Basic SQC Practices for Healthcare Labs.pdf

8/18/2019 Basic SQC Practices for Healthcare Labs.pdf

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Basic QC Practices SECOND EDITION

Training in Statistical Quality Control

for Healthcare Laboratories

James O. Westgard, PhD

with contributions from

Patricia L. Barry, BS, MT(ASCP) Elsa F. Quam, BS, MT(ASCP)

Sharon S. Ehrmeyer, PhD David Plaut BA

Bernard E. Statland, MD, PhD

Copyright © 2002 7614 Gray Fox Trail, Madison WI 53717

Phone 608-833-4718 HTTP://WWW.WESTGARD.COM


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Library of Congress Control Number 2002102704

ISBN 1-886958-17-3Published by Westgard QC, Inc.

7614 Gray Fox TrailMadison, WI 53717

Phone 608-833-4718

Copyr ight © 2002 by West ga rd QC , In c. (WQC). All righ t s re-served. No pa rt of th is publica t ion m a y be reproduced, stored in a

retrieva l system, or t ra nsmitted in a ny form or by an y mea ns,

electronic, mechanical, photocopying, recording, or otherwise,

w ith out prior w rit t en perm ission of West ga rd QC , Inc..


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Westgard QC, Inc, Copyright © 2002

Table of Contents

1. What’s the idea behind statistical quality control?...............1

QC – The I dea ......... . . . . . . .. . . . . . . .. . . . . . . .. . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . 32. How do you plot and interpret control results on a Levey-

J ennings chart?..........................................................................15

QC – The Levey-J ennings Cont rol Ch a rt . . . .. . . .. . . .. . . .. . . .. . . .. . . . 17

Levey-J ennings QC pra ctice exercise .. . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . . 28

3. Isn’t there a simpler way to do QC? ........................................37

QC – E lectr onic Ch ecks a nd t he Tota l Testing P rocess .. . .. . 39

4. What is the minimum QC? .........................................................49

QC – P oint -of-Ca re Testing a nd P hys icia n Office La bora tories,

Sh a ron S. E hrm eyer, P hD ... .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. 51

5. Are QC improvements still needed? ........................................63

QC – D Os a nd D ON’Ts .......... . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . 65

6. How do you interpret control data using a multirule QC

procedure?...................................................................................75

QC – The “Westg a rd R ules” ........ . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . .. . . . . 77

7. How do you interpret multilevel QC data? ...........................91QC – The Multirule and Multilevel Int erpreta t ion . . .. . .. . .. . .. 93

8. How do you solve out-of-control problems?........................105

QC – The Out -of-C ont rol P roblem .......... . . . . . . . .. . . . . . . . .. . . . . . . . .. . . 107

9. What documentation and QC records are required? ........115

QC – The Record s ........ .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . 117

10. What is external quality assessment?.................................123

QC – E xterna l Qua lity Assessment .. . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. 125

11. What regulatory guidelines influence QC? .......................141

QC – The Regulat ions, Sha ron S. Eh rmeyer, Ph D .. .. . .. .. . .. 143

12. What are control materials and what characteristics are

important? .................................................................................155

QC – The Ma t eria ls ....... .. . . . . . . . .. . . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . 157

Medica l Decision Levels , Berna rd E . Sta t la nd, MD, P hD 164

13. What calculations do you have to perform?......................169

QC – The Ca lcula t ions ........ .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . 171


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Basic QC Practices, 2 nd Edition

14. What are the chances of run rejection?..............................185

QC – Rejection C ha ra cterist ics a nd P ow er Cur ves . . .. . .. . .. .. 187

15. What’s wrong with Quality Control? ..................................199QC – Compla ints a nd S olutions .. . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . 201

16. What does “doing the right QC right” mean? ...................207

Repea ted, Repea ted, G ot Lucky .. . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . 209

17. What is the right QC?..............................................................215

Ma pping t he Road to Qua lity .. . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . 217

18. What’s the right way to select the right QC? ....................227

QC – Computer Technology for S electing t he Right QC ... 22919. What’s the right way to implement the right QC? ...........241

QC – C omputer Technology for I mplementing QC Right . 243

20. What’s in the future for laboratory QC? ............................261

QC – Sa ge Advice on New QC Approaches ........ . .. . . . . . . . . . .. . . . 263

21. Basic QC Glossary ...................................................................273

22. References and Online Resources .......................................291

23. Self-Assessment Answers .......................................................299

Appendix 1: CLIA’88 Quality Requirements............................327

Appendix 2: Normalized OPSpecs Charts................................331


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Foreword to the 2nd Edition

J ames O. Westgar d, PhD

I hea rd th is story from a Medical Technologist w ho wa s inspecting a la bora tory. Sh e

observed a meth od th a t w a s in very good control – in fa ct, the control w a s too goodbecause all the points on the control charts were within plus or minus just one

sta nda rd devia tion of the means for t he control ma terials. She a sked how t he control

l imits were set – an d lea rned tha t the la bora tory used the ma nufacturer 's

recommended range to estimate the standard deviation(s), then computed 3s

control limits. A quick calculation from real control data showed that the actual

sta nda rd devia tions w ere about 1/3 of th ose used in calculat ing t he cont rol limits,

w hich mea nt tha t the nominal 3s control l imits corresponded to actua l sta tist ica l

cont rol limits of 9s! The la b wa s using a 19s

control rule on this method. It's no

w onder the laborat ory never ha d a ny contr ol problems! I t w ould ta ke a systema tic

error of 10 to 15 times t he size of th e sta nda rd devia tion t o trigger a n out -of-control

signal. The method would probably have to run out of reagents before the QC

procedure would detect something was wrong.

This story shows that plotting points on control charts does not constitute

quality control. There's a right way to do QC if the purpose is to assure that test

results provide the quality necessary for patient care. Somehow in this highly

advanced medical industry, in this age of high technology, in this era of making

healthcar e a n eff icient business, and w ith t oda y's focus on " the bottom line, " the

ba sics a re being forgott en. Tha t's wh y w e've wr itt en th is book. The most ba sic a nd

funda menta l expecta tion of a laborat ory is th a t i t provides correct test results.St a tist ica l QC pra ctices are cri t ica l for a ssuring test results a re correct .

QC is Safety

QC practices, like safety practices, are recognized as important if something bad

ha ppens, but t hey seem t o be a w a ste of t ime an d effort w hen things a re working

okay. The key to their success is advance planning, anticipation of what might go

w rong, w a rnings w hen things a re going wr ong, an d a pla nned course of action t o

respond t o a problem a nd minimize the dam a ge. The similari ty betw een q ua lity a nd

sa fety processes provides a u seful a na logy: th ink of a QC problem a s a fire a nd t hinkof a QC procedure as a smoke detector. Many people agree that a smoke detector

is necessary, but st i l l think i t 's a w a ste of t ime to ha ve an evacuation pla n, f ire dril ls,

and training with fire extinguishers, at least until a real fire occurs. Then it

suddenly becomes relevant, and hopefully it ’s not too late to read the emergency

response guide posted in the laboratory.

Sa fety is pa rt of basic tr a ining in the labora tory. So is qua lity control. We need

to keep at i t to maintain a good program, prevent unfortunate events, detect

problems, a nd respond quickly w hen th ey occur. We must periodica lly review our

sa fety procedures a nd pa rt icipat e in plann ed drills. We also need to review our QC

procedures a nd be su re th ey a ccomplish w ha t th ey're supposed to – t hey're supposedto detect errors, without a lot of false alarms that waste t ime and effort .


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QC Basic Training 101 is the minimum tra ining for a nyone w hoperforms a laboratory test, including personnel in point-of-care settings.

• QC – The I dea presents the concept of measurement va riat ion a nd i ts

use to monitor method performance with the aid of a control chart.

• QC – The Levey-Jenn i ngs Cont r ol Cha r t describes the construction of acontrol chart, plotting of control data, and interpretation of control results.

• QC – E lectr oni c QC an d t he Total Testi ng Pr ocess deals with the specific

issue of using electr onic checks a s pa rt of a QC syst em.

• QC – Poin t-of-Car e Testi ng an d Physician Off i ce L abor ator i es describes

the overall QC system t ha t is needed in a P OC setting.

• QC – DOs and DON ’Ts provides a summa ry of good a nd ba d QC

practices.

QC Basic Training 102 a dds the fol low ing for a na lysts w ho workunder supervision in a laboratory t esting si te.

• QC – The "Westga r d Ru l es" describes the use of multiple decision rules,

or multirule QC, to make judgments about the acceptance and rejection of

analy t ica l runs.

• QC – Th e M ul t i ru le and M ul t i level I nterpr etat i on i l lustrates the

application of multiple control rules with multiple control materials.

• QC – The Ou t-of-Cont r ol Pr oblem provides some guidelines on how to

respond to control rule violations, with emphasis on identifying and solving the

problem causing th e rejection signa l.

• QC – The Recor ds describes the importa nce of ha ving a good syst em of

document a tion t o aid in solving control problems.

• QC – Exter nal Qual i ty Assessment describes the use of external quality

assessment information to further support the evaluation of performance and

identification of problems.

QC Basic Training 103 a dds the fol low ing for a na lysts w ho workindependently, have responsibilities for managing specific instruments and

systems, or responsibilities for supervising other laboratory personnel.

• QC – The Regul at i ons provides a summ a ry of guidelines from govern-

ment a nd a ccredita tion organizat ions, part icularly CLI A, CAP , and J CAHO.

• QC – The M ater i al s discusses the selection of control materials and

factors that affect their usefulness for monitoring laboratory methods.

• QC – The Calcul ati ons explains how to calculate the mean and standard

devia tion from control dat a a nd how to ca lculat e month ly an d cumula tive

control limits.

• QC – Rejecti on Char acter i sti cs and Power Cu r ves describes the perfor-

ma nce cha ra cteristics of QC procedures in term s of probabilities of fa lse rejec-tion a nd error detection (false ala rms a nd t rue a larms, resp.) a nd intr oduces the


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power function graph as a tool for evaluating and comparing the performance of

different control rules.

• QC – Complai nt s and Solu ti ons discusses the changes and improve-

ments that wil l provide better QC systems.

QC Basic Training 104 provides guidance for analysts, supervisors,managers, and directors who are responsible for establishing and implementing

QC procedures in laboratories.

• Repeat ed , Repeat ed , Got Lucky describes a common problem in many

labora tories due to poor pla nning of QC procedures.

• M apping the Road to Qual i ty describes a st ep-by-st ep procedure for

selecting control rules and numbers of control measurements on the basis of the

qua lity required for a test a nd t he imprecision a nd bias observed for a method.

• QC – Compu ter Techn ol ogy for Sel ecti ng t he Righ t QC demonstrateshow to implement an efficient planning process with the aid of a QC design

program.

• QC – Comput er T echn ol ogy for I mpl ement i ng QC Ri ght demonstrates

the feat ures tha t a re importa nt w hen selecting softw a re to implement rea l-time

data checking.

• Sage Ad vi ce on N ew QC Appr oaches provides a summa ry of the “sta te-

of-the-art” in laboratory QC and the future directions for development of

improved QC systems.

Ba si c QC Pr ac t i ces, 2 n d E d i t i on is par t of a tr ilogy of “back to basics”books tha t deal w i th a naly t ica l qual i ty man agement . Ba si c M et h o d Va l i d a -

t i o n considers the experimental and statistical techniques needed to character-

ize the precision a nd a ccuracy of a m ethod. Ba si c P l a n n i n g f or Q u a l i t y

provides a set of manual tools for selecting control rules appropriate for a test

a nd m ethod. A more adva nced Qua lity-P lann ing a pproach tha t describes in

deta il the a pplica tion of computer t ools can be found in our book S i x Si gm a

Qua l i t y D esi g n a n d Con t r ol .

We hope tha t r eaders of th e 2nd edition w ill find Ba si c QC P r ac t i c es

even more useful than the 1st edition.


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Foreword to the 1st EditionThe 1990s will probably be remembered as the "re-decade" in healthcare –

reorganizing, restructuring, and reengineering to reduce costs. The management

stra tegy of "doing more w ith less" ha s meant tha t la bora tories end up w ith less sta f f

w ith less educa tion, less experience, and less tra ining. G iven t he ma jor cha nges inorgan izat ion a nd processes, it w ould be expected t ha t q ua lity cont rol effort s should

ha ve increased to gua rd a ga inst t he da ngers of doing more wit h less, i .e. , doing more

test s less w ell. B ut la bora tory inspections continue to cite QC pra ctices as one of

the most frequent and serious deficiencies, suggesting more tests are being done

w ith less qua lity control. This out come is defended by a rgum ents th a t th ere a re

more an d bigger problems elsewhere in th e tota l testing process, therefore an a lyti-

cal qua lity is no longer a concern.

This book is part of a counter-a tt a ck a gainst the notion t ha t w e should a ssume

analytical quality is okay. While it may be simple to perform tests with today'sanalytical systems, i t is also simple-minded to assume that the test results are

a utomat ica l ly okay a nd th a t nothing can go wrong with t he testing process. Does

a nyone believe the t est r esult given by a ba th room sca le? Will a nyone sett le for a

single measur ement, or does it t a ke a series of meas urement s t o convince them of

the correctness of their w eight? La bora tory mea surements a re certa inly m uch m ore

complica ted a nd sh ould not be a ssumed t o be correct! We ow e it to our pat ients a nd

physicians to assure, rather than assume, that the test results are correct .

In t he sports verna cular of this deca de, there is alw a ys ta lk about t he need to

get " ba ck to th e basics" w henever performa nce isn't w ha t it 's supposed to be. Forgeta bout the ra zzle-da zzle and be sure everyone understa nds t he funda menta ls. We

have a similar message for anyone who does laboratory tests. Forget about the

ra zzle-da zzle of the a utoma tic instru ment w ith i ts digita l readout a nd i ts computer

interface: be sure the test results are correct. Basi c QC Pr actices is about the

funda menta ls of performing sta tist ica l QC to assure th e quali ty of la bora tory tests.

We hope this book will help you serve your patients better.

The production of this book came about in a different manner than usual.

Before these words were put down on paper, they were first published on the

In tern et. Tha t's right ! This w a s a virt ua l book, in a sense, before it w a s rema de in

its present form. Lit era lly thousa nds of people ha ve had a chan ce to preview ma ny

of the lessons present ed here, from over 40 different count ries a round t he w orld.


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AcknowledgmentsThis book w ould n ot exist w ithout th e help of others. S ten Westga rd provided

th e inspira tion for our website, guided its development, a nd ma na ged the publica -

tion of these ma terials.

Elsa Quam a nd Trish B a rry continue to be my sounding boa rd for new idea s,

concepts, th eories, a nd a pproa ches. The pra ctica lity of th ese ma teria ls ar e ma inly

th e result of their insight s on how to relat e theory t o pra ctice. I've been privileged

to work with ma ny capa ble technologists during my career and E lsa a nd Trish a re

certainly two of the best.

Sha ron E hrmeyer alw a ys steps in t o help me with the government a ngle on

regula tions. I confess I don't r eally get very excited by publica tions in t he Federa l

Register a nd a m glad t ha t S ha ron does a nd is wil ling to keep me up to dat e on w ha t's

happening. Bernie Statland saved us a lot of t ime and effort by al lowing us to

present a summary of his recommendations on medical decision levels. That

informa tion f i l ls w ha t otherw ise would ha ve been a real void in t hese mat eria ls.

Da vid Pla ut suggested the reorga nizat ion of these mat eria ls and contr ibuted

to the expansion of coverage to make this a better and more complete manual for

teaching a nd tra ining.

J a mes O. Westgard

Madison Wisconsin

About the authors and contributors J ames O. Westgard, PhD, is a P rofessor in the Depart ment of P a thology a nd

Laboratory Medicine at the University of Wisconsin Medical School, where he

tea ches in th e Clinica l La bora tory S ciences progra m. He is a lso Director of Quality

Ma na gement S ervices at th e Clinica l La bora tories, U niversity of Wisconsin Hospi-

tal & Clinics, and the President of Westgard QC, Inc.

David S. Plaut, BA, is a Scientific Specialist with more than 30 years ofexperience in clinical chemistry. He is a fixture at national, regional, and local

meetings, where his presentations regularly attract standing-room-only crowds.

Elsa F. Quam, BS, MT(ASCP), is a Quali ty Special ist in the ClinicalLaboratories at the University of Wisconsin Hospital and Clinics.

Patricia L. Barry, BS, MT(ASCP), is a Quali ty S pecial ist in t he Clinica lLaboratories at the University of Wisconsin Hospital and Clinics.

Sharon S. Ehrmeyer, PhD, MT(ASCP), is a P rofessor in th e Departm entof Pathology and Laboratory Medicine and Director of the Clinical Laboratory

Sciences Program at the University of Wisconsin Medical School.

Bernard E. Statland, MD, PhD, is Director, Office of Device Evaluation,Center for D evices an d Ra diologica l Hea lth, U S Food a nd D rug Administra tion.


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Author's Note:

I have spent the last 25 years of my career advocating the improvement of

qua lity contr ol in the hea lthcare laborat ory. Du ring th a t t ime, I ’ve discovered t ha t

it’s not enough t o ta lk about th e need for qua lity improvement ; people need t ools a nd

techniques t o ma ke improvements. S o I developed sta tist ica l tools to help ident ify

poor quality control practices and I also developed planning processes to guide

quality improvements. Then I discovered it wasn’t enough to demonstrate those

qua lity improvement tools; people needed t hese tools to be practical, fas t, a nd ea sy

to use. So I colla bora ted w ith others on t he creat ion of softw a re tha t a utoma ted the

tools a nd t echniq ues an d provided a simple, graphic int erface for users. E ven then,

I discovered tha t ha ving the t heory a nd t ools an d softw a re wa sn’t enough; people

need a quick and convenient way to learn and access these things. So I started

publishing books, offering online courses, a nd posting a rt icles on t he In ternet .

Throughout t his book, you’ll see tha t I ment ion Westga rd QC products in t helessons. You may be tempted to say this book is therefore commercially biased. I

a dmit to several biases: I a m biased aga inst th e “sta tus q uo” complia nce menta l ity

of current QC pra ctices in hea lthcare. I am biased aga inst t he idea t ha t w e should

abandon statist ical QC for unproven, less capable QC techniques. I am biased

against the short-sighted, short-term impulse to slash costs in every area of

hea lthca re la bora tories a nd elimina te an y investment in qua lity control. These very

biases, a nd m y un w illingness t o accept th e decline of qua lity cont rol pra ctices, led

me to found a company that would create products to enable and enhance the

improvement of quality control.

I’m happy to admit that I’m proud of every book that has been published by

Westgard QC a nd t ha t I ’m proud of a l l the softw a re packa ges we’ve relea sed. In th e

text, w here possible, I note some of the other products a va ilable on t he ma rket t ha t

provide the sa me qua lity improvement t ools or techniques. In m a ny cas es, how ever,

there is no other software or book out there on the market – for instance, our

software may remain the only software in the world that provides automatic QC

selection for quite some time. I look forw a rd t o th e day w hen t his ma rket is crowded

with competitors and I have to provide a third edition of this book.

As a f ina l note, I ha ve structured B a sic QC P ra ctices, 2nd edition, so it st a nds

a lone. You do not need to buy a nyth ing further t o sta rt improving the qua lity of your

labora tory. The book ha s links to free Int ernet t ools on our w ebsite tha t w ill a llow

you to plot a nd int erpret control results on a Levey-J ennings cha rt . The last

a ppendix conta ins a series of normalized OP Specs chart s th a t you can use to sta rt

a ma nua l qua lity improvement process. All you need to do is read t his book a nd st a rt

taking action.


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Westgard QC, Inc. Copyright © 2002

Page 1

1: What's the idea behind statistical

quality control?Lear n t o describe the basic idea of sta t ist ica l QC in t erms of the

va ria t ion expected in a mea surement process. In QC – The Idea,Dr . Westga rd shows how a histogram representing m easurement

va ria t ion is the basis for th e QC char t .

Objectives:

E xplain the ba sis of a s ta t is t ica l QC chart . Review th e QC t erminology.

P review t he a pplica tion of QC for la bora tory t ests.

Lesson materials:

QC – The Idea, by J a mes O. Westga rd, P hD

Things to do:

St udy t he lesson.

Select a n exa mple labora tory test t ha t is of interest to you.

Review your la bora tory’s description for doing QC for t his t est.


12/24


Page 2

Self-Assessment Questions:

Wha t is t he ba sic principle of sta t istical QC ?

Wha t is a cont rol rule?

Wha t is t he meaning of 12s an d 13s?

Wha t is an a na lyt ica l run?

Wha t informa tion is needed to ca lcula t e cont rol limits?


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Page 3

QC – The Idea

James O. Westgard, PhD

The Need for QC

The product of a testing process is a numerical result. Unlike a

physica l product th a t ca n be inspected t o assess w hether it looks

good or ba d, you ca n't look a t a test result a nd t ell w hether it 's va lid.

247 – w ha t d o you t hink? If this is a pat ient sa mple, do you t hink

th e test r esult is of good q ua lity (mean ing t he corr ect va lue)?

If t he va lue of 247 is mea -sured on a sa mple tha t ha s been

a na lyzed before and ha s the va l-

ues show n in t he a ccompa nying

histogram , do you t hink the t est

result is of good q ua lity? B eca use

va lues betw een 240 an d 260 ha ve

often been observed in pa st mea -

surements, i t is expected that

th is new va lue should a lso fa ll intha t ra nge if everything is w ork-

ing oka y, t herefore, the pa tient

test results included in t his run

of measurements are also most

likely correct.

A simple graphical tool – the QC chart

In the laboratory, control charts are used to make it simple tocompa re toda y's observed va lue wit h w ha t is expected ba sed on pa st

history. As shown in th e second f igure, by tur ning t he histogram

sidew a ys a nd spreading t he results out a ccording to the t ime they

w ere collect ed, it is ea sy t o see how ea ch observa tion compa res t o

th e expect ed distribut ion of pa st observa tions, wh ich a re show n by

the central l ine and certain limits calculated from the mean and

sta nda rd devia tion (SD ) of the pa st cont rol da ta . In th is figure, th e

limit l ines correspond t o th e mea n ± 1 SD , 2 SD , an d 3 SD .

XX

XX XX

XX

X

X X

X

XXX

XX

XXX

XXXX

250 260245240 255235 265


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Page 4

Assuming a Gaussian or normal distribution, i t would be

expected t ha t a bout 68% of th e point s fa ll wit hin 1 SD of the m ean ,

95% w ith in 2 SD of th e mea n a nd 99.7% w ith in 3 SD of th e mea n.

Ther efore, it w ould be very un expect ed (0.3% cha nce) t o obser ve a

control value greater than 3 SD from the mean and such an

observa tion usua lly indica tes t here is a problem w ith t he method.It is somewh a t unexpected t o observe a contr ol va lue grea ter t ha n

2 SD from t he mean , but t his w ill ha ppen a t lea st 5% of the t ime

w hen a na lyzing 1 contr ol per run , so it m a y indica te a real problem

or it ma y be a fa lse a la rm . It is very comm on (32% cha nce) to see

individual values beyond 1 SD from the mean, therefore this

control limit is of no value for making a judgment about method

performa nce ba sed on a single cont rol va lue.

The IDEA of a

QC Chart

• Determine the expected

distribution of control values

• Calculate mean and SD fromcontrol data to establish control

limits for control chart

• Expect control values to fall with

certain control limits

– 95% within 2 SD

– 99.7% within 3 SD

• Plot control values versus time to

provide control chart

• Identify unexpected values

265

250

260

255

245

1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 20 1 2 3 4

Run Number (or Time, Date)

240

235

X

XX

X

XX

X

X

X

X

XX

X

X

XX

XX

X

X

X

X

X

X

XX

XX XX

XX

XX X

X

XXX

XX

XXX

XXXX

250 260

245

240

255235 265

Very Unexpected

Somewhat Unexpected


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Page 5

Tha t's t he idea behind st a t ist ica l qua lity contr ol. See if you

ca n get the right a nsw er for a sa mple w ith known va lues. The right

a nswer is ac tua l ly a ra nge of values tha t a re ca lculated f rom t he

mea n a nd sta nda rd devia tion of pa st results. Tha t mea n a nd cont rol

limits ca n be show n on a cont rol cha rt to ma ke it sim ple to plot n ew

cont rol mea surement s a nd see how t hey compa re w ith t he expected

ra nge of va lues.

In the beginning, there was Shewhart

Wa lter A. Shewh a rt w a s a sta tisticia n a t B ell Telephone Laborat ories

who developed the scientific basis for statistical process control.

Sh ewha rt sta ted th a t " th e object of indust ry is t o set up economic

w a ys of sat isfying huma n w a nts a nd in so doing to reduce everyt hing

possible to routines requiring a minimum a mount of huma n effort .

Thr ough th e use of t he scient ific met hod, extended to t a ke a ccount

of modern st a tist ica l concepts, it ha s been found possible to set up

limits w ithin w hich t he results of routine effort s must lie if they a re

to be economical. Deviations in the results of a routine process

outside such limits indica te th a t t he routine ha s broken down a nd

w ill no longer b e economica l unt il th e ca use of t rouble is removed."

Shewhart made this statement in the preface to his book on the

" E conomic Control of Qua lity of Manufa ctur ed Pr oduct" tha t w a s

publis hed in 1931.[1]

S ta tist ica l process cont rol, from th e beginning, h a s been con-

cerned w ith a chieving the desired qua lity (sa tisfying huma n w a nts)

at minimum cost (economic control). Shewhart identified critical

element s such as t he expected va ria tion of a r out ine process, a w a y

to set l imits t ha t w ill identify w hen the routine ha s broken down,

a nd t he need t o elimina te causes of tr ouble w hen th e process wa sobserved to exceed th ose limit s.

Almost t w enty yea rs pa ssed before Levey a nd J ennings int ro-

duced sta t ist ical cont rol meth ods in clinica l la bora t ories in 1950 [2].

Sh ewha rt 's origina l recommenda tions called for m a king a group of

mea surements, ca lculat ing the avera ge a nd ra nge (ma ximum dif-

ference), th en plott ing t he a vera ge a nd the r a nge on t w o dif ferent

contr ol cha rt s. Levey a nd J ennings proposed ma king duplica te

mea surements on a pat ient specimen. Beca use the a ctua l level of


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th e measu red const ituent va ried from specimen to specimen, this

w a s a more difficult a pplica t ion. H enry a nd S ega love [3] developed

a n a lterna tive procedure in w hich a sta ble reference sam ple wa s

analyzed repeatedly and individual measurements were plotted

directly on a control chart. This reference sample type of QC in

which individual values or single values are plotted directly is

commonly know n today a s a Levey-J ennings cha rt .

Since tha t t ime, industr y h a s developed st a ble cont rol prod-

ucts that mimic pat ient samples, thus today there are safe QC

materials readily available for most established tests. A better

und erst a nding of th e perform a nce cha ra ct eristics of QC procedures

ha s been developed [4], w hich ha s led to refinements s uch a s t hemultirule procedure for eva lua ting a nd int erpreting cont rol dat a

[5]. St ra t egies for cost -effective opera t ion ha ve been fur t her r efined

[6]. Comput er progra ms h a ve been developed to implement st a tis-

tica l cont rol procedures by perform ing t he necessa ry ca lcula tions,

prepa ring gr a phica l displa ys, a pplying t he desired contr ol rules,

and alert ing analysts to problem situations. Today, support for

ha ndling contr ol results is provided by most a utoma ted a na lyzers,

inform a t ion syst ems, a nd even point -of-ca re devices.

Learning the QC lingo

Sta ti sti cal pr ocess cont r ol is th e genera l term used to describe those

aspects of a control system in which stat ist ics are applied to

determine whether observed performance is within the expected

va ria tion of the process, in cont ra st t o oth er component s of a tota l

cont rol system such a s prevent ive maint enan ce, inst rument function

checks, operator training, etc., that are included in CLIA's broad

definition of qua lity cont rol.

Qual i t y contr ol pr ocedu r e is us ed here t o refer t o a specific protocol

for a na lyzing a specific num ber of cont rol ma teria ls an d interpreting

a specif ic number of test results. In healthcare laboratories, a

cont rol procedure is usua lly implemented by collectin g t est r esults

on st a ble cont rol ma teria ls, then plott ing th ose cont rol observa tions

on a contr ol cha rt tha t ha s specified contr ol l imits, or by eva lua ting

those contr ol result s by da t a calcula t ions employing specified decision

crit eria or cont rol rules.


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Contr ol chart is a graphical method for displaying control results

a nd eva luat ing w hether a mea surement procedure is in-cont rol or

out -of-cont rol. Control results a re plott ed versus tim e or s equent ia l

run number; l ines a re genera lly dra w n from point to point to a ccent

a ny t rends, systema tic shif ts, and r a ndom excursions.

Cont r ol l im i ts a re lines dra w n on a cont rol cha rt to provide graphic

criteria for assessing whether a measurement procedure is in-

cont rol or out -of-cont rol. These cont rol limit s a re us ua lly ca lcula t ed

from t he mean a nd st a nda rd deviat ion (SD , or s) determined for a

given control material. Typically the interpretation is based on a

specified n umber of result s or point s exceeding a certa in cont rol

limit. When in-cont rol, pa tient test results a re report ed. When out-of-cont rol, the ru n is rejected a nd no test results ca n be r eport ed.

Cont r ol ru le means a decision criterion for judging whether an

a na lyt ica l run is in-cont rol or out -of-cont rol. It is comm only defined

by a sym bol of the form AL , where A is an a bbreviat ion for a sta t ist ic

or represents a num ber of cont rol mea surement s, a nd L ident ifies

th e cont rol l imits, of ten specif ied a s t he mean ± a multiple of the

st a nda rd d evia tion (s) or sometimes by a specified probability for

false rejection (P fr). S ome exa mples follow :

13s refers to a control ruleth a t is commonly used w ith a

Levey-J ennings cha rt w hen

th e cont rol l imits a re set a s

the mea n + 3s and the mea n

–3s. A ru n is r eject ed w hen a

single control measurement

exceeds t he mean + 3s or t he

mea n –3s cont rol limit .

+3s+3s

+2s+2s

+1s+1s

-3s-3s

-2s-2s

-1s-1s

MeanMean

11 22 554433 66 77 88 99 1010

13s rule

violation

13s rule

violation


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Page 8

12s refers t o the cont rol rule

th a t is commonly used w ith aLevey-J ennings cha rt w hen

the control l imits are set as

the mea n ± 2s. In the origina l

W e s t g a r d m u l t i r u l e Q C

procedure, this rule is used

as a warning rule to t r igger

c a r e f u l i n s p e c t i o n o f t h e

cont rol da ta by other r ejection

rules.

22s refers t o the cont rol rulethat is used with a Levey-

J en n i n g s ch a r t w h en t h e

control l imits are set as the

m e a n ± 2s . I n t h i s ca s e,

however, th e run is rejected

when 2 consecutive control

m easurem en ts ex c eed th e

same mean + 2s or the same

mea n –2s.

R4s refers to a control rulew here a reject occurs w hen 1

control measurement in a

group exceeds t he mea n + 2s

a n d a n o t h e r e x c e e d s t h e

mea n –2s.

+3s+3s

+2s+2s

+1s+1s

-3s-3s

-2s-2s

-1s-1s

MeanMean

11 22 554433 66 77 88 99 1010

12s rule

violation

12s rule

violation

+3s+3s

+2s+2s

+1s+1s

-3s-3s

-2s-2s

-1s-1s

MeanMean

11 22 554433 66 77 88 99 1010

22s rule

violation

22s rule

violation

+3s+3s

+2s+2s

+1s+1s

-3s-3s

-2s-2s

-1s-1s

MeanMean

11 22 554433 66 77 88 99 1010

R4s rule

violation

R4s rule

violation


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Run, analyt ical r un, or r un length refers to the interval, which

could be a period of t ime or group of sa mples, for w hich a decision

on cont rol sta tus is to be ma de. CLIA defines a ma ximum run length

of 24 hours for chemist ry a na lyt es an d 8 hours for hema tology t ests.

Ma ny la bora tories define a sh ort er period ba sed on chan ges tha t

ma y a ffect t he performa nce of the test ing process, such a s cha nging

opera tors, cha nging rea gents, reca libra tion, or other fa ctors tha t

ma y m a ke the process susceptible t o problems. Run length va ries

from system to system and laboratory to laboratory. For random

access automated systems, a run is usually defined as the t ime

interva l a t w hich controls a re rea na lyzed. For ma nua l systems and

ba tch instr uments, a run is of ten defined a s a group (or ba tch) of

sa mples tha t a re a l l a na lyzed at the same t ime.

Doing the deed

The idea is simple, but doing QC for r ea l can become complica ted.

In t his book, we’ve divided th e ma teria ls into four st udy a rea s tha t

are appropriate for operators and analysts who have dif ferent

levels of interest a nd r esponsibility.

Basic Training 101 – Doing QC. Not everyone needs toundersta nd everything in this book, but everyone should stud y t he

first group of lessons. These initial lessons are concerned with

doing QC an d understa nding its import a nce. They assum e tha t t he

testing application is overseen by someone who takes care of the

more technical d eta ils discussed la t er on in t his book.

The lesson QC – Th e L evey -J en n i n gs Con t r o l Ch a r t

provides deta iled dir ections on how to prepar e cont rol cha rt s, plot

control values, and interpret control data. Because QC can becomplicated (and unfamiliar to non-laboratory personnel), many

ma nufa ct urers t oda y recomm end the use of electr onic QC inst ead

of statistical QC for Point-of-Care (POC) applications. The lesson

QC – E l ec t r on i c QC an d th e To ta l Test i n g P r ocess discussesth e need a nd us efulness of both a pproa ches. The minimu m r equire-

ments that must be satisf ied are discussed in the lesson QC –

Poi n t -of -Ca r e Test i n g an d Phy si c i a n O f f i c e L abo r a t o r i es . A

sum ma ry of good a nd ba d pra ctices is provided in the lesson QC –

DOs an d D ON ’ T s .


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Basic Training 102: Understanding the Results. Ana lystsw orking in a production labora tory need bett er skills in int erpreting

QC data from internal and external programs. Decisions about

accepting or rejecting test results on the basis of QC should be

str a ight-forw a rd. H ow ever, it is st i l l dif ficult t o know w ha t t o do

w hen t here is a problem. P roblem-solving req uires a more in-depth

understanding and interpretat ion of QC data , QC records, and

peer-comparison results.

The use of multiple control rules provides one source of

information about the type of error that might be occurring. The

lesson QC – T h e “West ga r d Ru l es” reviews commonly-used

multirule QC procedures. The lesson QC – Th e M u l t i r u l e an d Mu l t i l ev el I n t er p r et a t i on focuses on ident ifying t he t ype of erroroccurr ing in complex applica tions w here th ere a re mu ltiple cont rol

measurements from multiple control materials. The lesson QC –

Th e Ou t -of -Con t r ol P r ob l em provides general guidance fortrouble-shooting QC problems. An important resource in trouble-

shootin g is good document a tion of the hist ory a nd cha nges w ith a

meth od, a s discussed in QC – T h e Recor d s . Final ly , an importa nt

source of information for understanding how your method is

perform ing rela tive to ot her met hods in th e field is described in QC – Ext er n a l Qu a l i t y Assessm en t .

Basic Training 103: Maintaining Proper Procedures.Analysts who have responsibility for particular methods and

systems, or work independently with little supervision, need to

understa nd ma ny of the f ine deta ils of QC. QC a ctua lly seems easy

w hen everything is properly set up an d ma inta ined. B ut t ha t ’s not

th e situa tion in ma ny labora tories. You need to ca lcula te contr ol

limits properly, select appropriate control rules and numbers ofcont rol mea surements, a nd define when cont rols w ill be a na lyzed.

These are complicated issues that are influenced by both legal

requ irement s a nd scient ific principles.

Fir st , consider professiona l pra ctice guidelines a nd g overn-

ment regulat ions t ha t inf luence the pra ctice of QC in la bora tory

sett ings today. See the lesson on QC – Th e Regu l a t i ons for asumma ry of lega l an d a ccredita t ion requirements. To sta rt sett ing

up a QC procedure, you f irst select control materials that are


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Page 11

a ppropria te for th e tests of interest a nd methods in use. Ma nufa c-

turers ma y supply these ma teria ls a long w ith t heir t est syst ems,

but it is a lso good pra ctice to ha ve at lea st one cont rol ma teria l tha t

is from a different m a nufa ct urer. The selection of ma teria ls should

consider import a nt factors, such a s ma tr ix ef fects, st a bility, via l-to

-via l varia t ion, a ssayed versus unassa yed ma teria ls , ana lyte lev-

els, pre-treatment problems, and cost. See the lesson QC – T h e

Ma t e r i a l s for m ore deta ils.

Next you must assay the selected control materials under

routine opera tin g condit ions t o cha ra ct erize t he var ia tion expected

in your own situation. This usually involves making at least 20

measurements , then ca lculat ing the mea n a nd sta nda rd devia t ion.There ar e ma ny pit fa lls from using bott le va lues a nd estima tes of

the mean a nd sta nda rd deviat ion tha t don’t r epresent t he varia t ion

in your own setting. See the lesson QC – Th e Ca l cu l a t i ons for

more informa tion a bout da ta ca lculat ions.

E ven w ith good ma teria ls a nd proper calcula tions, there ma y

be false alarms or false rejections that cause much confusion for

a na lysts, pa rt icular ly w hen certa in contr ol rules a re utilized. The

lesson QC – Rej ec t i on Ch a r ac t er i st i c s an d P ower Cu r ves describes the rejection characteristics of commonly-used control

rules and int roduces an import a nt eva luat ion a nd planning t ool –

th e Power Function G ra ph. Fina lly, th e lesson QC – Comp l a i n t s

a n d Sol u t i o n s describes th e cha nges an d improvements t ha t w illlea d to better QC systems in a ny labora tory sett ing.

Basic Training 104: Implementing Effective QCProcedures. Someone – probably you since you’re the oneint erested enough t o be readin g th is book – needs to define wha t QC

procedures are to be used for the tests in your laboratory. This

responsibility often resides w ith dir ectors, ma na gers, supervisors,

or QC specia lists, but someone needs to know w ha t QC t o do. The

qu estion often a rises w hen old methods or sys tems a re repla ced by

newer ones tha t ha ve bett er sta bility a nd performa nce, but should

a lso be pa rt of the a nnua l review of labora tory procedures. Ma ny

laboratories continue to do the same old QC because they don’t

know how t o plan QC procedures to account for t he qua lity r equired

for a test and the performance observed for the method.


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Page 12

The problems du e to poor pla nning of QC a re high lighted in

Repea ted , Repeat ed , Got L u ck y . The essa y’s tit le comes from a

rea l experience wit h a comm on problem in ma ny la bora tories. Tha t

problem ca n be solved by implementin g a syst ema tic, step-by-st ep

plan ning process to select cont rol rules a nd th e num ber of cont rol

mea surements in a n objective wa y. S ee th e lesson Ma p p i n g t h e

Road t o Qua l i t y for a description of an approach for selecting

cont rol rules a nd n umbers of cont rol mea surem ents on th e basis of

th e qua lity required for a test a nd t he imprecision a nd ina ccura cy

observed for a meth od. P ra ct ica l tools a re described in Compu te r

Techn o l ogy f or Sel ec t i n g t h e R i gh t QC .

Wha t r ema ins is to provide a n effective w a y for implementinga well-designed QC procedure. See Com pu ter Techn ol ogy f or

Im p l emen t i n g QC R i g h t for a discussion of the softw a re fea tu res

that are needed to implement both simple and advanced QC

designs. Finally, some perspective on future directions in QC is

provided in the lesson QC – Sag e Ad v i ce on N ew QC A p -

p r oa ches .

References

1. Shewhart WA. Economic Control of Quality of Manufactured

P roduct. New York; D . Va n H ostr a nd C ompa ny, I nc., 1931.

2. Levey S, J ennings E R. The use of cont rol cha rt s in t he clinica l

la bora t ory. Am J C lin P a t hol 1950;20:1059-66.

3. Henry RJ , Sega love M. The running of sta nda rds in clinica l

chemist ry a nd th e use of the contro l cha r t . J Cl in P a tho l

1952;27:493-501.

4. Westga rd J O, G roth T, Aronsson T, Fa lk H , deVerdier C -H .

P erforma nce cha ra cterist ics of rules for interna l qua lity cont rol:

probabilities for false rejection and error detection. Clin Chem

1977;23:1857-67.

5. Westga rd J O, B a rry P L, Hunt MR, Gr oth T. A multi-rule Sh ewha rt

char t for qua l i ty contro l in c l in ica l chemist ry . Cl in Chem

1981;27:493-501.


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6. Westga rd J O, Ba rry P L. Cost-E ffective Qua lity Control: Mana ging

th e Quality a nd P roductivity of Ana lytica l P rocesses. Wa shington,

DC:AACC Press, 1986.


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