Basic Planning for Quality Lessons
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QP 15: Frequently-Asked-Questions about Quality Planning |
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QP 14: What's wrong with statistical quality control? |
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QP 13: Coagulation Applications |
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QP 12: Immunoassay Applications |
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QP 11: Blood Gas Applications |
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QP 10: Automated Chemistry Applications |
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QP 9: Practice makes Proficient |
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QP 8: Implementing a manual Quality Planning process with Normalized
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QP 7: Formulating a TQC Strategy |
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QP 6: Using OPSpecs to plan Quality |
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QP 5: Defining Quality Requirements |
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QP 4: Devising a Practical Process |
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QP 3: Complying with Regulations, Standards & Practices Guidelines |
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QP 2: Assuring Quality through Total Quality Management |
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QP 1: A Wake-Up Call for Quality Management |
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Z-Stats - Basic Statistics - Lessons
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Z-1: Aligning attitudes through purpose |
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Z-2: An organizer of statistical terms, Part I |
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Z-3: An organizer of statistical terms, Part II |
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Z-4: Mean, standard deviation and coefficient of variation |
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Z-5: Sum of squares, variance, and the standard error of the
mean |
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Z-6: Probability and the standard error of the mean |
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Z-7: Hypothesis testing, tests of significance, and confidence
intervals |
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Z-8: Two-Sample and Directional Hypothesis Testing |
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Z-9: Truth or consequences for a statistical test of significance |
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Z-10: ANOVA - The Analysis of Variance |
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Z-11: Confidence intervals |
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Z-12: Correlation and least squares analysis |
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Z-13: The Least Squares Regression Model |
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Z-14: Estimating analytical errors using regression statistics |
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Advanced Quality Management: Six Sigma
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Six Sigma Basics
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Just what is Six Sigma? What has it accomplished? Does it
apply to healthcare laboratories? Do I really have to
learn it? All these questions (and more) are answered in this
lesson. This is a gentle introduction to Six Sigma -- in fact,
this lesson provides a "statistic-free" description
of Six Sigma.
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Six Sigma: Outcome measurement of
process performance
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Six Sigma saves you money when you can reduce defects. But
how do you find those defects? Dr. Westgard gives you a step-by-step
method of analyzing your tests, identifying waste, calculating
sigma-metrics, and more.
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Six Sigma: Quality Design and Control
Processes
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Six Sigma tells us that well-designed processes will reduce
waste, boost efficiency, and increase profits. But how do we
"design well" in the laboratory? How do we convert
our customer feedback into useful quality requirements and method
specifications. Dr. Westgard provides step-by-step guidelines,
graphic tools, and advice.
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Six Sigma: General attributes of
the OPSpecs Design Tool
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The OPSpecs chart (operating specifications) is an ideal Six
Sigma tool. It allows us to easily identify how our laboratory
processes are performing -- and gives us immediate guidance on
where dramatic improvements can be made and what impact those
improvements will have. Whether your're trying to establish performance
specifications for imprecision and inaccuracy for a method, or
just trying to pick the right control rule for a test, the OPSpecs
chart is your best pick.
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From Method Performance Claims to Six Sigma Metrics
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How can you get a definitive conclusion on the performance of a new instrument, based on just the performance claims
provided by the manufacturer? With a fusion of QC Design, Method Validation, and Six Sigma. You can translate
performance claims into Six Sigma metrics. This lesson shows you how, and provides all the calculators you need.
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Other Lessons on Quality Issues
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What is QC Validator?
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QC Validator is a software program that chooses control rules
to meet any quality requirement you specify. Version 2.0, described
in this lesson, has automatic QC selection with user-defined
selection criteria and logic. If you want to know more about
its features and possibilities, read this lesson.
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Starting a QC Planning Process
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How do you plan quality? Not by throwing darts at a Levey-Jennings
charts. You have to choose methods, numbers of materials and
controls, control rules, and more. You need a planning process
for your QC. Here's the one that Dr. Westgard recommends
Read this lesson
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Power Function Graphs
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Power Function Graphs are statistical tools. They reveal the
performance of the statistical rules used in the laboratory.
Why do those 1:2s rules have so many repeat runs? One power function
graph will explain it to you.
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Critical-Error Graphs
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Want to know just how much your error detection is? What precisely
your false rejection is? For all the methods in your laboratory?
Critical-Error graphs are powerful tools that tell you quickly
how well your method is performing.
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OPSpecs (Operating Specifications)
Charts
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What if there was a chart that allowed you to choose a rule
that would guarantee you met the quality you specify, just by
figuring out if a point is above or below a line? OPSpecs Charts
allow you to choose the methods for your laboratory by simple
visual inspection. If you know your bias, your CV, and the quality
you need to acheive, you can pick the best control rule in less
than a minute!
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Quality Planning Models
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How sophisticated are your quality planning needs? Does your
quality take imprecision (CV) and inaccuracy (bias) into account?
That's an analytical quality requirement. Does your quality take
biological variability of the patient, as well as the physician's
clinical decision interval, into account? That's a clinical decision
interval. Learn the differences between analytical and clinical
quality planning models, as well as the advantages and disadvantages
of these approaches.
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Total Quality Control (TQC) Strategies
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You've got some methods in your laboratory that you don't
have to worry about (right?). Then there are some that from time
to time, have "issues." And then there are the persistently
difficult methods, where it's always out-of-control and you don't
know where the problem lies. And you have to make sure your scarce
personnel are making all these methods work? Here's a
new way to do it: a TQC strategy. Using TQC strategies, you'll
know what to do for every method, when to do it, and when to
move on.
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Normalized OPSpecs Charts
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Are there normal and abnormal OPSpecs charts? No. A "normalized"
chart is specially created so that you can use it for all methods,
regardless of their quality requirements. It's one-stop shopping
for OPSpecs charts - making it even easier to choose control
rules for your laboratory methods. Read this lesson and then
use the online calculator to see how it works - quickly, easily,
and hassle-free.
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QC Selection Grids
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QC Selection Grids are quick planning guides for your single
rule or multirule selection. If you know how often you have problems
with a method (and you do know, don't you?) and can calculate
the method's critical-error (we give you an online calculator
to do it for you), then you can find out the best rule for your
method.
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A new tool from Westgard QC:
The Automatic QC Selection Engine
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For years, QC Validator has been automatically choosing the
best control rules and number of measurements and materials for
laboratory tests. Now it's available in a new format: a dynamic
library tha can be embedded in other software programs. So if
you want your LIS or your instrument or your POC device to automatically
plan your quality, read this lesson to find out how...
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The "Area Under a Table": Assessing the probabilities of rejection for QC Procedures
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This is a lesson on how to determine the performance of a QC procedure using a table of areas under a normal curve. At the intersection QC Design, Six Sigma, statistics, and QC - you can establish your capability to detect an important medical error
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It's about Time: QC Performance measurements in units of time
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How many runs does it take before your instrument will detect a medically important error? This is a basic question that other industries take great pains to determine - so why is it healthcare laboratories generally don't know the answer? Dr. Westgard explains how this number can be calculated - and how new technologies in the lab are creating a new way to describe the performance of QC procedures
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Lessons on Basic QC Practices
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QC - The Idea
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An introduction to the theory and concepts of quality control
in the healthcare laboratory. Dr. Westgard also gives a preview
of the articles that follow in this critical series on Basic
QC Practices.
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QC - The Levey-Jennings Chart
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This lessons discusses one of the cornerstones of QC practice.
We can no longer take for granted that everyone knows how to
build a control chart, plot the control values, and interpret
those results correctly. Patricia L. Barry, co-author of Cost-Effective
Quality Control: Managing the Quality and Productivity of Analytical
Processes, provides a primer on how to construct, use, and
interpret the Levey-Jennings chart. This article also includes
an online Control Limit calculator!
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QC - The Materials
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Elsa F. Quam, BS, MT(ASCP) is one of our most popular guest
essayists. Elsa rightly points out that while we concentrate
on the statistics of quality control, we can't forget the selection
of the control materials. Important attributes such as the stability,
vial to vial variability, assayed versus unassayed, appropriate
analyte levels, and pretreatment procedures affect the very success
of the control procedure.
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QC - The Calculations
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This lesson discusses the math involved with QC practice.
Despite the age of computers, we still have to crunch the numbers
ourselves sometimes. Dr. Westgard discusses the terms Mean,
SD, CV, Control Limits, z-scores and SDI's, explaining
what they are, giving the equations, and demonstrating how to
calculate them.
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QC - The Chances of Rejection
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Dr. Westgard explains how an analytical testing process works
to reject the bad runs and keep the good runs. False rejection
and error detection are explained. The different kinds of problems
(precision , accuracy, etc.) are also described. If you've ever
wondered whether there was method to your laboratory madness,
this article is for you.
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QC - The Out-of-Control Problem
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What do you do when you're control is out-of-control? Conventional
wisdom is that you repeat the control or try a new one. But that
ignores the problem. It doesn't solve anything. Elsa P. Quam
BS, MT(ASCP) explains what bad habits we have and what good habits
we can adopt to make our laboratory practice better
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QC - The Multirule Interpretation
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Those "Westgard rules" can be confusing. How do
you use them? This lesson combines basic QC theory and practice
to show you how. Dr. Westgard walks you through a Levey-Jennings
chart day by day, plotting the control data and pointing out
which run violates which rule. See how the multirule QC should
be done (and find out if you've been doing it right yourself).
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QC - The Reports
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Tricia Barry MT(ASCP) gives us another valuable article on
the basics of quality control, explaining the who, what, when
and how of recording your QC history -- plus the why we need
to do it. It turns out your records are important - they provide
a voice for the method. If you listen closely, you'll understand
why things are out-of-control sometimes.
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Lessons on Basic Method Validation
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MV - Selecting a Method to Validate
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Before you run all the experiments and crunch all those numbers
and print out the report, make sure you've chosen the right method
to validate. Methods are not one-size-fits-all when in comes
to your laboratory. In this lesson, Dr. Westgard helps you choose
the right method for your unique needs.
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MV - The Experimental Plan
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Ok, so you've chosen the method. Now what do you do? Well,
you've got to run the experiments and crunch the numbers. But
guess what, not every experiment was created equal! Once again,
you must choose carefully and plan out your experiments.
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MV - The Replication Experiment
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No, this doesn't involve making clones (we'll leave that to
the Scots). This article is about one of the gold standards in
method comparison studies. Dr. Westgard explains what this important
experiment is, how you perform and interpret it, as well as how
you can use the results to improve your laboratory. A
Javascript Replication Experiment calculator is included to crunch
the numbers for you
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MV - The Comparison of Methods Experiments
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The comparison of methods experiment is critical for assessing
the systematic errors that occur with real patient specimens.
Guidelines for performing the experiment are provided and there
is an introductory discussion of how to graph the data and what
statistics should be calculated.
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MV - The Data Analysis Tool Kit
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Are you less frightened of statistics when we talk about them
as tools? How about talking about statistics without showing
you any equations? Well, that's what this lesson by Dr. Westgard
does. If you can think about method validation as a job that
needs a set of tools, you're ready to read this article.
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MV - The Decision on Method Performance
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You've crunched numbers, plotted and graphed -- now what do
you do? Keeping in mind the inner, hidden, deeper, secret meaning
of Method Validation, Dr. Westgard explains how to judge your
method performance.
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MV - The Linearity or Reportable
Range Experiment
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Elsa Quam BS, MT(ASCP) -- a frequent contributor to our Basic
QC series -- turns in this essay for our Method Validation series.
It is crucial to know the upper and lower limits of a test's
reportable range. Elsa gives a step-by-step explanation of how
to prepare and calculate the experiment that determines the range,
using a cholesterol example as well as two Javascript worksheet
calculators. Once you're done reading, you can plug in your own
data and see the results.
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MV - The Interference & Recovery
Experiments
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Elsa P. Quam, BS MT(ASCP) joins Dr. Westgard in describing
the importance of these two experiments. There are times when
comparison methods are not available and experiments for linearity
or reportable range and replication are not enough. If your laboratory
modifies a manufacturer's method, you need to know how to perform
the interference and recovery experiments. Sample data calculations
are included.
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MV - The Need for Standard Processes
and Standards of Quality
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Quality doesn't happen by itself! Quality must be achieved
by work processes that are carefully planned, properly operated,
optimally controlled, appropriately measured, and continuously
improved, i.e., by proper management of quality. This lesson
emphasizes the need for standard laboratory processes to provide
consistent quality, as well as standards of quality to guide
the management of those processes.
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MV - The Detection Limit
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When does a test become less useful? Dr. Westgard gets some
help from Karen Mugan, Elsa Quam, Trish Barry, and Neill Carey
to explain this confusing aspect of method validation.
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MV - Reference Interval Transference
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It's not some new age religion, it's yet another confusing
aspect of method validation. This time Dr. Westgard teams up
with Trish Barry to explain the ins and outs.
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Z-Stats: Lessons on Statistics
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Z-1: Aligning attitudes through purpose
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Madelon F. Zady, Ph.D., begins a series on statistics. This
first lesson is easy. All she does is explain the best way to
learn about statistics (without falling asleep).
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Z-2: An organizer of statistical
terms, part I
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Dr. Zady introduces all the terms of statistics. If you don't
know your t-tests from your F-tests, this is a painless place
to start.
Read this lesson
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Z-3: An organizer of statistical
terms, part II
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Dr. Madelon F. Zady, Ph.D., talks about the nature of a relationship
(correlation) and the strength of a relationship (regression).
These are statistical relationships, of course. For other forms
of relationship advice, we suggest you consult the webmaster
or another website entirely.
Read this lesson
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Z-4: Mean, standard deviation, and
coefficient of variation
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Don't be caught in your skivvies when you talk about CV's,
or confuse STD's with SD's. Do you know what they mean when they
talk about mean? These are the bread and butter statistical calculations.
Make sure you're doing them right.
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Z-5: Sum of squares, variance, and
the standard error of the mean
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When you compare monthly QC data or perform initial method
validation experiments, you do a lot of mean comparison. Dr.
Madelon F. Zady, Ph.D., talks about the means of means and other
important statistical calculations.
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Z-6: Probability and the standard
error of the mean
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How a coin toss relates to laboratory testing. How z-scores
can help us find probabilities. And how that bell-shaped curve
came to be.
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Z-7: Hypothesis testing, tests of
significance, and confidence intervals
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Hypothesis testing, tests of significance, and confidence
intervals - here are three more statistical terms that strike
fear in the hearts of many laboratory scientists! If you survived
the previous lesson on probability, then you can also get through
this lesson. The ideas presented here will be very helpful in
making good decisions on the basis of the data collected in an
experimental study.
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Z-8: Two-Sample and directional hypothesis
testing
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This lesson describes some refinements to the hypothesis testing
approach that was introduced in the previous lesson. The truth
of the matter is that the previous lesson was somewhat oversimplified
in order to focus on the concept and general steps in the hypothesis
testing procedure. With that background, we can now get into
some of the finer points of hypothesis testing.
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Z-9: Truth or consequences for a
statistical test of significance
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How much power does a statistical test have? What do the results
of a statistical test mean? Dr. Zady weighs in on this matter
and gives you guidance on how you should weigh the results of
your tests.
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Z-10: ANOVA - The analysis of variance
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ANOVA. No, this isn't a PBS show, it's the analysis of variance.
While this is the statistician's passion, it's a bit less thrilling
for laboratory personnel. Dr. Zady simplifies the topic and makes
it easier to understand and implement ANOVA in a healthcare situation.
Read this lesson
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Z-11: Confidence Intervals
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How much do you trust the numbers your laboratory produces?
There's a statistical way to determine just how much.
Read this lesson
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Z-12: Correlation and least squares
analysis
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Learn about r squared, Pearons Products, and other things
that will make you want to regress.
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Z-13: Least Squares regression model
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More on scattergrams, variables independent and depente, variances
explained and unexplained, and deviations squared and unsquared.
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Z-14: Estimating analytical errors
using regression statistics
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Enough of this abstract statistical stuff: how do we use these
things in the laboratory? This article shows you the practical
application of statistics on the bench-level, including how to
find the bias and other important stats.
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Basic Planning for Quality
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QP-1: A Wake-Up Call for Quality
Management
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What does the Abbott-FDA Decree mean? What was wrong with
the quality of all those tests and why didn't any of the laboratories
using those tests notice a problem? Dr. Westgard offers analysis,
answers, and a roadmap for the future.
Read this lesson
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QP-2: Assuring Quality through Total
Quality Management (TQM)
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There's a way to plan instruments, methods, and procedures
so quality is built-in from the start: Total Quality Management
(TQM). This triage system allows you to rely on statistical qc
for the easy methods, and emphasizez the non-statistical qc components
for those harder methods. Dr. Westgard explains how to adopt
and apply TQM where you work.
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QP-3: Complying with Regulations,
Standards, and Practices Guidelines
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JCAHO, CLIA, NCCLS - which guidelines define quality in the
lab? And which rules and requirements trump the other rules?
Dr. Westgard sorts through all the different regulations, recommendations,
and emerges with the critical directions.
Read this lesson
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QP-4: Devising a Practical Process
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Ok, so we agree that we need to plan quality. But how to do
it? Isn't it incredibly complex and consuming and therefore we
simply don't have time for it? NO! There's an easy way to plan
quality, a step-by-step process that we can use for every test
in the lab. Read this to discover it.
Read this lesson
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QP-5: Defining Quality Requirements
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Now that we agree that we need to plan quality, and that we've
decided on a practical process to do it, there's just one hitch
left: Where do we find quality requirements? How do we reconcile
CLIA PT criterion, clinical decision levels, analytical quality
requirements, biological variation, etc.? What does it mean when
a salesman says his instrument is "state of the art"?
What state? What art? Dr. Westgard introduces a system of quality
standards to allow you to determine the quality required for
every test.
Read this lesson
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QP-6: Using OPSpecs to Plan Quality
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Want to build QC requirements into your quality-planning process?
Take method instability into account? Use a quick graphical method?
OPSpecs (Operating Specifications) charts are graphical tools
does all that and more. All you need to do is figure out if a
point is above or below a line. How much simpler can it get?
Read this lesson
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QP-7: Formulating a Total QC (TQC)
Strategy
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Total Quality Control Strategies optimize your management
of the tests and methods in your laboratory. Learn how to distribute
your scarce laboratory resources in this lesson.
Read this lesson
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QP-8: Implementing a manual quality
planning process using Normalized OPSpecs Charts
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So you don't have the resources for fancy computers and networks
and such to plan your quality. What would you say if you only
one graph to plan your quality? A "Normalized"
OPSpecs chart allows you to choose control rules for any laboratory
method. Learn how to use them. You can even download them and
use them now.
Read this lesson
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QP-9: Practice makes Proficient
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You've read about this new Quality Planning process. But you're
skeptical. Want to see proof? How about some real-world examples,
showing bench-level quality planning in seconds. Well, this lesson
provides an overview of needs, approach, and methodology for
quality planning in healthcare laboratories. This overview should
be useful as a preview, review, or quick refresher of the quality-planning
process and the training materials available to support your
applications.
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QP-10: Automated Chemistry Applications
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The Quality Planning Process in action! Examples in this lesson
show how to choose control rules and materials for cholesterol,
glucose, chloride, calcium, and more. General suggestions
for chemistry analytes are also given.
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QP-11: Blood Gas Applications
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The Quality Planning Process in action! Examples in this lesson
show how to choose control rules and materials for pH, pO2,
pCO2and more. The differences between POC and
instrument analyzers are also discussed..
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QP-12: Immunoassay Applications
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The Quality Planning Process in action! Recently a survey
of immunoassay instruments in CAP TODAY said that immunoassay
methods were the "trickiest" to QC. But no matter how
difficult or tricky, you can still plan quality for it. Examples
in this lesson show how to choose control rules and materials
for thyroxine, cortisol, thyroid stimulating hormone,
and more. General suggestions for immunoassays are also given.
Read this lesson
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QP-13: Coagulation Applications
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Many laboratorians commonly think that QC theory is only for
chemistry. Not true! The Quality Planning process can be applied
to any test - and here's the proof for coagulation analytes like
Prothrombing Time, Partial Thromboplastin Time, and Fibrinogen.
This is also the first application that uses our new online Quality Planning Tools.
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QP-14: What's wrong with statistical
Quality Control?
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In the 50 years since we started using statistical QC in the
laboratory, a large number of complaints have accumulated. Dr.
Westgard sorts through the complaints to find solutions and an
easier way.
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QP-15: Frequently-Asked-Questions
about Quality Planning
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Usually, we put the FAQ's in our Questions section, but after
14 lessons in Quality Planning, a few questions have come up
and it's better to answer them right here. Dr. Westgard clears
up some of the common areas of confusion in quality planning.
If you still have a question after reading this, please
let us know and we'll answer you.
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