.
Apply the concept of performance driven quality control (PDQC).
Use various software to evaluate data and simulate changes in data sets.
Determine the acceptability of risk.