Cost Effectiveness of Automated

Multichannel Chemistry Analyzers

May 1981

NTIS order #PB81-209793
CASE STUDY #4

COST-EFFECTIVENESS

ANALYSIS OF

MEDICAL TECHNOLOGY

MAY 1981

BACKGROUND PAPER #2: CASE STUDIES OF

MEDICAL TECHNOLOGIES

CASE STUDY #4: COST EFFECTIVENESS OF

AUTOMATED MULTICHANNEL CHEMISTRY ANALYZERS

4

Milton C. Weinstein, Ph.D.

Professor of Policy and Decision Sciences

and

Laurie A. Pearlman, B.A.

Harvard School of Public Health

Boston, Mass.

OTA Background Papers are documents that contain information believed to be

useful to various parties. The information undergirds formal OTA assessments or is

an outcome of internal exploratory planning and evaluation. The material is usually

not of immediate policy interest such as is contained in an OTA Report or Technical

Memorandum, nor does it present options for Congress to consider.
Library of Congress Catalog Card Number 80-600161

For sale by the Superintendent of Documents,

U.S. Government Printing Office, Washington, D.C. 20402
Foreword

This case study is one of 17 studies comprising Background Paper #2 for OTA’s

assessment, The Implications of Cost-Effectiveness Analysis of Medical Technology,

That assessment analyzes the feasibility, implications, and value of using cost-effectiveness and cost-benefit analysis (CEA/CBA) in health care decisionmaking. The major, policy-oriented report of the assessment was published in August 1980. In addition

to Background Paper #2, there are four other background papers being published in

conjunction with the assessment: 1) a document which addresses methodological

issues and reviews the CEA/CBA literature, published in September 1980; 2) a case

study of the efficacy and cost-effectiveness of psychotherapy, published in October

1980; 3) a case study of four common diagnostic X-ray procedures, to be published in

summer 1981; and 4) a review of international experience in managing medical technology, published in October 1980. Another related report was published in

September of 1979: A Review of Selected Federal Vaccine and Immunization Policies.

The case studies in Background Paper #2: Case Studies of Medical Technologies

are being published individually. They were commissioned by OTA both to provide

information on the specific technologies and to gain lessons that could be applied to

the broader policy aspects of the use of CEA/CBA. Several of the studies were specifically requested by the Senate Committee on Finance.

Drafts of each case study were reviewed by OTA staff; by members of the advisory panel to the overall assessment, chaired by Dr. John Hogness; by members of

the Health Program Advisory Committee, chaired by Dr. Frederick Robbins; and by

numerous other experts in clinical medicine, health policy, Government, and economics. We are grateful for their assistance. However, responsibility for the case studies remains with the authors.

Ill
.—— . — — — —

Advisory Panel on The Implications of

Cost-Effectiveness Analysis of Medical Technology

John R. Hogness, Panel Chairman

President, Association of Academic Health Centers

Stuart I-I. Altman

Dean

Florence Heller School

Brandeis University

James L. Bennington

Chairman

Department of Anatomic Pathology and

Clinical Laboratories

Children’s Hospital of San Francisco

John D. Chase

Associate Dean for Clinical Affairs

University of Washington School of Medicine

\

Joseph Fletcher

Visiting Scholar

Medical Ethics

School of Medicine

University of Virginia

Clark C. Havighurst

Professor of Law

School of Law

Duke University

Sheldon Leonard

Manager

Regulatory Affairs

General Electric Co.

Barbara J. McNeil

Department of Radiology

Peter Bent Brigham Hospital

Robert H. Moser

Executive Vice President

American College of Physicians

Frederick Mosteller

Chairman

Department of Biostatistics

Harvard University

Robert M. Sigmond

Advisor on Hospital Affairs

Blue Cross and Blue Shield Associations

Jane Sisk Willems

VA Scholar

Veterans Administration
OTA Staff for Background Paper #2

Joyce C. Lashof, Assistant Director, OTA

Health and Life Sciences Division

H. David Banta, Health Program Manager

Clyde J. Behney, Project Director

Kerry Britten Kemp, * Editor

Virginia Cwalina, Research Assistant

Shirley Ann Gayheart, Secretary

Nancy L. Kenney, Secretary

Martha Finney, * Assistant Editor

Other Contributing Staff

Bryan R. Luce Lawrence Miike Michael A. Riddiough

Leonard Saxe Chester Strobel*

OTA Publishing Staff

John C. Holmes, Publishing Officer

John Bergling* Kathie S. Boss Debra M. Datcher

Patricia A. Dyson* Mary Harvey* Joe Henson

*OTA contract personnel.
Preface

This case study is one of 17 topics being issued that comprise Background Paper #2 to the

OTA project on the Implications of Cost-Effectiveness Analysis of Medical Technology. * T h e

overall project was requested by the Senate

Committee on Labor and Human Resources. In

all, 19 case studies of technological applications

were commissioned as part of that project.

Three of the 19 were specifically requested by

the Senate Committee on Finance: psychotherapy, which was issued separately as Background Paper #3; diagnostic X-ray, which will

be issued as Background Paper #5; and respiratory therapies, which will be included as part of

this series. The other 16 case studies were selected by OTA staff.

In order to select those 16 case studies, OTA,

in consultation with the advisory panel to the

overall project, developed a set of selection

criteria. Those criteria were designed to ensure

as a group the case studies would provide:

examples of types of technologies by function (preventive, diagnostic, therapeutic,

and rehabilitative);

examples of types of technologies by physical nature (drugs, devices, and procedures);

examples of technologies in different stages

of development and diffusion (new, emerging, and established);

examples from different areas of medicine

(such as general medical practice, pediatrics, radiology, and surgery);

examples addressing medical problems that

are important because of their high frequency or significant impacts (such as

cost );

examples of technologies with associated

high costs either because of high volume

(for low-cost technologies) or high individual costs;

examples that could provide informative

material relating to the broader policy and

methodological issues of cost-effectiveness

or cost-benefit analysis (CEA/CBA); and

● examples with sufficient evaluable literature.

On the basis of these criteria and recommendations by panel members and other experts,

OTA staff selected the other case studies. These

16 plus the respiratory therapy case study requested by the Finance Committee make up the

17 studies in this background paper,

All case studies were commissioned by OTA

and performed under contract by experts in academia. They are authored studies. OTA subjected each case study to an extensive review

process. Initial drafts of cases were reviewed by

OTA staff and by members of the advisory

panel to the project. Comments were provided

to authors, along with OTA’s suggestions for

revisions. Subsequent drafts were sent by OTA

to numerous experts for review and comment.

Each case was seen by at least 20, and some by

40 or more, outside reviewers. These reviewers

were from relevant Government agencies, professional societies, consumer and public interest

groups, medical practice, and academic medicine. Academicians such as economists and decision analysts also reviewed the cases. In all,

over 400 separate individuals or organizations

reviewed one or more case studies. Although all

these reviewers cannot be acknowledged individually, OTA is very grateful for their comments and advice. In addition, the authors of

the case studies themselves often sent drafts to

reviewers and incorporated their comments.

These case studies are authored works

commissioned by OTA. The authors are responsible for the conclusions of their specific case study. These cases are not statements of official OTA position. OTA does

not make recommendations or endorse particular technologies. During the various

stages of the review and revision process,

therefore, OTA encouraged the authors to

present balanced information and to recognize divergent points of view. In two cases,

OTA decided that in order to more fully

present divergent views on particular technologies a commentary should be added to

the case study. Thus, following the case

i’11
studies on gastrointestinal endoscopy and

on the Keyes technique for periodontal disease, commentaries from experts in the appropriate health care specialty have been

included, followed by responses from the

authors.

The case studies were selected and designed to

fulfill two functions. The first, and primary,

purpose was to provide OTA with specific information that could be used in formulating

general conclusions regarding the feasibility and

implications of applying CEA/CBA in health

care. By examining the 19 cases as a group and

looking for common problems or strengths in

the techniques of CEA/CBA, OTA was able to

better analyze the potential contribution that

these techniques might make to the management

of medical technologies and health care costs

and quality. The second function of the cases

was to provide useful information on the specific technologies covered. However, this was

not the major intent of the cases, and they

should not be regarded as complete and definitive studies of the individual technologies. In

many instances the case studies do represent excellent reviews of the literature pertaining to the

specific technologies and as such can stand on

their own as a useful contribution to the field. In

general, though, the design and the funding

levels of these case studies was such that they

should be read primarily in the context of the

overall OTA project on CEA/CBA in health

care.

Some of the case studies are formal CEAs or

CBAs; most are not. Some are primarily concerned with analysis of costs; others are more

concerned with analysis of efficacy or effectiveness. Some, such as the study on end-stage

renal disease, examine the role that formal

analysis of costs and benefits can play in policy

formulation. Others, such as the one on breast

cancer surgery, illustrate how influences other

than costs can determine the patterns of use of a

technology. In other words, each looks at evaluation of the costs and the benefits of medical

technologies from a slightly different perspective. The reader is encouraged to read this study

in the context of the overall assessment’s objectives in order to gain a feeling for the potential

role that CEA/CBA can or cannot play in health

care and to better understand the difficulties and

complexities involved in applying CEA/CBA to

specific medical technologies.

The 17 case studies comprising Background

Paper #2 short titles and their authors are:

Artificial Heart: Deborah P. Lubeck and John P.

Bunker

Automated Multichannel Chemistry Analyzers:

Milton C. Weinstein and Laurie A. Pearlman

Bone Marrow Transplants: Stuart O. Schweitzer and C. C. Scalzi

Breast Cancer Surgery: Karen Schachter and

Duncan Neuhauser

Cardiac Radionuclide Imaging: William B.

Stason and Eric Fortess

Cervical Cancer Screening: Bryan R. Luce

Cimetidine and Peptic Ulcer Disease: Harvey V.

Fineberg and Laurie A. Pearlman

Colon Cancer Screening: David M. Eddy

CT Scanning: Judith L. Wagner

Elective Hysterectomy: Carol Korenbrot, Ann

B. Flood, Michael Higgins, Noralou Roos,

and John P. Bunker

End-Stage Renal Disease: Richard A. Rettig

Gastrointestinal Endoscopy: Jonathan A. Showstack and Steven A. Schroeder

Neonatal Intensive Care: Peter Budetti, Peggy

McManus, Nancy Barrand, and Lu Ann

Heinen

Nurse Practitioners: Lauren LeRoy and Sharon

Solkowitz

Orthopedic Joint Prosthetic Implants: Judith D.

Bentkover and Philip G. Drew

Periodontal Disease Interventions: Richard M.

Scheffler and Sheldon Rovin

Selected Respiratory Therapies: Richard M.

Scheffler and Morgan Delaney

These studies will be available for sale by the

Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. 20402.

Call OTA’s Publishing Office (224-8996) for

availability and ordering information.
Case Study #4

Cost Effectiveness of Automated

Multichannel Chemistry Analyzers

Milton C. Weinstein, Ph.D.

Professor of Policy and Decision Sciences

and

Laurie A. Pearlman, B.A.

Harvard School of Public Health

Boston, Mass.

AUTHORS’ ACKNOWLEDGMENTS

Specifications and prices of current models of multichannel clinical chemistry

analyzers were provided by the following manufacturers’ representatives: Frank

Brevoort (E.I. du Pont de Nemours, Inc.); David Hardt, Janet Lea], Rusty Senac

(Hycel); and Vincent Stanley, Susan Werner (Technicon ). We gratefully acknowledge

their cooperation in this study.
Contents

Pag e

Summary . . . . . . . . . . . . . . . . . . . . . . . . . .

General Findings . . . . . . . . . . . . . . . . . . .

Product and Industry Characteristics. . . .

Quality of Measurement . . . . . . . . . . . . .

Diagnostic Value. . . . . . . . . . . . . . . . . . .

Clinical Efficacy . . . . . . . . . . . . . . . . . . .

Economic Efficiency. . . . . . . . . . . . . . . . .

Cost Effectiveness . . . . . . . . . . . . . . . . . .

Cardiac Enzymes . . . . . . . . . . . . . . . . . . .

Introduction . . . . . . . . . . . . . . . . . . . . . . . .

The Problem . . . . . . . . . . . . . . . . . . . . . .

Scope of the Case Study. . . . . . . . . . . . . .

Description of Multichannel Chemistry

Technology . . . . . . . . . . . . . . . . . . . . .

Overview of the Industry. . . . . . . . . . . . .

Continuous-Flow Analyzers. . . . . . . . . . .

Discrete-Sample Analyzers . . . . . . . . . . .

Centrifugal Fast Analyzers . . . . . . . . . . . .

Design Issues and Tradeoffs . . . . . . . . . . .

Future Trends . . . . . . . . . . . . . . . . . . . . .

Framework for Cost-Effectiveness

Evaluation . . . . . . . . . . . . . . . . . . . . . .

Efficiency and Cost Effectiveness . . . . . . .

Economic Efficiency. . . . . . . . . . . . . . . . .

Cost Effectiveness . . . . . . . . . . . . . . . . . .

Concluding Observations Based on the

Conceptual Framework . . . . . . . . . . . .

Economics of the Multichannel Analyzer. . .

Overview. , . . . . . . . . . . . . . . . . . . . . . . .

Review of Cost Data . . . . . . . . . . . . . . . .

Hypothetical Analyses of Efficiency. , . . ,

Fixed Costs of Production and the Rate

of Innovation . . . . . . . . . . . . . , . . . . . .

Induced Costs . . . . . . . . . . . . . . . . . . . . .

Financial Incentives . . . . . . . . . . . . . . . . .

Cost Effectiveness of Cardiac Enzymes in

Diagnosis of Myocardial Infarction:

A Structured Review . . . . ..., . . . . . .

Overview. . . . . . . . . . . . . . . . . . . . . ...,

Clinical Properties of the Cardiac

Enzymes. . . . . . . . . . . . . . . . . . . . . . . .

Structure for Decision Analysis . . . . . . . . .

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Quality of Measurement . . . . . . . . . . . . . 30

Diagnostic Value . . . . . . . . . . . . . . . . . . . 31

Clinical Efficacy.. . . . . . . . . . . . . . . . . . . 33

Cost Effectiveness . . . . . . . . . . . . . . . . . 33

Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . 34

References. . . . . . . . . . . . . . . . . . . . . . . . . . 35

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Case Study #4:

Cost Effectiveness of Automated

Multichannel Chemistry Analyzers

Milton C. Weinstein, Ph.D.

Professor of Policy and Decision Sciences

and

Laurie A. Pearlman, B. A.

Harvard School of Public Health

Boston, Mass.

SUMMARY

General Findings

1.

2.

3.

While the unit cost per chemistry determination has fallen considerably since multichannel analyzer technology first came into widespread use, the number of determinations

and total costs have increased.

Automated multichannel chemistry analyzers are most efficient, relative to alternative

methods, at high test volumes. This is because such analyzers tend to decrease variable costs per determination while increasing

fixed costs for equipment and labor.

The cost effectiveness of multichannel analyzers depends on the incremental health

benefits derived from the additional determinations that are required to make multichannel analyzers economically efficient

compared to alternative methods. An important issue, not yet resolved, is whether it

is necessary to demonstrate the cost effectiveness of multichannel chemistry screening

(hospital or outpatient) in order to demonstrate the cost effectiveness of multichannel

analyzers, or whether multichannel analyzers are the most efficient testing method even

if such high-volume uses are excluded.

Product and Industry Characteristics

4. There are two classes of multichannel analyzers: continuous-flow analyzers and dis5.

6.

crete-sample analyzers. One difference is

that discrete-sample analyzers perform only

the specific tests requested, whereas continuous-flow analyzers perform all tests on every

sample. Many manufacturers produce discrete analyzers, but only one makes analyzers that use the continuous-flow process.

New developments in multichannel technology are likely to include the availability of

more tests, more flexibility, and more automation of sample input and data output

functions. Processing speed is no longer a

major concern, largely because machine

startup time, sample collection and coding

time, and reporting time are now rate-limiting steps in the laboratory.

The rate of product turnover in this industry

is rapid; industry representatives suggest

that the state of the art is advanced significantly every 4 to 7 years. This high rate of

product development contributes to equipment costs. The increase in fixed costs, despite the role of innovation in reducing variable costs per test, may or may not be desirable given society’s interest in containing

health care costs. The appropriate role of reimbursement policies in encouraging or discouraging product innovation and capital

investment needs to be examined.

3
Quality of Measurement

7. The quality of measurement (i.e., accuracy

and precision) in multichannel analyzers is

generally regarded as quite good for most

tests, although high interlaboratory variations have been reported for some tests. This

consensus holds, despite the problem of

sample cross-contamination. It is incumbent

on those who interpret test results, however,

to be aware of the range of interlaboratory

variation.

Diagnostic Value

8. Diagnostic value depends not only on the

sensitivity and specificity of the test in question, but also on the prevalence of the condition for which the test is performed. Where

multichannel analyzers are used to screen for

unsuspected abnormalities, the prevalence

of each condition screened for will be quite

low; therefore, the predictive value of any

positive test will be quite poor. This state of

affairs has led to increased induced costs for

subsequent tests to rule out diagnoses following falsely positive test results.

Clinical Efficacy

9. Clinical efficacy of a laboratory test depends

on the ability of the test results to influence a

subsequent treatment decision and on the

health benefits to be derived from such a decision. Evaluation of the clinical efficacy of

the multichannel analyzer depends, therefore, on its uses. The clinical efficacy of routine chemistry profile screening is controversial, although some patients clearly benefit

from such screening.

Economic Efficiency

10. The economic efficiency of any configuration of laboratory equipment may be defined as its ability to respond to a specified

pattern of test orders at the lowest cost.

Multichannel equipment is most efficient at

high test volumes and in cases where the

average number of determinations required

per sample is high.

11. The economic efficiency of any particular

number of channels, or of any particular

combination of tests to occupy those channels, has not been analyzed systematically

from a societal perspective.

Cost Effectiveness

12.

13.

14.

A complete evaluation of the cost effectiveness of multichannel analyzers depends on

cost effectiveness of each individual test for

each of its major uses. Such analysis is feasible with use of decision-analytic methods.

A major consideration in evaluating the

economic impact of the multichannel analyzer is the induced cost for diagnosis and

treatment of patients with abnormal test results. Induced costs include costs of repeat

chemistry profiles and individual tests,

more specific laboratory tests to confirm a

diagnosis, radiographic and other diagnostic tests, and therapeutic interventions. In

the evaluation of cost effectiveness, these

induced costs must be combined with the

cost of the initial chemistry tests. However,

many of these induced costs may be the result of inappropriate (i. e., cost ineffective)

decisionmaking in response to abnormal

test results; if so, the cost effectiveness of

initial chemistry tests may depend on

whether or not one assumes cost-effective

decisionmaking will follow from the

results.

The cost

analyzer

volumes

efficient

effective.

effectiveness of the multichannel

depends on whether its uses at

sufficient to render the analyzer

are, themselves, considered cost

If health care resources are to be

allocated optimally, the judgment as to

whether a particular use is cost effective at

the margin should depend on society’s costeffectiveness criterion value (i. e., the cost

per unit of health benefit it is able or willing

to pay, given the limit on total resources

and competing health-care demands for

those resources). The marginal, low-yield

uses of chemistry tests, therefore, are cost

effective only if resources are sufficiently

plentiful to accommodate a high cost-effec-
Case Study #4: Cost Effectiveness of Automated Multichannel Chemistry Analyzers ● 5

tiveness criterion. Thus, paradoxically, the

cost effectiveness of the multichannel analyzer may rest on whether we, as a society,

are willing to pay enough for diminishing

incremental health benefits to justify sufficient volumes of testing to permit us to afford the reduction in unit testing costs that

multichannel analyzers offer.

Cardiac Enzymes

15.

16.

Three cardiac enzymes—creatine phosphokinase (CPK), lactic dehydrogenase (LDH),

and aspartate aminotransferase, also called

serum glutamic oxaloacetic transferase

(SGOT)—and their isoenzymes were selected as examples of chemistry tests that

are frequently ordered on multichannel

equipment. CPK, LDH, and SGOT tests

are available on all major multichannel

analyzers; isoenzymes of CPK and LDH are

becoming available on the equipment of at

least one manufacturer. Their major use is

to diagnose (“rule in/rule out”) myocardial

infarction (’[heart attack”). In the emergency room, they are used to decide whether to

admit a patient to the hospital; in the coronary care unit, they are used to monitor a

patient’s condition and to decide when to

discharge the patient to the general ward.

The diagnostic value of cardiac enzymes is

difficult to evaluate, because the levels of

these enzymes are themselves used to define

the diagnosis of myocardial infarction. No

published study has examined the incremental diagnostic information value of

any single enzyme test, given the other

two; of two, given the other one; of the enzymes, given the isoenzymes; or of the isoenzymes, given the enzymes. Such evaluations could lead to cost savings if some of

the tests were found to provide information

INTRODUCTION

. . . In recent years, the economic and technologic development of laboratory operations

seem to have had a greater influence than the

physician on trends in laboratory utilization

(17).

17.

18.

largely supplied by other tests in the

management of myocardial infarction.

The clinical efficacy of tests measuring

cardiac enzymes and isoenzymes depends

ultimately on the risks associated with failing to hospitalize a patient with myocardial

infarction, or on the incremental risk associated with complications suffered outside

a coronary care unit. Even if these tests improved the accuracy of diagnosis, or even

prognosis, the value of this clinical information would be nil unless treatment

made a difference in health outcome. The

efficacy of coronary care units and of hospitalization of patients with myocardial infarction is a matter of much controversy

and is not likely to be resolved soon. Therefore, the health benefits to be derived from

a more sensitive or specific diagnosis of

myocardial infarction remain uncertain.

As long as coronary care and hospitalization are standard practice, the greatest value of cardiac enzyme and isoenzyme measurement may be induced economic savings.

To the degree that these tests improve the

sensitivity of the diagnosis, physicians may

be more willing to rule out myocardial infarction on the basis of negative findings.

To the degree that they improve specificity

of diagnosis, they are less likely to lead to

hospital admission of patients without

myocardial infarction. An economic evaluation of the impact of enzyme and isoenzyme measurements is feasible and may

be sufficient to demonstrate their cost effectiveness given actual practice, although it is

difficult to assess the cost effectiveness of

such measurements in ideal practice because of the uncertainty surrounding the

benefits of intervention.

The Problem

Since 1963, when Technicon Corp. began to

market the first automated chemistry analyzer

that could perform more than one test on a
6 ● Background Paper #i?: Case Studies of Medical Technologies

single sample of serum, this technology has had

a profound economic impact on the U.S. health

care system. The question still being debated

among industry officials, health services researchers, hospital administrators, laboratory

directors, and clinical chemists and pathologists

is whether this technology has been, on balance,

an economic dream or nightmare.

Evidence is strong on both sides of the question. On the one hand, largely as a result of

automation and especially as a result of multiple-channel technology, the cost per chemistry

determination has fallen dramatically during the

past decade. This cost reduction has resulted, in

large part, from increased productivity of laboratory personnel as measured by the number

of determinations per labor-hour. One British

study showed a tripling of laboratory output

per technician-hour between 1955 and 1966 in a

hospital that had begun to use a 12-channel

analyzer during that interval (55). A U.S. study

showed a doubling of laboratory output per

labor-hour from 1961 to 1970, during which

time multichannel analyzers were purchased by

three of the five hospitals in the study (30).

The importance of such savings in labor costs

is underscored by studies that indicate that

direct labor costs constitute 50 to 65 percent of

the total direct costs of hospital-based, clinical

chemistry laboratories (30,35,53,55). Comparisons of different studies indicate that the average direct cost of a chemistry determination was

approximately $1.25 in 1969 (in hospitals that

had not adopted a 6- or 12-channel analyzer)

(30), $0.70 in 1971 (35), and $0.30 in 1974 (32).

This trend has persisted despite a 35-percent increase in price levels from 1969 through 1974.

On the basis of the continued decline in prices of

reagents and consumables per test (10,23,46)

and the increased labor productivity made possible by the availability of 20- and 30-channel

analyzers, one can conclude that the real cost

per test has continued to decline.

The problem is that, despite the dramatic reductions in unit costs, total expenditures on

clinical chemistry tests have risen dramatically.

Between 1971 and 1975, the number of chemistry determinations performed in the United

States increased by 67 percent—from 2.9 billion

to 5 billion. During that period, charges (not

true costs) for these tests increased from $5.6

billion to $15 billion (34). These figures do not

reflect the concomitant increase in costs of tests

(other than chemistry) induced by unexpected

results and required to rule out or confirm a

diagnosis.

Underlying this alarming trend in laboratory

costs is the increased frequency with which physicians order multiple-test profiles to be performed on automated equipment, rather than

just individual tests needed for the immediate

diagnosis. If, at one extreme, all of the tests had

clinical value equal, on average, to the tests that

would have been ordered without the availability of test panels, then the cost per unit of health

benefit would have clearly decreased as a result

of multichannel technology. If, at the other extreme, most of the additional determinations

had no clinical value, then the cost per useful

determination—hence, the cost per unit of

health benefit—would have increased as a result

of multichannel technology.

The truth, no doubt, lies between these extremes. Therein lies the dilemma in evaluating

the cost effectiveness of multichannel chemistry

analyzers. In essence, their cost effectiveness

depends on the way they are used in clinical

practice. It depends not only on their ability to

reduce costs or improve efficacy of tests that

would have been performed by other methods

in their absence, but also on the benefits of new,

high-volume, uses of the chemistry lab (e.g.,

hospital admission screening) induced by having more tests at low incremental, but high

fixed, cost.

Scope of the Case Study

The remainder of this case study is in four

parts. In the first part, by way of background,

we offer a brief review of the history of multichannel clinical chemistry technology; we also

describe several analyzers that exemplify the

technology as currently marketed in the United

States. Next, we present an analytic framework

for evaluating the cost effectiveness of the multichannel analyzer, pointing out in the process the

methodological problems inherent in evaluating

a diagnostic technology that consists of many
Case Study #4: Cost Effectiveness of Automated Multichannel Chemistry Analyzers ● 7

elements, each of which may have many clinical

uses. Within this analytic framework, we

survey the evidence that bears on the question

of cost effectiveness. Third, we review the

available data concerning the costs of multichannel chemistry analysis. Finally, we use the

analytic framework to examine the evidence

concerning the cost effectiveness of using the

cardiac enzymes and isoenzymes (for which

tests are becoming more commonly available on

multichannel analyzers) in the diagnosis of

myocardial infarction. The review of the data

on cardiac enzymes has more general implications for evaluation of not only the cost-effective use of the individual tests that can be performed with multichannel analyzers, but also

the cost effectiveness of developing multichannel capability to perform such tests.

This case study touches on only some of the

aspects of technology assessment that might be

undertaken with respect to multichannel chemistry analyzers. Thus, for example, we pay only

passing attention to the question of test precision and accuracy, one that has received considerable attention in the literature (5,28) and in

the regulatory activities of the Food and Drug

Administration. Moreover, we do not consider

the impact of multichannel chemistry analyzers

on the organization of laboratory services or

health care institutions. Rather, we concentrate

on the issues of cost and cost effectiveness of

multichannel chemistry analyzers.

For purposes, of this case study, we consider

only instruments designed to yield more than

one chemical determination on a single blood

sample. Thus, for example, the study excludes

the centrifugal fast analyzer, which (despite its

ability to process from 15 to 39 samples at a

time at very high speed) is technically a singlechannel analyzer., Evaluation of the centrifugal

fast analyzer technology as an alternative to the

multichannel analyzer technology would be of

great value at this early stage of its diffusion,

but is beyond the scope of the present study.

DESCRIPTION OF MULTICHANNEL CHEMISTRY TECHNOLOGY

Overview of the Industry

Development of the first continuous-flow

analyzer, begun by Leonard T. Skeggs in 1950,

led to the introduction of Technicon’s AutoAnalyzer to the U.S. market in 1957. At about

the same time, the first discrete-sample analyzers, the Robot Chemist and the AutoChemist,

were marketed in the United States by American

Optical and AGA, respectively (l). Continuousflow and discrete-sample remain the two types

of multichannel chemistry analyzers available

today, although considerable sophistication and

versatility have evolved.

The selling feature of a multichannel analyzer

is its ability to per-form numerous tests simultaneously on one blood sample. This feature

distinguishes multichannel analyzers from

dedicated machines, which can run only one test

at a time, and from centrifugal fast analyzers,

which currently perform only one test at a time,

although at very high speeds.

Although there is a large and growing market

for automated multichannel analyzers (sales in

1978 were estimated at $170 million) (23), the

field is dominated by a small number of firms.

The undisputed market leader is Technicon;

probably one-third to one-half of all automated

multichannel analyzers in use are Technicon instruments. 1 Other firms that manufacture automated multichannel analyzers include Abbott

Laboratories, American Instrument, American

Monitor, Beckman Instruments, Chemetrics,

Coulter Electronics, E. I. du Pont de Nemours,

Inc. (Du Pont), Gilford, Hycel, Micromedic

Systems, Ortho Diagnostics, Perkin-Elmer,

Union Carbide, and Vickers.

Y

., . .

.!

I Figures on market shares are not available to the public. Market

research tirms pr{wide this kind of information for substantial

tees, but the results are available only to their clients, Our estimates are based (m conversations with individuals familiar with

the industry, on Hycel’s 1978 report to the Security Exchange

C{}mmlssion (23), and on Schwartz’s discussion of data from the

Center trom Dwease Control (42).
. .

8 ● Background Paper #2: Case Studies of Medical Technologies

This study concentrates on the products of

three firms: Technicon, Hycel, and Du Pont.

These firms differ both in size and in the importance of automated chemistry analyzers in

their overall sales pictures. Although they do

not exhaust the market, their products exhibit

enough variety to give a good sense of the market. Basic product information for the instruments reviewed below is shown in table 1.

Continuous-Flow Analyzers

General Description

Details of the continuous-flow process are described clearly by Schwartz (42). The process

used today is a modification developed by Leonard Skeggs in the 1950’s. In brief, one serum

sample is split into numerous aliquots (parts)

separated by air bubbles. The partitioned sample passes through separate incubation and detecting modules. Samples and reagents are

drawn through the system by a pump. The flowing reactants are mixed as they pass through

glass tubes in the form of concentric helixes.

In addition to dividing the sample, the air

bubbles regulate the flow and clean the tubes

between sample portions. Reagents are added in

the required sequence, and everything flows

along to the module where the appropriate

measurements are taken at the endpoints of the

reactions. The results are recorded on a strip

chart recorder and may a I so be sent to a computer. Every chemical test is run every time a

sample passes through the system; hence, reagents are consumed whether or not a test is requested or reported. It is possible, however, to

deactivate one or more channels on any particular day, thus reducing the number of tests run.

Technicon Corp.

Technicon is the only manufacturer of continuous-flow analyzers. It purchased the original rights to the process from Leonard Skeggs in

1954. Since the introduction of its AutoAnalyzer in 1957, Technicon has held patents that

preclude the use of the continuous-flow technique by other manufacturers. That technique is

used in all of Technicon’s multichannel analyzers, which are all variants of the Sequential

Multichannel Analyzer (SMA), introduced in

1965.

Four of Technicon’s multichannel instruments, the SMA 6/60, SMA 12/60, SMA II, and

Sequential Multichannel Analyzer with Computer (SMAC), are described below.2 Purchase

prices and other specifications are given in table

1. In all, 23 tests are available on any of Technicon’s machines (see table 2).

Technicon’s primary business is the development, production, marketing, and servicing of

automated analytical systems, including reagents. Technicon is generally agreed to be the

‘Technicon’s Auto Analyzer 11 is not discussed in this report.

That analyzer is available in two- and three-channel models, as

well as a basic single-channel model.

Table 1 .—Multichannel Analyzers Produced by Three Major Manufacturers

Number of Number of tests Number of samples

Manufacturer Model channels available processed per hour List price

Technicon Corp. . . . . . . . . . . . . . . . SMA 6/60 6 23 60 $ 52.000

Hycel, Inc. . . . . . . . . . . . . . . . . . . . .

E. 1. du Pont de Nemours & Co. . . .

SMA 12/60

SMA II

SMA II

SMAC

Super-17

SKS-60

Hycel-M

ACA-I

ACA-II

ACA-II

ACA-III

12

12

18

20

17

17

30

30

30

60

625 b

23

23

23

23

18

15

24

29

38

38

38

60

90

90

150

60

60

120

97a

97a

97a

97a

99,500

138,600

173,250

271,000

75,000

120,000

225,000

49,000

69,000

89,000

120,000

afq~mbgr of tjgtgrminations per hour.

bRefers to software capabditles.

SOURCE: Personal communications and marketing materials from the manufacturers (10,24,46).
Case Study #4: Cost Effectiveness of Automated Multichannel Chemistry Analyzers ● 9

Table 2.—Chemical Determinations Available on Technicon, Hycel, and Du Pont Multichannel Analyzers

Technicon

(continuousf low) Hycel (discrete-sample) Du Pont (discrete-sample)

Test All models Super-17 SKS-60 Hycel-M ACA-I ACA-II ACA-III

Acid phosphatase . . . . . . . . . . . . . . . . . . . x x x

Alanine aminotransferase (SGPT) . . . . . . X x x x x x

Albumin . . . . . . . . . . . . . . . . . . . . . . . . . . . X x x x x x x

Alkaline phosphatase . . . . . . . . . . . . . . . . X x x x x x x

Alpha-hydroxybutyrate dehydrogenase . x x x x

Ammonia . . . . . . . . . . . . . . . . . . . . . . . . . . x x

Amylase . . . . . . . . . . . . . . . . . . . . . . . . . . . x x x

Aspartate aminotransferase (SGOT) . . . . X x x x x x x

Bilirubin, total . . . . . . . . . . . . . . . . . . . . . . X x x x x x x

Bilirubin, direct . . . . . . . . . . . . . . . . . . . . . X x x x x

Bilirubin, neonatal. . . . . . . . . . . . . . . . . . . x x

Calcium . . . . . . . . . . . . . . . . . . . . . . . . . . . X x x x x x x

Carbon dioxide . . . . . . . . . . . . . . . . . . . . . . X x x x x

Cerebrospinal fluid protein. . . . . . . . . . . . x x x

Chloride . . . . . . . . . . . . . . . . . . . . . . . . . . . X x x x x x

Cholesterol . . . . . . . . . . . . . . . . . . . . . . . . . X x x x x x x

Creating phosphokinase(CPK) . . . . . . . . X x x x x x

Creatine phosphokinase-MB (CPK-MB). . x x

Creatinine . . . . . . . . . . . . . . . . . . . . . . . . . . X x x x x x x

Ethanol . . . . . . . . . . . . . . . . . . . . . . . . . . . . x x x

Gamma-glutamyl transferase . . . . . . . . . . X x x x

Glucose . . . . . . . . . . . . . . . . . . . . . . . . . . . X x x x x x x

Iron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X x x x x

Lactic acid . . . . . . . . . . . . . . . . . . . . . . . . . x x x

Lactic dehydrogenase(LDH) . . . . . . . . . . X x x x x x x

Lactic dehydrogenase, liver . . . . . . . . . . . x x x

Lipase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x x

Magnesium. . . . . . . . . . . . . . . . . . . . . . . . . x x x

Phenobarbital . . . . . . . . . . . . . . . . . . . . . . x x

Phenytoin . . . . . . . . . . . . . . . . . . . . . . . . . . x x

Phosphorus, inorganic . . . . . . . . . . . . . . . X x x x x x

Potassium . . . . . . . . . . . . . . . . . . . . . . . . . X x x

Primidone. . . . . . . . . . . . . . . . . . . . . . . . . . x x

Protein, total . . . . . . . . . . . . . . . . . . . . . . . X x x x x x x

Pseudocholinesterase . . . . . . . . . . . . . . . x x x

Salicylate . . . . . . . . . . . . . . . . . . . . . . . . . . x x x

Sodium . . . . . . . . . . . . . . . . . . . . . . . . . . . . X x x

Triglyceride . . . . . . . . . . . . . . . . . . . . . . . . X x x x x x

Urea nitrogen(BUN) . . . . . . . . . . . . . . . . . X 1 x x x x x

Uric acid . . . . . . . . . . . . . . . . . . . . . . . . . . . X x x x x x x

SOURCE: Personal communications and marketing materials from the manufacturers(10,24,46)

market leader, responsible for perhaps 40 to 50

percent of all sales of automated multichannel

chemistry analyzers in the United States.

“Clinical systems” represented 57 percent of its

total 1978 revenues of more than $275 million.

Another 27 percent of Technicon’s 1978 revenues were contributed by sales of reagents and

consumables .

SMA 6/60 and SMA 12/60.—These noncomputerized analyzers are descended directly from

the SMA 12 “Hospital Model” that was introduced in 1965. Their names indicate that they

have 6 and 12 channels, respectively, and that

each is capable of processing 60 samples per

hour. Both analyzers are equipped with aflame

photometer for analyzing serum electrolytes.

The manufacturer estimates that at least 2,000

SMA 6/60s and 5,000 12/60s (first sold in 1969)

are currently in place (46).

SMA II.–The SMA II was first sold in 1977,

and the manufacturer estimates that several

hundred SMA IIs are now in place. Two different models are available, one with 12 channels (SMA II-12) and the other with 18 (SMA
10 ● Background Paper #2: Case Studies of Medical Technologies

11-18). The SMA 11 has a computer that can be

used for extensive report production. A flame

photometer is used for analyzing serum electrolytes. Both models process 90 samples per

hour. The SMA II requires smaller sample sizes

than do the SMA 6/60 and 12/60. In the SMA

11-18, which is, in effect, composed of a 6-

channel and a 12-channel module, reagent economy is improved somewhat, since only the

modules that contain tests included in the test

order are activated.

SMAC.—SMAC is the largest of Technicon’s

automated multichannel analyzers. It has 20

channels and can process 150 samples per hour.

SMAC is computerized, allowing for extensive

report preparation, computerized verification

that all operations are occurring in their appropriate sequence, and selective reporting of abnormal results, among other functions. Since

the first sale in 1974, more than 1,000 SMACs

have been sold. This analyzer offers more specific chemical methods than previous Technicon

instruments. Electrolytes are analyzed by an

ion-selective electron method instead of by the

traditional flame photometer.

Discrete-Sample Analyzers

General Description

Discrete-sample analyzers, in effect, replicate

the manual process of testing. These devices

perform a number of tests sequentially on one

sample. Opinion is divided on whether the

speed, accuracy, and precision of discretesample analyzers are comparable to those of the

continuous-flow analyzers (42,43). Increasing

sales indicate, however, that these analyzers fill

what many clinical laboratory directors perceive to be a need.

A discrete-sample processor is a collection of

relatively independent, general purpose channels that are run in parallel. The channels are

tied together at three points: 1) sample presentation, made in sequence to successive channels;

2) sample transport, at several reaction temperatures; and 3) data acquisition, accomplished through a computerized system that

reads dedicated detectors in each channel. Users

may select only those tests whose results are

needed; other tests will not be run,

The prototypes for the discrete sample analyzers were American Optical’s Robot Chemist

and AGA’s AutoChemist. Today, there are

many models available. The models of two

manufacturers, Hycel and Du Pent, are discussed below.

Hycel, Inc.

Hycel is primarily a manufacturer of clinical

chemistry analyzers. This company derives a

large share of its revenues from sales of automated analyzers and related reagents. In 1978,

37 percent of Hycel’s total revenues of almost

$39 million were from the sale of clinical systems and 39 percent were from sales of reagents

to be used with automated analyzers (23).

Among Hycel’s products are three multichannel chemistry analyzers: the Super-Seventeen,

the SKS-60, and the Hycel-M. These analyzers

range in price from $75,000 to $225,000 (see

table 1). The prices of the machines differ depending on the number of channels and tests

available, output as measured in test results per

hour, and the test methods used.

Super-Seventeen. —Introduced in 1975, the

Super-Seventeen is a computerized, programmable multichannel analyzer with 17 channels

that is capable of performing 18 tests (see table

2). The Super-Seventeen uses a flame photometer to measure electrolytes. It is capable of

processing 60 samples per hour, yielding 1,020

determinations. Patient data are stored on

cassettes.

SKS-60.—First sold in 1978, the SKS-60 has

17 channels and is capable of performing 15

tests (see table 2). In place of the traditional

calorimetric methods used by most analyzers,

the SKS-60 employs newer, more specific enzymatic test methods that are thought to provide more reliable results because of reduced interference. The following functions are under

computer control: reagent dispense timing, incubation, calculation of chemistry results, and

system calibration. If the appropriate tests are

selected, globulin, AG ratio, and BUN/creatinine ratio are calculated and printed out

automatically. The SKS-60 can process. 60

samples per hour, yielding 900 determinations.
Case Study #4: Cost Effectiveness of Automated Multichannel Chemistry Analyzers ● 11

Hycel-M.—The Hycel-M is Hycel’s newest

automated analyzer at this writing. This computerized analyzer has 30 channels and is capable

of processing 120 samples per hour. Twentyfour tests are now available (see table 2). Any

combination of individual tests, panels, or profiles may be selected by the operator. The

machine is equipped with a microprocessor that

processes samples and another processor that

manages data.

E. I. du Pont de Nemours & Co.

Du Pont is a large firm with many interests

and sources of revenue, among which automated chemistry analyzers and reagent sales are

relatively much less important than they are to

Technicon and Hycel. Nonetheless, Du Pont’s

Automatic Clinical Analyzer (ACA) line is being expanded and refined, and sales are considerable in terms of market penetration. The company estimates that 30 to 40 percent of hospitals

wit h more than 100 beds have an ACA in the

clinical laboratory; about 70 percent of the

ACAs are thought to exist alongside other

manufacturers’ equipment. Du Pont emphasizes

small-batch processing, so its equipment is often

used to complement the larger processors. The

ACA was first sold in 1970; ACA sales recently

passed the 2,000 mark (10).

Du Pont manufactures three multichannel

analyzers: the ACA-I, ACA-II, and ACA-III.

The number of channels and tests, output

speeds, and purchase prices are shown in table

1. All three machines are mechanically similar.

Each operates with an individual test pack for

each analysis. The analyzer is loaded with a

number of samples and, for each sample, the

correspondin g test packs containing the appropriate reagents, The test pack serves as both

reaction chamber and cuvette for photometric

analysis. Either standard or enzyme methods

can be incorporated into the test packs. Under

computer control, the proper amounts of sample and diluting agent are injected into the test

packs. Temporary seals around the reagents in

the packs are broken with hydrostatic pressure.

The analyzer then mixes the reagents, waits a

predetermined amount of time, and measures

the outcome of the reaction. One test pack is accepted every 37 seconds, so a total of 97 determinations can be made per hour.3 

The number of channels refers not to physical

apparatus, but to the maximum number of tests

that can be programed into the machine at any

one time. The ACA works best in a lab that runs

only a few profiles or many individual tests each

day, or in a lab needing fast, accurate emergency or small-batch test processing. It would not

compete with a multichannel instrument in a lab

where dozens of multiple-test profiles were run

each day.

ACA-I.—This analyzer has 30 channels and

can perform 29 different clinical tests (see table

2). It produces a report slip that has two parts: a

photographic reproduction of the handwritten

sample identification card, and a computerprinted list of the test names and numerical

results.

ACA-II.—The ACA-II is available in a 30- or

a 60-channel version. Thirty-eight test methods

can be selected by the purchaser (see table 2).

One of these, the CPK-MB, has been “technically released” (i.e., it is not yet universally

available). Results are provided by the ACA-II

in the same format as results provided by the

ACA-I.

ACA-III.—This machine is similar in many

respects to the other ACAs. The main difference

is that it contains a microprocessor, which

makes it programmable and thus very versatile.

The ACA-III can be programed to run tests on

as many as 625 channels in sequence. At pres-

‘Alth{mgh the ACA I S, strictly speaking, a multlchdnne]

analyzer, because It autt~matlcally withdraws the apprc>pr]ate

am(wnt (d sample tr{}m a sample cup to perform many tests, It

Iuncti(ms in many respects like a single-channel analyzer. Dllterent

tests are fully c(mtalned within the consumable test packs and

passed through the machine In a c(~nvey[~r-belt manner. The tlrst

test pack enters the tlrst preheat area within the temperature chamber t(dlowin~ It\ associated sample cup. Thirty-seven seconds

later, the “conveyor’’” IS Indexed tiwward, and the Ilrst test pack

m(~ves to the sec(md preheat area wlthi n the temperature chamber,

while the second test pack m(wes into the tlrst preheat area.

An(]ther 37 sec(mds later, the system indexes torward, m(}vlng the

tlrst test pack t{} the next stati{m, the sec{>nd pack til the second

preheat area, and the third test pack to the tlrst preheat area. The

pr[)ce~s c[~ntlnues In thi~ way, with the tirst test pack eventually

reaching the ph~~t(~meter, where the results are react. In another 37

wct)nds, that test resu I t IS printed (w t and the second test pack

enters the ph(lt(~meter area. Thus, a determination IS made every

37 sec(~nds.
12 ● Background Paper #2: Case Studies of Medical Technologies

ent, only 38 chemistry tests are available (see

table 2). But since Du Pont has been introducing

new tests (or methods) at a rate of four or five

per year, the enormous expansion capability of

the ACA-III may eventually be useful. The

microprocessor can also reprogram the machine

to accept new methods for performing existing

tests; provide the flexibility to run adult and

pediatric sample sizes simultaneously; and produce a more extensive printout of results, including an indication that a particular result is

abnormal, along with the range of normal

values for the test, the units of measure for the

test results, and the time of day the test was run.

Centrifugal Fast Analyzers

Centrifugal fast analyzers were developed at

the Oak Ridge National Laboratory. The first

commercial machine was sold in 1970. These

analyzers are designed to run a single test on a

large number of samples—the reverse of the

multichannel concept of running a large number

of tests on a single sample. The commercially

available centrifugal fast analyzers today include the GEMSAEC (Electronucleonics, Inc. ),

the CentrifiChem (Union Carbide), and the

Rotochem II (AMINCO). These analyzers do

not compete directly with the high-speed, multichannel analyzers such as SMAC and Hycel-M

at present, but they may within the next few

years.

Design Issues and Tradeoffs

Accuracy and Precision

For both continuous-flow and discrete-sample

analyzers, there is a tradeoff between speed and

reduced variable cost, on the one hand, and accuracy and precision, on the other. The tradeoff

has been most clearly recognized in the design of

continuous-flow analyzers; the problem of “carryover” from one sample to the next in the common tubing of these analyzers has been studied

at length (s). There are also problems of sample

cross contamination in discrete-sample analyzers. Companies have directed their product improvement efforts (e.g., in Technicon’s SMA II

and SMAC) to minimizing the carryover problem; despite this problem, however, the accuracy and precision of multichannel instruments

have long been recognized to be superior to

those of manual methods (54).

Another theoretical problem with continuous-flow analyzers and certain discrete-sample

analyzers has to do with the timing of reactions.

If reactions are all required to be synchronized,

then suboptimal accuracy must be tolerated for

the slower reactions, or a reduction in the

overall processing rate must be made to accommodate the slowest reactions, or additional

channels must be created to split the fastest reactions into two or more parts.

Speed

The ability of automated equipment to analyze specimens rapidly is increasing to the point

where the speed of analysis is no longer the ratelimiting step in the overall testing process (28).

Limits in the ability to speed up the specimen input and coding system and to record and report

results make further increases in equipment

speed of little value. This is reflected in the

relatively modest sales in the United States of

the Vickers M300, which can analyze 300 samples per hour on 20 channels, yielding 6,000

determinations per hour. Concerns for future

development of automated equipment will shift

to improved versatility and reliability, as

evidenced by Technicon’s development of the

more flexible, intermediate-sized SMA II and

Du Pont’s development of increased test selection in the ACA-II and ACA-III.

Selectivity y

Discrete-sample analyzers have an apparent

cost advantage over continuous-flow analyzers,

because they have the capability of performing

only those tests desired for each sample, thus

not only saving reagents, disposable, and channel maintenance, but also minimizing the number of unsolicited tests. On the other hand, the

reagent cost per test is so low with the available

continuous-flow instruments that this advantage may be more theoretical than real.4

4

See review of cost data below in the part of this case study on

the economics of the multichannel analyzer.
Case Study #4: Cost Effectiveness of Automated Multichannel Chemistry Analyzer-s ● 13

Future Trends

Instruments

The rate of product turnover in the market for

multichannel chemistry analyzers is rapid. Manufacturers, in their user cost estimates, suggest

that machines be depreciated over 4 to 7 years,

although the machines’ useful life could be much

longer (1,22). High corporate expenditures on

product development -e.g., in 1978, Technicon

and Hycel spent 7.4 and 8.7 percent, respectively, of their sales revenues on research and development (23,45) — undoubtedly contribute to the

rapid rate of turnover. That Technicon spent $7

million to develop SMAC (34), which must be

recovered in the purchase price of equipment,

further suggests possible inefficiencies in the

equipment replacement rate.

The rapid growth of centrifugal fast analyzers

in this market cannot be ignored. Despite their

single-channel capability, such analyzers are

already competing successfully in the same

market as the multichannel instruments.

Chemistry Tests

A number of chemistry tests that have heretofore been rather expensive and had to be performed manually will be available in the near

future on multichannel analyzers, especially on

discrete-sample systems. Examples include isoenzymes of creatine phosphokinase (CPK-MB),

high-density lipoproteins, renin, and vanillylmandelic acid (VMA). If added to multichannel

equipment, each of these will undoubtedly increase in use, and the possible implications for

clinical practice in the areas in which these

tests are used (e.g., diagnosis of myocardial infarction and coronary disease, diagnosis and

treatment of hypertension) should be examined

carefully.

FRAMEWORK FOR COST= EFFECTIVENESS EVALUATION

Efficiency and Cost Effectiveness

There are at least two levels of questions one

might ask concerning the cost effectiveness of

the automated multichannel analyzer. The question at the simpler level is whether, for a given

pattern of test ordering (i. e., for a specified

number of each type of test ordered per unit of

time), a Particular class of automated multichannel analyzers can produce results at lower

cost than (and with accuracy and precision at

least equal to) alternative methods. This may be

called the question of economic efficiency. An

evaluation of the economic efficiency of multichannel analyzers for any particular pattern of

utilization would be straightforward, given data

on the various components of testing costs. s

Even with information on the most efficient

means of producing a specified set of test results

over time, however, it would remain to be determined whether any particular pattern of utilization, and therefore any particular analyzer,

is cost effective. This determination would reThese are revlevveci below In the part ot this case study on the

econ(lmic~ 01 the mllltlchannel a ndlvzer,

quire answers to a more complex set of questions, which concern not only the costs of

testing, but also the induced costs and health

benefits attributable to the many available

chemistry tests in their various clinical uses.

One important limitation of the analytic

framework described below ought to be mentioned. The analytic approach is designed to

permit evaluations of the cost effectiveness of

discrete clinical chemistry technologies (e.g.,

automated analyzers v. manual, multichannel

v. single channel)—not of the cost effectiveness

of configurations of equipment in a clinical

laboratory. Although evaluation of the latter

would surely be the more realistic basis for

policy, we offer this more microlevel approach

as a necessary step toward that complex undertaking.

Economic Efficiency

Available data concerning the question of

economic efficiency in the production of test

results are reviewed in the part of this case study

on the economics of the multichannel analyzer.
14 ● Background Paper #2: Case Studies of Medical Technologies

Here we offer a conceptual framework for

analysis of the efficiency question.

Fixed and Variable Costs

Both economic theory and cost-accounting

principles offer methods of classifying costs for

purposes of analysis. One distinction that is central to the evaluation of multichannel analyzers

is that between costs that do not vary with the

volume of testing (fixed costs) and costs that

vary in direct proportion with the volume (variable costs). A major economic effect of automation in the laboratory, generally, and of

multichannel analyzers, in particular, has been

to transform costs that had previously been

variable into fixed ones (4,35). The result is that

increasing automation becomes economically

more attractive at higher volumes of use.

Among the costs that are typically considered

to be fixed, that is, independent of the number

of samples analyzed, are equipment costs, insurance, maintenance, laboratory supervision,

and overhead (including administration, space,

utilities, etc. ). Variable costs, that is, those

roughly proportional to the number of samples

analyzed, include reagents, and consumables

and supplies. Automation reduces the variable

costs of reagents and supplies per test, while increasing the fixed costs of equipment, insurance,

and maintenance.

Direct labor, the most important economic

element, is not yet reflected in this typology of

costs. Labor costs for technician and technologist time do not fall neatly into either category.

Some aspects such as sample collection, coding

and preparation, and reporting of results are

more variable than fixed. Other aspects, especially sample processing and analysis, are more

nearly variable if tests are performed manually,

but more nearly fixed if automated equipment is

used. An additional fixed labor cost with automated equipment is training of technicians.

Thus, a major impact of automation has been to

transform many labor costs from variable to

fixed (35).

One common misperception is that automation reduces the need for highly skilled professional labor and thus reduces unit labor costs.

Commenting on this, Mather points out that the

training and skill required for adequate quality

control and supervision are no less with

sophisticated instruments than with manual

methods (28).

The validity of all cost estimates is, of necessity, compromised by market imperfections in

both the production process and the hospital.

As in virtually all benefit-cost and cost-effectiveness analyses, one must settle for proxies for

“true” costs, recognizing that biases may exist

and endeavoring to adjust for the most important and obvious of these,

The Cost Envelope

The problem of selecting the most efficient

technology for producing a specified number of

tests has been formulated by several authors

(4,11,35). If the assumption is made that determinations are undifferentiated and that the only

variable affecting cost is the number of determinations performed, the picture may be represented as in figure 1. This figure displays total

direct costs, as a function of test volume, for

three prototypical technologies: manual, firstgeneration automated, and advanced autoFigure I.—Cost Envelope for Three Alternative

Clinical Chemistry Technologies

Total

direct

cost

N, N2

Number of determinations
Case Study #4: Cost Effectiveness of Automated Multichannel Chemistry Analyzers ● 15

mated. The slope of each cost line represents the

variable cost per test; the intercept on the vertical axis represents the fixed cost. In reality, of

course, the fixed cost for manual determinations

is not zero, although it is much smaller than for

automated technologies. Note that the more advanced technologies exhibit greater fixed costs

but lower variable costs.

For any specified number of tests, the most efficient technology is the one with the lowest

total cost at that test volume. Thus, for test

volumes less than N1

, the manual method is

most efficient; for volumes between N1 and N2

,

first-generation automation is most efficient;

and for volumes above N 2

, the advanced

technology is most efficient. This is a wellknown result: Automated chemistry technologies are economically most advantageous at

higher test volumes. ’

The cost envelope in figure 1 is characterized

by decreasing incremental costs per test as the

test volume increases. This reflects the cost

characteristics of the underlying technologies.

The technologies with lower variable costs (but

higher fixed costs) become relatively more efficient as the number of tests increases.

Multichannel Efficiency Issues

The questions of economic efficiency become

more complex when one takes into account the

multichannel aspect of the technology. For one

thing, the simple distinction between fixed and

variable cost no longer suffices. There are some

costs that depend on the number of samples

tested, but not on which determinations are run

on each sample; and there are others that depend both on the number of samples and the

number and identity of determinations performed.

Although the distinction may be unimportant

for continuous-flow instruments once they have

been designed and purchased (since all tests on

the machine are run on every sample), it is very

important when equipment design decisions are

made. Specifically, two questions have important efficiency implications: 1) how to select the

“Data c(~ntlrmlng this p(~lnt are rev]ewed below In the part Ot

this case study on the econ[)rnlcs of the multichannel analyzer.

particular tests to include in the 12, 20, or 3 0

channels; and 2) how to ascertain the most efficient number of channels. At this point, we are

still assuming that the number and distribution

of tests ordered have been specified; hence, the

economic analysis depends on the test-ordering

pattern.

Optimal Selection of Channels.—With the

objective of minimizing costs, the selection of

channels in a multichannel analyzer ought to depend on the frequency with which determinations are requested, individually and in groups.

In the extreme case, if a particular test were

always ordered alone, it would probably not be

efficient to include it among the channels of a

multichannel continuous-flow analyzer. At the

other extreme, if certain profiles were always requested together, they would be more logical

choices for automation.

Taylor contends that the key economic consideration in deciding which tests to automate is

the number of aliquots of sample to be processed by the lab technician (44). He reports that,

prior to the introduction of an SMA 12/60 in his

hospital, the average number of aliquots required per specimen was 1.98. (Ideally, the

average would be 1.00, the number that would

obtain if all multiple-determination specimens

could be accommodated by the multichannel

analyzer. ) With the SMA 12/60, the optimal

combination of tests yielded an aliquot rate of

1.42 per specimen. The 20-channel SMAC, optimally configured, would reduce this rate to

1.21 per specimen, given the test-ordering pattern at this investigator’s hospital (44).

In principle, it might be desirable to exclude a

relatively common test from the multichannel

analyzer if it were usually ordered alone. In the

study cited above, for example, the optimal set

of 12 tests in the SMA 12/60 excluded calcium,

inorganic phosphorus, and cholesterol, even

though these tests are ordered more often than

some tests that were included in the efficient set.

Optimal Number of Channels.—Our review

of the literature revealed no studies evaluating

the most efficient number of channels to build

into a multichannel analyzer, given a particular

test-ordering pattern. Specifically, the merits of
16 ● Background Paper #2: Case Studies of Medical Technologies

6, 12, or 20 channels have not been evaluated.

Assuming any specified test-ordering pattern,

the question of whether to add a channel to the

design of a continuous-flow multichannel analyzer ought to depend on the following factors:

1.

2.

3.

4.

5.

the fixed cost of the new channel;

the variable cost of using existing channels

when the test on the new channel is ordered alone;

the variable cost of using the new channel

when only tests on existing channels are

requested;

the differences, for the new channel, between the variable cost per test on multichannel equipment and the cost per test on

dedicated equipment or by manual methods; and

the cost savings for aliquot preparation

for orders involving the new channel and

at least one existing channel.

Only if there exists a test for which the cost increases described by items 1 through 3 are outweighed by the cost reductions described by the

items 4 and 5 is it efficient to add a channel to

the design.

Further complicating the problem of evaluation is the fact that test-ordering patterns differ

from institution to institution. Hence, equipment-design decisions must be based on aggregates across the range of institutions that constitute the potential market for the instrument.

Of course, multichannel analyzers may induce changes in test-ordering behavior which

may alter the initial findings from analyses that

are conducted before the instrument is in place.

Under such circumstances, the question is not

merely one of economic

clinical cost effectiveness

ordered.

Cost Effectiveness

efficiency, but of the

of the additional tests

Overview

For the second, more complex question, concerning the cost effectiveness of alternative

technologies, we present a framework that can

be used to evaluate the cost effectiveness—in the

broader sense—of automated multichannel analyzers. This framework is based on more detailed presentations available elsewhere (11,50,

51,52) and on research in progress.

As stated earlier, cost analyses have shown

that the more advanced analyzers are cost effective at high test volumes, but not at lower ones

(35). This result can be seen in figure 1. As the

number of samples to be analyzed increases, the

incremental cost of additional tests falls. This

situation, known to economists as “diminishing

marginal cost, ” is juxtaposed with “diminishing

marginal benefit. ” That is, additional tests

ordered are decreasingly beneficial from the

clinical perspective (assuming that physicians

are already making the best use of the level of

testing they utilize). This juxtaposition necessitates explicit channel-by-channel and use-byuse analysis of individual chemistry tests to

evaluate ‘the overall cost effectiveness of the

automated multichannel analyzer.

The complexity of this situation is illustrated

in figure 2. Let us suppose that costs and health

benefits have been measured on the same scale.

This could be done by valuing each year of life

saved by the opportunity cost of saving a year

of life by alternative means (i. e., by some

specified cutoff value of a cost-effectiveness

ratio) (52). This assumption will be relaxed later

on. In the present formulation, however, the

difference between the height of the curve

measuring the total cost and the height of the

curve measuring total benefit is the variable of

interest—it represents the net benefit of testing.

The segments of the benefit curve represent

decreasingly beneficial uses of the test as the

number of available tests increases. The slopes

of these segments represent the expected value

per test. The segments of the cost curve (as in

figure 1) represent successively more advanced

technologies exhibiting diminishing incremental, but increasing fixed, costs.

By inspection of figure 2, we see that at the

point of switchover to the most advanced technology (where the number of tests is N2

), the

slope of the benefit curve becomes steeper than

that of the cost curve; incremental benefits exceed incremental costs. This does not necessarily mean that it is cost effective to use the ad-
Case Study #4: Cost Effectiveness of Automated Multichannel Chemistry Analyzers ● 17

Figure 2.—Hypothetical Analysis of the Cost= Effective Level of Testing

Total

cost

per

year

f

vanced technology to test beyond this level. To

take advantage of this low variable cost, we

must pay the high fixed cost, F. If the incremental benefit continues up to the level N+ , and

then flattens out as the beneficial uses of the test

are exhausted (solid line in the figure), then

society is actually better off testing at level N*

with the intermediate technology than at N+

with the advanced technology. At N*, the net

benefit is maximized. If, however, the incremental benefit of testing continues all the way up to

N

+ + (dotted line in the figure), then it would

be best to use the advanced equipment for this

larger number of tests.

The above remarks relate only to evaluation

from a societal perspective, in which the costs of

health care resources are considered. From the

perspective of patients, considered here not as

payers for their own health care, it would be

best to test at the highest possible level at which

benefits accrue to the patient. This would occur

at N+ if the solid benefit curve were to obtain,

a result that would be at odds with the societal

interest (1 1).

Total

benefit

per

year

Thus, the cost effectiveness of multichannel

technology depends on the slope of the benefit

curve as the number of tests increases. This, in

practice, depends on the value of such tests in

screening patients who are asymptomatic for

the conditions being tested. It is not clear

whether the most advanced multichannel analyzers can pay for themselves, in efficiency

terms, without increasing the use of the chemistry laboratory for screening purposes. If they

cannot, then the evaluation must turn to the

issue of clinical efficacy of such uses.

Levels of Evaluation of Clinical Efficacy

and Cost Effectiveness

The cost effectiveness of multichannel analyzers can be considered at each of four levels:

1) technical quality of measurement; 2) diagnostic value of test results; 3) clinical efficacy, in

terms of expected impact on-treatment decisions

and patient outcomes; and 4) overall cost effectiveness.

Technical Quality of Measurement.—The

technical quality of measurement in automated
-——

18 . Background Paper #2: Case Studies of Medical Technologies

analyzers may be described in terms of precision

(absence of test/retest variability) and accuracy

(absence of bias or systematic departure from

the true value). The precision and accuracy of

the major automated analyzers are generally regarded as quite good, largely because such analyzers eliminate human error in measurement

(5,28,54). For some tests, however, although

within-laboratory reliability is excellent, between-laboratory variations may be important.

Between-laboratory variations have been noted

especially in relation to cholesterol and SGOT

determinations (12).

Furthermore, there is a tradeoff between accuracy and speed because of the problem of contamination of results from one specimen to the

next. This tradeoff also arises when the ratelimiting reaction among, say, 17 or 20 tests is

accelerated beyond optimal rates to keep step

with the others. In weighing this tradeoff from a

cost-effectiveness point of view, the costs of

repeat measurements induced by lack of accuracy must be considered.

Finally, although minimizing human error,

machines suffer from fatigue and are prone to

systematic failures that must be monitored

closely by liberal interspersing of controls in

each batch of tests (5,28). The costs of these controls must also be considered and compared to

those for manual testing, because they compromise both the unit cost reduction attributable to

automation and the effective rate of testing (28).

Diagnostic Value.—There are many possible

measures of the diagnostic value of a test (31).

These include its predictive value positive and

predictive value negative (49). The former is the

proportion of positive test results that truly correspond to disease or abnormality; the latter is

the proportion of negative test results that truly

correspond to the absence of disease.

The diagnostic value of a test can only be

measured in the context of how the test is used.

It is well known that the predictive value

positive of a test depends not only on the properties of the test (i.e., its sensitivity and

specificity y), 7but also on the prevalence of the

“rt,~t sen~ltlvlty I\ the pr(lp{~rtlt~n (d p(w]tlve tests amtmg dlseawd patwnts: test speclticlty IS the pr(}p(~rtlt~n (~t negative tests

a m ~Jng ntlnd]wased pat Ien t$.

disease in the tested population. The lower the

prevalence, the lower the predictive value of a

positive result. As the number of tests run on a

multichannel analyzer increases, most determinations will be performed despite the absence of

any particular suspicion of abnormality; hence,

the prevalence in the tested population of the

conditions being tested for will be very low. It

follows that the probability that any single

positive result will be a true positive is very low.

One example cited in the literature relates to

the use of the serum calcium value to screen for

parathyroid cancer (12). Suppose that the prevalence of this tumor in the screened population

were 1 per 1,000. Suppose also that the probability that a patient with no parathyroid abnormality had a 5-percent chance of having an

elevated serum calcium level, according to the

usual definition of normal limits. Then, the

probability that a patient who tests positive will

actually have

as follows:

+ + high ]

a parathyroid tumor is calculated

=

( 1 )(0.001) .

= 0.02

Forty-nine of 50 patients who screen positive

would have no tumor. Many of these would

eventually be ruled out on the basis of repeated

blood and urine tests. Some might be subjected

to biopsies that prove to be negative.

As the number of tests increases, the number

of false positives also increases. If 20 independent tests, each with a false-positive rate of only 5

percent, are run on a normal patient, odds are

nearly two-to-one (64 percent) in favor of observing at least one positive result. That positive

result must then be verified and possibly followed up at some cost. The induced costs that

result from positive tests generally—and false

positives in particular—must be considered

carefully in evaluating the overall economic impact of multichannel analyzers.

It is not clear what the physician’s attitude

should be with respect to unexpected positive results (25). If the result is so unexpected that the
Case Study #4: Cost Effectiveness of Automated Multichannel Chemistry Analyzers ● 19

physician considers an option to be simply to ignore it, then perhaps the test should not have

been ordered. If, on the other hand, the test is

available at virtually zero incremental cost on a

multichannel profile, then should the physician

discard the information? This issue, the appropriate inference to be drawn from test results in

the context of a multiple-test panel, is unresolved both in theoretical and practical terms.

Clinical Efficacy.—The clinical efficacy of the

multichannel analyzer also depends on the pattern of its utilization. One might differentiate

between its clinical efficacy in optimal usage

(defined, perhaps, by cost-effectiveness criteria)

and that for actual usage (i. e., its effectiveness).

Evidence for the former requires analysis that

because of both data limitations and methodological obstacles has not yet been undertaken

(50). There is, however, some fragmentary evidence concerning the latter.

One measure of effectiveness, albeit imperfect, is the degree to which test results contribute

to altering or confirming an existing treatment

plan. Dixon and Laszlo (8) reported that only 5

percent of automated chemistry tests ordered by

house officers in their study were used in this

way. These investigators found that, on average, only 3 of the 12 chemical values provided

by the analyzer were actually wanted by the

house officers. After an intervention in which

the physicians were restricted in the number of

tests they could order, the percentage of test

results that contributed to altering or confirming the course of treatment increased from 5 to

23 percent.

A study by Durbridge, et al. (9) corroborated

these findings. They found that the effects of introducing hospital admission screening with a

multichannel analyzer were to increase the number of tests during the patients’ hospitalization

by 78 percent (not including the screening profile itself), to increase consultations by 25 percent, and to increase laboratory costs by 64 percent. These effects had no measurable health

benefit to any patient in the study.

Other studies have found multichannel chemistry screening to have appreciable value, however. Carmalt, et al. (7), in evaluating a 14-test

screen on hospital admission, reported that new

or additional diagnoses were found in 16.9 percent of patients. An additional 21.6 percent had

“unexplained abnormal” results, such as hypercholesterolemia or hyperuricemia. Of all patients tested, 1.4 percent were found to have

deteriorating or newly discovered renal disease,

a finding that led to treatment in four patients.

Belliveau, et al. (3) found clinical value in an

18-channel chemical admission screen in a community hospital. In 7.4 percent of patients

screened, test results indicated or “possibly” indicated a new diagnosis. These included: 21

cases of diabetes mellitus, 9 of gout, 6 of cirrhosis, 5 of hypercholesterolemia, and 1 of

myeloma. Tests most often found to be abnormal were uric acid (15.1 percent), SGOT (13.5

percent), and LDH (12.3 percent). In all, nearly

half (43.2 percent) of patients had at least one

abnormal result, leading in most cases to retesting and further diagnostic workups.

Thus, the evidence as to the effect of chemistry screening on treatment decisions is mixed.

There are undoubtedly some treatable conditions discovered as a result of such screening,

but the value of early intervention in those conditions must be examined. Clearly, better measures of clinical effectiveness of tests and their

uses are needed, as are studies of the benefits of

individual tests in the full range of their clinical

uses. The methods of decision analysis may be

useful in carrying out such evaluations, using

expected improvement in patient outcome as the

ultimate measure of effectiveness (19,51).

Cost Effectiveness. —Weinstein and Fineberg

(51) describe an analytical approach for evaluating the cost effectiveness of an individual chemistry test in one of its uses. The methodological

problems of cost-effectiveness analysis increase

dramatically, however, when one moves from

an individual test in a single use (e. g., VMA

in the diagnosis of pheochromocytoma in an

asymptomatic hypertensive patient) to an instrument that performs multiple tests, each of

which may have several clinical uses.

The problem is complicated further because it

would be inappropriate, in analyzing the cost

effectiveness of state-of-the-art multichannel in-
20 . Background Paper #.?: Case Studies of Medical Technologies

struments, to compare them with fully manual

testing. It is, however, appropriate to ask:

1) whether it is cost effective for a particular test

to occupy a channel in any such instrument, and

2) under what circumstances the reduced variable cost of an instrument justifies its increased

fixed cost relative to alternative equipment.

The cost of any diagnostic technology largely

depends on the costs induced by test results.

These include the costs of repeat tests required

to compensate for measurement error or to confirm presumptive diagnoses. Running more tests

generates more positive results, leading to greater induced costs to verify these findings (9).

These induced costs must be balanced against

the decreasing cost per test in laboratories that

use multichannel analyzers at high volumes.

The issue of induced costs must also be considered in evaluating the relative cost effectiveness

of continuous-flow (unselective) and discretesample (selective) analyzers. Technicon’s SMA

11 and SMAC systems address this concern by

automating a function that many hospital laboratories have provided all along: selective

reporting of only those tests requested by the

clinician.

One of the authors of this study (Weinstein),

under a grant from the National Center for

Health Services Research, is now developing an

analytical model for cost-effectiveness analysis

(CEA) of the multichannel analyzer. The model

addresses two questions: 1) What is the cost effectiveness of a test in each of its clinical uses?

and 2) Given the answer to that question, what

is the cost effectiveness of automating a particular test? The model is used here to analyze

the marginal value of a single test. Methods to

evaluate the joint cost effectiveness of many

tests remain to be developed.

The analysis proceeds in several steps. The

first is to estimate the cost envelope for the test,

i.e., the cost of testing as a function of the

number of tests ordered per unit time, assuming

the most efficient testing technology at each

level (this was shown in figure 1). The next step

is to specify the clinical indications for the test,

including the possibility of using it for screening. Estimates of the frequencies with which

these indications arise must also be obtained.

Next, for each clinical indication or use, the

expected value of clinical information (EVCI) is

estimated using a decision-analytic framework.

The EVCI is a measure of the average amount of

health benefit (e.g., quality-adjusted life years

saved (QALYs) (52)) per test. Also, for each

clinical use, the expected induced costs per test

are estimated. Methods for performing both of

these analyses have been demonstrated (51).

Assuming that the first indication occurs in

N, patients per year in a particular setting, that

the second indication occurs in Nz patients per

year, and so forth, the aggregate expected benefit and aggregate expected induced cost can be

plotted as a function of the number of tests for

each use (see figure 3). Each curve is actually a

line segment whose slope is the benefit or induced cost per test, and which extends from the

origin to the maximum possible number of tests

(N,, Nz, etc.). For screening, the corresponding

segment may be replaced by an open-ended ray,

assuming that there is no limit to the number of

potential candidates for screening. Alternatively, the limit for a hospital may be the total numFigure 3.—Analysis of Cost Effectiveness for

a Test With Multiple Uses

Benefits as a function of number of tests, by use

B, B2

Use 1 Use 2 Use 3

Induced costs as a function of number of tests, by use

c1

Expected

Induced

cost
Case Study #4: Cost Effectiveness of Automated Multichannel Chemistry Analyzers ● 21

ber of hospitalizecl patients, less the number

with some specified indication.

The next step in the analysis is to construct a

net benefit curve for each possible cost-effectiveness cutoff level. This cutoff level, k, might

range from $1,000 per QALY to arbitrarily high

values of cost per unit of health benefit. For each

value of A, and for each clinical use, the net

benefit per test is calculated as:

Bi is the expected benefit per test (in life years or

QALYs), Ci is the expected induced cost per

test, and A is the cost per unit of benefit. A net

benefit curve, analogous to that in figure 2, is

then constructed by concatenating line segments

with slopes B~et(X) and with horizontal spans Ni,

in decreasing order of their slopes. This is

shown in figure 4. For each cost-effectiveness

cutoff value, A, the optimal level of testing,

N*(X), can be found as the point of maximum

vertical distance between the net benefit curve

and the testing cost curve.

Figure 4.—Composite Analysis of the

Cost-Effective Level of Testing

N“ (A) N, Optimal Number

(global (level at level if of tests, N

optimum) which auto- automated

mation becomes

efficient)

aThe variable A IS the cost-effectiveness cutoff level ($/OALY)

For the hypothetical illustration in figure 4,

automation is not cost effective at this particular

cutoff level. For sufficiently high values of A,

reflecting the ability to pay more per unit of

benefit despite scarce resources, the cost-effective level of testing will shift to the right and

into the domain in which automation is efficient.

Concluding Observations Based on

the Conceptual Framework

The foregoing conceptual framework for

evaluating the efficiency and cost effectiveness

of the multichannel automated analyzer suggests that the cost effectiveness of automation

cannot be assessed independently of the level of

resources in the health care system, nor of the

price society is able and/or willing to pay for

the diminishing incremental benefits to be derived from marginal indications for testing. In

order to reach volumes at which multichannel

instrumentation is efficient, for example, we

must be willing and able to pay for relatively

low-yield uses of tests. If resources were truly

scarce, however, we would be able to afford

only the most essential uses of tests—and these

would be most efficiently performed by manual

methods. In summary, the question may be

whether or not we, as a society, really wish to

pay enough for diminishing incremental health

benefits to justify sufficient levels of testing to

justify, in turn, the reduction in unit testing

costs that the multichannel analyzers offer.

The empirical question, yet to be analyzed, is

at what point on the spectrum of clinical uses

for each test the multichannel analyzer becomes

efficient. Is it necessary to adopt hospital admission screening or well-patient ambulatory

screening to render the multichannel analyzer

efficient? Or does the level of testing necessary

to render the multichannel analyzers efficient

(i.e., N2 in figure 1 ) occur well before we exhaust the generally accepted and cost-effective

indications for testing? In the answer to this

question may lie the answer to the overall question as to the cost effectiveness of multichannel

dn.] 1 vz~’f% In 01] r h~’.j I t h cdr~) Svstem.
—

22 ● Background Paper #2: Case Studies of Medical Technologies

ECONOMICS OF THE MULTICHANNEL ANALYZER

Overview

In this part of the case study, we review the

available data concerning the costs of performing chemistry tests on multichannel equipment.

The data reviewed are intended to answer the

question of economic efficiency: What is the

cost of producing test results according to a

specified pattern?

In the first section, we review data pertaining

to each component of chemistry laboratory cost

that may be influenced by the choice of equipment. These cost components, reviewed separately below, are as follows: 1) direct nonlabor

costs (equipment, service and maintenance, reagents and consumables); 2) direct labor costs;

and 3) indirect costs.

Data on direct nonlabor costs were obtained

from manufacturers as the prices charged to

institutional purchasers (10,24,46). For purposes of this review, we concentrate on the following products: Technicon’s SMAC and SMA

12/60, Hycel’s Super-Seventeen, and Du Pont’s

ACA II.

Data on direct labor costs are the most difficult to obtain, because estimates of these costs

require estimates of worker productivity in laboratories with various configurations of equipment. Later in this case study, we will refer to

two published studies in an effort to derive approximate estimates of the magnitude of labor

costs and their sensitivity to the availability of

automated equipment. Unpublished data on

labor costs obtained by the College of American

Pathologists were not available to us in performing these analyses, but could be of great value in

cost-effectiveness analyses (CEAs) to be performed in the future.

No published studies suggest that indirect

costs are affected by the types of equipment

used. In the review below, therefore, we discuss

this component of cost only briefly.

In the section following the review of cost

data, we suggest analyses to address the following two efficiency questions, applied to currently available multichannel equipment. First,

what is the relation between the unit cost per

determination and test volume, assuming a

fixed number of determinations per sample?

(These cost functions may be compared to unit

costs with dedicated equipment or manual

methods to determine breakeven test volumes. )

Second, what is the relation between the unit

cost per determination and the number of determinations per sample, for various types of analyzers? The published data on labor costs are

too fragmentary to allow us to resolve these

questions, but we do suggest an analytic

framework for their resolution.

In the third section below, we discuss the cost

implications of R&D policies in this area and of

the rate of product turnover. Then we restate

the problem of induced costs and repeat tests

and give some indication of the magnitude of

their economic impact. Finally, in the last section, we make some observations on the

economic incentives faced by hospital laboratories, given the current regulatory and reimbursement environment.

Review of Cost Data

Direct Nonlabor Costs

Equipment.—Equipment prices are shown in

table 1. The prices for the instruments to be reviewed here are as follows:

Technicon SMAC . . . . . . . . . . . . . . . . $271,000”

Technicon SMA 12/60 . . . . . . . . . . . . . 99,500

Hycel Super-Seventeen . . . . . . . . . . . . . 75,000

Du Pont ACA II . . . . . . . . . . . . . . . . . . 69,0009

These are list prices, obtained directly from the

manufacturers (10,24,46).

The annual cost of owning a machine depends

on its useful life and the interest rate. Assuming

a 7-year amortization period (22) and a real interest rate of 5 percent (after inflation), the

annual equipment costs, in constant 1979 dollars, are:

Technicon SMAC ... ... ... ... ... .. $45,000

‘1’lus an addit]t~nal $4, OOO it a test c(~nti~urati(~n other than one

(}t the 14 standard c~~ntl~ura t l[~ns IS ch(wen.

“Thlrt y-channel m(del.
Technicon SMA 12/60 . . . . . . . . . . . . . 16,500

Hycel Super-Seventeen . , . . . . . , . . . . . 12,400

Du Pont ACA II . . . . . . . . . . . . . . . . . . 11,400

Service and Maintenance.—Costs for equipment service and maintenance (including parts

and labor) can be estimated from experiences of

laboratories that perform their own maintenance, or from prices charged by manufacturers

for full-service plans. One study reported annual maintenance costs in a hospital laboratory to

be $6,100 in 1971, or slightly more than $10,000

in 1979 dollars (35). Others estimate that labor

costs for maintenance amount to approximately

20 percent of direct labor costs (32).

Although the prices charged for full-service

plans may overestimate true costs, many labor-a tories buy these plans. Technicon offers a

plan that includes preventive maintenance,

three emergency service calls per year, parts,

and unlimited consultation. The annual prices

are $25,000 for the SMAC and $7,900 for the

SMA 12/60, or slightly less than 10 percent of

their purchase prices.

Hycel’s plan for the Super-Seventeen includes

service and maintenance, plus all reagents, supplies, control samples, and other items. The

price is based on test usage. For individual tests,

it averages between 12 and 22 cents per determination, depending (in block-rate fashion) on

the volume of usage and actual tests performed.

For complete profiles, it averages between 4 and

17 cents per test, also depending on the volume

of usage. Charges are determined by reading

meters built into the reach inc.

Du Pont has two service plans for the ACA.

One is a rental plan; the user does not buy the

machine, but leases it from Du Pont. The fixed

price of $1.13 per determination includes use of

the machine, maintenance, unlimited emergency visits, parts, consultation, reagents, nonreagent consumables such as control and calibration products, and a training course for three

technologists. Alternatively, customers who

own their equipment may purchase a service

contract for $6,200 per year. This covers parts,

labor, unlimited emergency visits, 24-hour telephone consultation, replacement of mechanical

and electrical parts as they are updated, and

replacement of reagents lost as the result of instrument malfunction.

Reagents and Consumables.—For Technicon’s equipment, the reagent prices per sample

are shown in table 3.10 Note that for these continuous-flow analyzers, all reagents (20 for

SMAC and 12 for SMA 12/60) are consumed on

each sample. Total reagent and consumable cost

per sample for the SMAC is approximately

$1.67; for the SMA 12/60, it is $1.26. Note the

variation among reagent prices; the prices range

from 36.8 cents per sample for triglycerides on

“’Under Technlct~n’s block-rate pric]n~, these prices c(~rresp~~nd

t~~ a user vvlth a vc~]ume of 100 samples per day ( 26,000 per year-).

Unit reagent prices WIJUICI be somewhat Iesi t t~r higher v(~lumes,

Table 3.—Reagent and Consumable Prices for

Two Technicon Analyzers

Cost per sample

Item SMAC SMA 12/60

Reagents

Albumin. . . . . . . . . . . . . . . . . . . . . . . . . $0.011

Alkaline phosphatase . . . . . . . . . . . . . 0.036

Calcium. . . . . . . . . . . . . . . . . . . . . . . . . 0.005

Carbon dioxide. . . . . . . . . . . . . . . . . . . 0.021

Chloride. . . . . . . . . . . . . . . . . . . . . . . . . 0.004

Cholesterol. . . . . . . . . . . . . . . . . . . . . . 0.022

CPK . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.266

Creatinine. . . . . . . . . . . . . . . . . . . . . . . 0.003

Direct bilirubin . . . . . . . . . . . . . . . . . . . 0.026

Glucose (oxidase). . . . . . . . . . . . . . . . . 0.070

Inorganic phosphorus. . . . . . . . . . . . . 0.004

Iron . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.009

LDH . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.053

Potassium. . . . . . . . . . . . . . . . . . . . . . . 0.015

SGOT. . . . . . . . . . . . . . . . . . . . . . . . . . . 0.031

SGPT . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.040

Sodium . . . . . . . . . . . . . . . . . . . . . . . . . 0.017

Total bilirubin . . . . . . . . . . . . . . . . . . . . 0.020

Total protein . . . . . . . . . . . . . . . . . . . . . 0.005

Triglycerides. . . . . . . . . . . . . . . . . . . . . 0.254

BUN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.004

Uric acid . . . . . . . . . . . . . . . . . . . . . . . . 0.024

Consumables

Calibrators and controls . . . . . . . . . . . 0.332

Other consumables . . . . . . . . . . . . . . . 0.430

Total reagent cost per sample . . . . . . $0.91a

Total reagent and consumable cost

per samplea

. . . . . . . . . . . . . . . . . . . . $1.67

Total reagent and consumable cost

per determination. . . . . . . . . . . . . ~. $0.08

$0.022

0.086

0.009

0.011

0.011

0.067

0.322

0.004

0.023

0.106

0.013

0.016

0.147

0.009

0.041

0.235

0.009

0.022

0.009

0.368

0.005

0.051

0.184

0.178

$0.90 b

$1.26

$0.10

aASSurneS SMAC contains all tests except iron and direct billrubin.

bAssumes SMA IZ60 Contains the following: albumin, alkaline Phosphatase,

calclum, CPK, creatlnlne, glucose, inorganic phosphorus, LDH, SGOT, total

bllirubln, total protein, BUN.

SOURCE: Personal communications and marketing materials from Technicon

(46).
24 . Background Paper #2: Case Studies of Medical Technologies

the SMA 12/60 to 0.3 cents per sample for creatinine on the SMAC. Consumables account for

roughly one-half of variable costs per sample in

the SMAC and for about one-third of variable

costs per sample in the SMA 12/60. Reagent

prices are lower for the SMAC than for the

SMA 12/60, in part because of the smaller reagent volumes required for the SMAC.

Hycel’s reagents and consumables are included in the price per sample under their maintenance plan for the Super-Seventeen, described

above. Alternatively, they can be purchased

separately at prices ranging from 10 cents per

test for inorganic phosphorus to 23 cents per test

for alkaline phosphatase, except for triglycerides, which are priced at 83 cents per test. Note

that the Hycel analyzer, unlike Technicon’s

continuous-flow analyzers, consumes only the

reagents for the tests requested.

Reagents and consumables for the Du Pont

ACA are available on an individual basis for

those who own the analyzer, but the “full-service” price of $1.13 per test includes reagents and

consumables as well as equipment use and service.

Summary of Direct Nonlabor Costs.—On the

basis of the data presented so far, the fixed and

variable components of direct nonlabor cost can

be calculated for a selection of instruments,

service and use plans, and test configurations.

Examples are shown in table 4.

Direct Labor Costs

Economic analyses of automated chemistry

tests have consistently found that direct labor

costs account for 40 to 60 percent of the total

direct costs (30,32,35). McLaughlin, in a 1971

study at a hospital with a 12-channel analyzer,

found the direct cost per determination to be 28

cents, or 51 percent of total direct costs (30).

This would be 46 cents at 1979 price levels.

Pegels, in a study based on 1971 data, found

average direct labor costs of 21 cents per test, or

62 percent of total direct costs for operating a

20-channel analyzer (35).

The effect of increased analyzer speed on

overall labor costs has not been studied in recent

years. However, McLaughlin’s study suggests

Table 4.—Direct Nonlabor Costs for

Selected Analyzers

Annual Variable cost

Analyzer fixed cost Per sample

Technicon SMACa with

service plan. ... ... ... ... .. .$70,000

Technicon SMA 12/60a with

service plan. . ...............24,400

Hycel Super-17 with service plan,

full profiles only

< 12,000 samples/year. . ......12,400

12,001-24,000 samples/year. .. .27,400

24,001-36,000 samples/year. .. .44,200

>36,000 samples/year. . ......55,000

Hycel Super-1 7 with service plan,

individual testsb

< 12,000 tests/year. . .........12,400

12,001-120,000 tests/year. . ....12,760

120,001-228,000 tests/year. . ...17,560

>228,000 tests/year. . ........24,400

Du Pont ACA II with rental plan. . . 0

$1.67

1.26

3.00

1.75

1.05

0.75

0.22 x NC

0.19 x N

0.15 x N

0.12 x N

1.13 x N

aA5sumes test configurations given in legend to table 3.

bApproximation of Hycel’s block-rate Pricin9.

CN = average rlurnber of determinations per SamPle.

SOURCE: Personal communications and marketing materials from the manufacturers (10,24,46).

that labor accounted for approximately 50 percent of direct costs both in hospitals with and

without multichannel analyzers (30). The important effect of automation, however, may

shift to certain labor costs from variable to fixed

(4,35). Some labor costs (e.g., for specimen collection and coding) remain variable, irrespective

of the method of analysis, and are relatively

unaffected in magnitude (4,28). Others (e. g.,

operation) become fixed in the short run. The

costs of some fixed components of labor (e. g.,

supervision and training) may be expected to increase (28,30).

There are insufficient recent data from institutions with automated analyzers on which to

base quantitative estimates of the effect of

multichannel technology on labor costs. Qualitatively, for analyses such as those presented

immediately below, it may be reasonable to

assume that: 1) the variable component of labor

is proportional to the number of samples tested

(not determinations), and 2) the fixed and

variable components are roughly in the same

proportion to each other as are the fixed and

variable components of nonlabor direct costs,

The second assumption reflects the observation

that supervision and training requirements tend

to increase as a function of the complexity of in-
Case Study #4: Cost Effectiveness of Automated Multichannel Chemistry Analyzers ● 25

strumentation (and, hence, fixed capital and

maintenance costs). Studies of laboratory productivity are needed to validate these assumptions and to provide more reliable quantitative

estimates.

Indirect Costs

No recently published data are available on

indirect costs of clinical chemistry laboratories.

Moran suggests a rule of thumb that indirect

costs (space, administration, utilities, etc. ) may

add 25 percent to direct costs (32). In any event,

it is unlikely that multichannel analyzers have

had much impact on indirect costs, compared to

other automated analyzers or even manual

methods.

Hypothetical Analyses of Efficiency

The available data on labor costs are inadequate for performing realistic analyses, but it is

possible to suggest the kinds of analyses that can

be used to evaluate the need for automated

multichannel equipment as a function of the

number of tests required per serum sample,

For purposes of these hypothetical analyses,

we assume that the fixed and variable components of labor are each equal to the fixed and

variable components of nonlabor direct costs

for each equipment configuration. This assumption is consistent with the finding that labor

costs consistently account for about half of total

costs. The variable component of labor is

assumed to be proportional to the number of

sample aliquots that the technologist must

prepare.

The following analysis is based on the cost

data for the SMA 12/60. The question is: How

large an annual demand for tests (determinations) is required to justify purchase of an SMA

12/60? Unlike some previous analysts who have

addressed this question in the past, we adopt the

societal interest in minimizing true cost, not the

laboratory director’s interest in maximizing net

revenues.

Suppose that the average number of determinations required per sample is two. (This is

consistent with the data of Taylor, cited above

(44). ) The direct fixed costs for the analyzer are

assumed to be $24,400 for nonlabor items (see

table 4), plus $24,400 for the fixed component of

labor. The direct variable costs are $1.26 p e r

sample, or $0.63 per determination, plus $0.63

per determination for labor. Hence, total cost

(TC) is given by the expression:

where N is the number of determinations. Average cost per determination is equal to:

As shown in figure 5, the average cost declines

as the number of determinations increases.

Suppose that the next best alternative method

has a unit cost of $1.50 per determination. (This

figure is consistent with data from Pegels, adjusted for inflation (35). ) Then, as shown in

figure 5, the analyzer is efficient, provided that

at least 200,000 tests on 100,000 samples are

performed per year.

If, instead of two determinations per sample,

four per sample were requested, then the breakeven volume would be only 56,100 determinations on only 14,000 samples. Thus, we conFigure 5.—Hypothetical “Breakeven” Analysis for a

Multichannel Analyzer

Average

cost per

determination

$3.00

$2.OCI

$1.50

$1.26

$1.00

$0.63

56,100 100,000 200,000 300,000

Number of determinations per year
26 . Background Paper #2: Case Studies of Medical Technologies

elude the efficiency of multichannel analyzers at

lower test volumes is very sensitive to the

number of determinations performed per sample. Their cost effectiveness, therefore, depends

critically on the benefits to be derived from

those additional determinations.

Fixed Costs of Production and

the Rate of Innovation

From societal perspective, the price of equipment to the purchaser overstates the actual incremental cost of production. It includes a

significant component that reflects the fixed,

hidden costs of product research, development,

and marketing (22). For example, development

costs for Technicon’s SMAC alone were $7

million (34). Allocated among 1,000 machines

sold, on average, 5 years later, and assuming an

annual interest rate (not inflation-adjusted) of

10 percent, these costs alone would account for

$11,400 in the $271,000 purchase price of

SMAC. Furthermore, as Holy points out, product development is but one of many costs in the

process from product conception to marketing

(22). Patent protection costs perhaps $100,000

or more for a major product and several thousand dollars for each patented component; engineering documentation for production may cost

$250,000. Perhaps the largest hidden costs are

those that a company must absorb for research

and development of ideas that never reach production (22).

From society’s viewpoint, it maybe appropriate to factor these elements into the cost of a

technology. But the question must be asked: Are

the resources devoted to innovation in multichannel analyzer technology appropriately

spent? Do the reductions in variable costs justify

the fixed costs that finance the rapid turnover in

cost-saving innovations? These issues deserve

study, and they apply to the cost-effectiveness

evaluation of technologies ranging far beyond

the realm of clinical chemistry analyzers.

Induced Costs

Given the ability of multichannel technologies to produce additional determinations at

very low incremental cost, perhaps their induced costs make their greatest economic impact. These induced costs include repeat tests,

additional laboratory tests required to rule out

or confirm a diagnosis, radiographic or other

diagnostic tests, and treatments.

Obviously, the magnitude of induced costs

depends on the particular tests ordered, the

clinical procedures followed, and the population tested. In one study of hospital admission

screening, use of a multichannel analyzer led to

a 78-percent increase in the number of other

tests performed, a 64-percent increase in other

laboratory costs, and a 25-percent increase in

the number of consultations (9). Explicit decision analyses have indicated that induced costs

can easily account for half of all costs attributable to laboratory tests, even in populations in which the proportion of patients with a

condition requiring intervention is small (51).

The high rate of false positives in multichannel test panels can amplify this effect (12). Thus,

even if the cost per determination can be reduced, one must consider the effect of multichannel technology on total costs, and ask

whether these increases in “rule-out” costs are

justified by the benefits. Clearly, further studies

are needed in which the magnitude of induced

costs attributable to particular chemistry tests is

estimated.

Financial Incentives

It would be inappropriate to conclude a discussion of the economics of the multichannel

chemistry analyzer without some discussion of

the financial incentives faced by laboratory

directors and other users of the technology.

Typical third-party reimbursement rates for

chemistry tests clearly exceed not only marginal

costs but also average costs for most laboratories. If the sole concern of the laboratory

director were to maximize net revenues, the obvious incentive would be to increase the number

of tests, at low incremental cost but high incremental revenue (32).

Whether this incentive motivates laboratory

directors in practice has not been shown, but

that it does motivate them certainly seems

plausible and is generally accepted as fact. On

an aggregate level, society seems willing to per-
Case Study #4: Cost Effectiveness of Automated Multichannel Chemistry Analyzers ● 27

mit hospitals to use laboratory revenues to subsidize other meritorious, but low-income, services. The result, of course, has been to make

multichannel analyzers even more attractive,

because their costs per test decrease with increasing test volumes.

Other subtler financial and organizational incentives may also contribute to the adoption of

multichannel analyzers. Mather points out that

many hospital laboratories with only moderate

volumes may purchase advanced multichannel

equipment to shift costs from labor to capital,

thus relieving pressure from the overburdened

labor supply (28). This inefficiency can be rationalized to the hospital administration if the

labor supply is relatively fixed and if added

revenues can more than pay for the loss of efficiency.

The countervailing effects of government

regulation of capital expenditures through

certificate-of-need programs have been negligible. No instance of denial for multichannel

analyzer purchase was found in the States

surveyed by the authors. (At current price

levels, only Technicon’s SMAC and the new

Hycel-M would be covered by such regulations

in any case. ) Other regulatory policies, including denial of reimbursement for certain uses

of the laboratory (e.g. such as hospital admission profiles) may ultimately alter the financial

incentives to acquire multichannel chemistry

analyzers, but such measures are not likely to

have much effect for several years.

COST EFFECTIVENESS OF CARDIAC ENZYMES IN DIAGNOSIS OF

MYOCARDIAL INFARCTION: A STRUCTURED REVIEW

Overview

The analytical framework for cost-effectiveness analysis (CEA) of multichannel chemistry

analyzers presented earlier strongly suggests the

need for cost-effectiveness evaluations of the individual chemistry tests that constitute test

panels. Such evaluations should consider not

only the quality of measurement and diagnostic

efficacy of such tests, but also the expected

clinical value of the information obtained, the

costs induced or averted as a result of the tests,

and the costs of producing various quantities of

test results on different chemistry analyzers. For

a full evaluation of the cost effectiveness of

automating a particular test, according to the

methodology presented earlier, each clinical indication for its use must be considered separately before proceeding to an overall evaluation of the test.

In this part of the case study, we present a

structured review of the information required to

conduct such CEAs for three chemistry tests that

are commonly used to diagnose myocardial infarction (heart attack). These tests are the cardiac enzymes: 1) creatine phosphokinase (CPK),

2) lactic dehydrogenase (LDH), 3) and aspartate

aminotransferase, also known as serum glutamic oxaloacetic transferaese (SGOT). They

are all available on the multichannel analyzers

reviewed in this study. We also review information pertaining to the use of certain isoenzymes

of these compounds. Some of these isoenzymes

have been widely acclaimed for their diagnostic

value in myocardial infarction, and some are

being considered for use in improved multichannel analyzers. One (CPK-MB) is now available

on a limited basis in Du Pont’s ACA.

It should be emphasized that our review of the

cardiac enzymes and isoenzymes pertains only

to a single clinical use of these tests, namely,

diagnosis (i.e., “rule-in/rule-out”) of myocardial infarction in patients with symptoms suggesting it. A complete evaluation of these tests

would consider other uses as well, including, for

example, the use of LDH and SGOT to diagnose

liver disease. (Tests for CPK, however, are

probably performed almost exclusively in the

diagnosis of myocardial infarction or screening

for cardiac damage. )

The clinical properties of these enzymes and
28 ● Background Paper #2: Case Studies of Medical Technologies

isoenzymes are briefly reviewed in the first section below. In the next section, we structure a

clinical decision tree for the typical “rulein/rule-out” decision problem. We then proceed

in the subsequent sections to evaluate the

available evidence at each of the four levels of

analysis described in the part of this case study

on the framework for cost-effectiveness evaluation: 1) technical quality of measurement, 2)

diagnostic value, 3) clinical efficacy, and 4) cost

effectiveness. Our review concludes with a brief

discussion of efficiency.

Clinical Properties of the

Cardiac Enzymes11

Elevation of the serum level of either LDH,

SGOT, or CPK suggests tissue damage, but not

necessarily damage to the muscles in the heart

wall (myocardium). Cardiac damage usually,

but not always, causes all three enzymes to rise.

LDH, however, may also be elevated in the

presence of liver disease (such as hepatitis),

pulmonary infarction, renal disease, muscular

damage, shock, leukemias and lymphomas, and

other cancers. SGOT may be elevated due to

liver disease, skeletal muscle damage, brain

damage, kidney disease, or shock. CPK may be

elevated due to damage to skeletal muscles or

the brain; it rises easily in the presence of even

mild skeletal trauma, such as that following

physical exercise. All three may be elevated due

to congestive heart failure or trauma of thoracic

surgery.

All three enzyme tests are typically ordered in

the workup for myocardial infarction, either in

the coronary care unit to determine whether to

return the patient to a regular hospital bed (or

possibly to discharge the patient), or in the

emergency room to decide whether to admit the

patient to the hospital. The three tests are usually ordered together because of their individual

lack of specificity. If all three are positive, it

helps to confirm the diagnosis, The enzyme tests

are usually accompanied by an electrocardiogram (EKG), which is considered a very specific

test if the classical signs of myocardial infarction

I I IJn]es\ (lt herwlse nt~ted, most (d the bdck~r(wnd md terld I t(~r

this secti(;n IS based (In a review by Rapapt}rt (36,37).

are found. An equivocal or negative EKG, however, is usually not considered sufficient evidence to rule out a myocardial infarction, and

the enzymes and patient history become the

principal diagnostic tools.

If the tests are given too soon or too late

following a myocardial infarction, they are likely to produce more false negatives than if given

at the proper time intervals. LDH elevations

typically appear within 24 hours of the myocardial infarction, peak between 48 and 72 hours,

and return to normal 7 to 10 days later. SGOT

elevations run a shorter course, beginning

within 8 to 12 hours, peaking at 36 to 48 hours,

and returning to normal 4 to 5 days after the

myocardial infarction. CPK follows the shortest

course of all. The elevations first appear in

about 6 hours, peak around 24 hours, and return to normal levels in 3 to 4 days. Thus, a patient whose enzymes are measured within a few

hours of the myocardial infarction is likely to

show a normal LDH. A patient whose enzymes

are measured 48 hours later may show a normal

CPK. For this reason, these enzymes are usually

measured for at least 3 to 4 days before a diagnosis is made. This practice, of course, can be

costly, since it means prolonged hospitalization

for many patients who will turn out not to have

suffered a myocardial infarction.

Isoenzymes are slightly different molecular

forms of an enzyme, all of which catalyze the

same reaction. The difference between isoenzymes is important because the composition of

total enzyme levels differs among body tissues.

Some isoenzymes or isoenzyme profiles are

quite specific for particular organs (e.g., heart

or liver), and therein lies their diagnostic value.

Methods for separating isoenzymes usually take

advantage of their physical properties, and

separation is often accomplished by electrophoresis. Measurement methods based on radioimmunoassay and on calorimetry have also

been developed, and the latter are the basis for

developments toward automation in multichannel analyzers.

The isoenzymes most valuable in the diagnosis of myocardial infarction are the isoenzyme

CPK-MB and the enzymes LDH1 and LDH2

.

CPK has three isoenzymes: CPK1 (or CPK-BB),
Case Study #4: Cost Effectiveness of Automated Multichannel Chemistry Analyzers ● 29

C P K2 (or CPK-MB), and CPK3 (or CPK-MM).

CPK-MB is found only in heart tissue. Its

presence in serum is thought to be a very specific indicator of cardiac damage. However, cardiac damage is not synonymous with myocardial infarction. Congestive heart failure, severe

angina, myocardial ischemia, and cardiac surgery or chest trauma can also cause CPK-MB

levels to rise in the blood, as can certain forms

of muscular dystrophy, polymyositis, or significant myoglobinuria. While CPK-MB is quite

specific for myocardial infarction, its clinical

sensitivity is reduced by the time course of the

elevation, typically less than that for total CPK.

LDH has five isoenzymes. Those that are useful in the diagnosis of myocardial infarction are

LDH1 and LDH2

. In particular, if the serum level

of LDH1 exceeds the level of LDH2

, then the

number of diagnostic possibilities is reduced

considerably. This “flipped LDH pattern” (so

called because in normal serum the LDH2 level

exceeds that of LDH1

) may be observed in patients with a myocardial infarction, but also in

those with acute renal infarction or hemolysis.

Figure 6.—Structure of Decision Analysis for Evaluation

However, since elevated CPK-MB and flipped

LDH have only one cause in common—myocardial infarction—the combination is extremely

specific. Some enthusiasts claim that 80 percent

of “rule-in/rule-out” tests for myocardial infarction cases can be resolved with virtual certainty

on the basis of CPK and LDH isoenzyme results

(15). These authors suggest that SGOT would

have virtually no clinical value in the diagnosis

of myocardial infarction if CPK and LDH isoenzymes were routinely available (15).

Structure for Decision Analysis

The structure of the decision whether to order

enzymes and/or isoenzymes for the patient with

suspected myocardial infarction is diagramed in

the decision tree shown in figure 6. The information required to complete the analysis

consists of data on: 1) probabilities at chance

nodes, and 2) costs and health outcomes at the

end of each path.

The tree begins at the tar left with the ordering of an EKG. Next, at ❑ , is the key decision

of Enzymes in Diagnosis of Myocardial Infarction (Ml)

EKG I
—.——— — —— —. ——.— ——..——

30 ● Background Paper #2: Case Studies of Medical Technologies

under analysis: what combination of enzyme

tests to order. At the first probability node, @,

the EKG results are obtained. At the next probability node, ~, the enzyme (and isoenzyme)

results are obtained. The probability of any particular combination of test results depends,

among other factors, on: 1) the conditional

probability of this set of values, given the true

enzyme levels that would be measured by a perfectly accurate and precise test; 2) the conditional probabilities of combinations of enzyme

levels, given the presence or absence of a

myocardial infarction; and 3) the prevalence of

myocardial infarction in patients with similar

histories and EKG results. This tree structure

assumes that the EKG results are not available

at the time of the decision whether to order enzymes; in some clinical situations, the order of

nodes ❑ and @ may be reversed.

Next comes a decision, at node ❑ : whether

to treat for or to rule out a myocardial infarction. The value of the tests, presumably, lies in

their ability to inform this decision-and, in

turn, in the value of a correct diagnosis of

myocardial infarction, if present, or correct ruling out of myocardial infarction, if absent. The

consequences of false negatives (inappropriate

rule-out ) may be added risk of complications or

even death if the patient does not get proper bed

rest or if the complication occurs outside the

coronary care unit. The consequences of false

positives (inappropriate rule-in) may be primarily economic costs in the form of prolonged

hospitalization or prolonged in the coronary

care unit.

Next, for purposes of analysis, estimates are

needed of the probabilities of the true enzyme

levels, given the measured values (a function of

test precision and accuracy). These are incorporated at probability node @. Given the enzyme

levels, the next probability node, @, indicates

whether myocardial infarction was present or

not. Finally, the clinical outcome unfolds at ~,

given the treatment decision at ❑ and the true

diagnosis as revealed at@. The final outcomes

are grouped, for purposes of cost-effectiveness

evaluation, into two categories: health outcome

(mortality and morbidity) and resource cost (for

diagnosis and treatment ).

The decision tree in figure 6 provides a structure for the review that follows in the remaining

pages of this case study. The discussion of enzyme test measurement relates to the probabilities required at node @, the conditional probabilities of true enzyme levels, given the

measured values. The discussion of diagnostic

value relates to the test result probabilities at

node B and their relation to the conditional o

disease-state probabilities at node@, the latter

being the predictive values of the test results

(e.g., the probability of myocardial infarction,

given a particular set of test values). The discussion of clinical efficacy concerns the probabil-

$7

ities at node G in relation to both the treatment

decision at and the true condition as revealed at @: Does intervention make a difference? The final section considers the cost data

to be associated with each path of the decision

tree and discusses the cost-effectiveness implications of altering the sensitivity and/or the

specificity of the diagnostic workup by the addition or deletion of enzyme and isoenzyme determinations.

Quality of Measurement

Measurement errors for calorimetric determinations of CPK, SGOT, and LDH have been

found to lie well within the bounds recommended by the College of American Pathologists, at least if the tests are carried out under

ideal laboratory conditions. The coefficients of

variation reported, however, depend on the

laboratory.12 An in-house evaluation by Technicon found coefficients of variation of approximately 5 percent for all three enzymes, with excellent linearity of response over the relevant

range of values (41). Other studies have

detected reliability problems in automated

measurement of SGOT, however, with coefficients of variation closer to 10 percent (2,12).

Even more serious than intralaboratory variation is interlaboratory variability, which has

been reported to be as high as 25 percent for

SGOT (12). Despite these caveats, however, the

ability of existing technology to provide reliable

‘

: The c{>etllctent c~t variation in a sample of measurements is the

rat[[~ (~t the sample standard deviation to the sample mean.
Case Study #4: Cost Effectiveness of Automated Multichannel Chemistry Analyzers ● 31

measurements of these enzymes is generally

acknowledged.

Data on test precision and accuracy are

available from the manufacturers for each of

their instruments and can provide estimates of

the probabilistic relations between measured

and true values. These data may be viewed,

however, only as lower bounds on variability in

actual practice. Studies of laboratory performance are needed to provide more realistic probability estimates. Such data can be obtained

from quality-control studies, which are performed by all laboratories under Federal law.

As for the quality of isoenzyme measurement,

coefficients of variation for immunologic methods have been reported to be quite acceptable—

approximately 5 to 10 percent for CPK-MB.

Other, notably calorimetric, methods have been

found to be less reliable (6). The new method

used by Du Pont on its ACA, however, is said

to be as precise as the best available manual

methods, with coefficients of variation well

under 10 percent (10). One problem in measuring CPK-MB is storage of samples, since

CPK-MB may undergo chemical change at higher temperatures, leading to false negatives (reduced sensitivity) (33).

Overall, data relating to quality of measurement are readily obtainable. Published data to

date suggest that measurement error is not a major consideration in the overall evaluation of

cardiac enzymes.

Diagnostic Value

Several studies report estimates of the sensitivity and specificity of the cardiac enzymes

and isoenzymes in the diagnosis of myocardial

infarction. These results, based largely on

hospital patients, are summarized in table 5.

With a few exceptions, the results are quite consistent across studies. These data support the

view that CPK, LDH, and SGOT are rather sensitive, but only moderately specific. In combination, considering a test positive only if all

three are positive, the specificity improves considerably, but at some loss of sensitivity.

CPK-MB and the flipped LDH do appear to

be rather specific, but probably not perfectly so.

No data on the joint sensitivity and specificity

of the isoenzymes were available, except for the

study by Galen, et al. (15), the results of which

are given in table 6. They find that CPK-MB elevation and flipped LDH never occur together in

the absence of myocardial infarction and that

CPK-MB is always elevated in its presence.

However, CPK-MB elevation and flipped LDH

both occur in only 80 percent of myocardial infarction cases, and CPK-MB is elevated in 15

percent of nonmyocardial infarction cases.

The data reported in the literature are deficient in several respects. First, there are few

estimates of probabilities of combinations of enzyme test results, Such data would be needed to

evaluate the incremental diagnostic value of one

test (e.g., SGOT), given that other tests are

already available. It would be inappropriate to

assume that the tests are conditionally independent, since many of the causes of serum enzyme elevation are not unique to a single enzyme. Assuming conditional independence

would tend to overestimate the incremental

diagnostic value of any single test.

A second, and more serious, deficiency in the

data is that there is no independent definition of
.— . .

32 ● Background Paper #2: Case Studies of Medical Technologies

Table 6.—ResuIts of Galen, et al., on Joint

Occurrence of Elevated CPK-MB Isoenzyme and

Flipped LDH Isoenzymes

Probability, Probability y,

Finding given Ml given no Ml

CPK-MB+, flipped LDH . . . 0.8 0

CPK-MB +, normal LDH . . . 0.2 0.15

CPK-MB – 

. . . . . . . . . . . . . . . 0 0.85

SOURCE: R. S. Galen, et al., ‘(Diagnosis of Acute Myocardial Infarction: Relative

Efficiency of Serum Enzyme and Isoenzyme Measurements,”

J.A M.A. 232:145, 1975.

myocardial infarction. In most studies, the tests

being evaluated are also used to define the presence or absence of disease. This can lead to two

scenarios, both of which lead to bias in the estimation process. One procedure is to restrict the

population under study to those with overwhelming evidence of myocardial infarction or

no myocardial infarction, either as confirmed

by a positive EKG finding or as ruled out by the

absence of enzyme elevations other than the one

under study. Such a procedure overestimates

test sensitivities and specificities by restricting

the study population to those who may be most

or least likely to show signs of myocardial

infarction-the equivocal cases are thus excluded. The other procedure reported by some

investigators is to use the enzyme values themselves to classify patients as having myocardial

infarction or not. This obvious circularity also

creates bias in the estimation process and will

tend to overestimate sensitivities and specificities if the enzyme levels are conditionally dependent with positive correlations.

The resolution of this problem of evaluating

the diagnostic value of tests which themselves

provide the only basis for defining the disease

state remains a topic of methodologic research.

One promising approach may be to bypass the

evaluation of diagnostic value, which is really

only a way-station in the overall evaluation

process, and to proceed directly to measures of

clinical efficacy in terms of prognostic value and

patient outcome. For example, one might estimate the survival curves and morbidity rates for

patients with a particular pattern of test results.

The rates would be conditional on the treatment

alternative (e. g., hospitalized or not), but irrespective of any attempt to classify the patient

as having had a myocardial infarction or not.

The incremental value of any enzyme test would

be measured in terms of its expected information value (calculated by averaging out an

appropriate decision tree). This value would depend on the consequences to the patient of making the right or wrong treatment decision, in

terms of patient outcomes, rather than in terms

of the test’s ability to classify patients into

diagnostic categories.

A third limitation of the available data is that,

for the most part, the data ignore the question of

defining a positive or abnormal enzyme level. In

actuality, enzyme levels are measured on a continuum, and the choice of a positivity criterion

is itself a decision. By selecting a stringent

criterion, one can gain specificity but at a loss of

sensitivity; with a lax criterion, one gains sensitivity but loses specificity. In order to include

these considerations in the analysis, one would

need data on the probability distributions of

values for each enzyme individually and in combination for patients with and without myocardial infarction, Data on the joint distribution of

results are not available in the literature,

although some data for individual tests have

been reported (14).

The final missing datum needed for evaluation of diagnostic value is the prevalence of

myocardial infarction in the population tested.

As argued earlier, the prevalence, or prior probability of disease, has an important effect on the

predictive value of a test result. Several studies

agree that for patients hospitalized with symptoms suggesting myocardial infarction, the

prevalence of myocardial infarction is approximately 50 percent (13,18). In patients with

equivocal symptoms, who would be tested if

cardiac enzymes were included in routine

screening of hospital patients, the prevalence of

myocardial infarction in the population tested

would be much lower. Therefore, the predictive

value positive would be lower than it would be

among those with symptoms of myocardial infarction. Moreover, patients with myocardial

infarction detected by screening would be more

likely to have uncomplicated cases which would

be less likely to benefit from prolonged hospitalization than would symptomatic cases (29).
Case Study #4: Cost Effectiveness of Automated Multichannel Chemistry Analyzers ● 33

Clinical Efficacy

The final piece of information on which the

ultimate value of cardiac enzyme tests relies

concerns the value of a correct diagnosis.

Specifically, what are the consequences of “ruling out” a patient with myocardial infarction?

What are the consequences of “ruling in” a patient without it? Since the consequences of a

false “rule-in” (false positive) are largely economic, we defer most of the discussion of the

latter question to the next section of this case

study. It should be noted, however, that an erroneous “rule-in” may have the adverse consequence of diverting the physician’s attention

from the investigation of other, treatable causes

of the patient’s symptoms. The probability that

such a treatable condition might be present

would depend on the nature of the symptoms

and the rest of the patient’s history.

The consequences of a false “rule-out” (false

negative) depend on the value of interventions,

which may range from care in a coronary care

unit, to hospitalization with bed rest, to bed rest

at home. The value of bed rest, whether at home

or in a hospital, during the first few days following a myocardial infarction, when the risk of

ventricular fibrillation is greatest, is widely

acknowledged. It is less clear whether bed rest

can reduce the subsequent risks of reinfarction

or arrhythmia and sudden death.

If bed rest were the only intervention of

value, then the consequences of a false “ruleout” might be that the patient would not benefit

from the reduction in risk afforded by that intervention. However, for patients with severe

chest pain, or other symptoms bringing them to

the hospital, bed rest is often the treatment of

choice for other conditions they may have, such

as acute ischemia. The intervention that has

historically been specific for myocardial infarction is hospitalization, and in recent years,

hospitalization in a coronary care unit. But the

consequences of not hospitalizing a patient with

myocardial infarction are a matter of some controversy, and therein lies the problem in evaluating the clinical efficacy of the enzyme tests

used in the diagnostic workup.

A complete review of the evidence on the effects of prolonged hospitalization and intensive

care units on mortality and morbidity of patients with myocardial infarction would constitute a case study in itself. A review of the

literature reveals numerous studies, many of

them British, indicating that home care is at

least as effective as hospital care following

myocardial infarction, and that shorter hospital

stays are at least as effective as longer stays

(21,29). Despite this evidence, the consensus in

the United States is now at about 10 to 12 days

hospitalization, possibly preceded by 3 to 7

days in the coronary care unit. Objective data

supporting this practice are not available,

however.

It is on this perceived benefit, measured as the

difference in risk for myocardial infarction patients who are hospitalized and those who are

not, that the clinical value of the diagnostic

workup for myocardial infarction, including the

enzymes, rests. If this difference were zero, the

information value of the cardiac enzymes would

be zero (excluding their purely prognostic value,

independent of effects on health outcomes). The

uncertainty surrounding this issue is the major

obstacle to evaluation of the clinical value of

cardiac enzyme and isoenzyme determinations

in the diagnosis of myocardial infarction.

Cost Effectiveness

The diagnostic value of the cardiac enzymes

lies in their ability to improve the predictive

value in diagnosing myocardial infarctions.

This involves reducing either the false-negative

or false-positive rate,

Reducing the false-negative rate is tantamount to improving test sensitivity. If the cardiac enzymes are as sensitive as the literature

reviewed above suggests, then their cost effectiveness would depend on the clinical value of

identifying and treating a patient with myocardial infarction, compared with the added cost of

diagnosis and treatment. As noted earlier, however, the health benefits from treatment in a

hospital remain in doubt. Hence, the costs of the

tests themselves, and the induced costs of hospi-
—— . . .

34 ● Background Paper #2: Case Studies of Medical Technologies

talization of patients “ruled-in,” must be

weighed against the hypothetical benefit of intervention.

Reducing the false-positive rate is tantamount to improving test specificity; it is in this

domain that isoenzymes are said to have the

greatest value. The value of improving the correct “rule-out” rate is largely economic; many

patients may be spared unnecessary hospitalization, with ensuing savings both to society and to

individual patients.

A study by Gerber, et al. (18) estimates the

net economic savings that may be expected if the

diagnostic test battery for myocardial infarction

is expanded to include routine CPK-MB and

LDH isoenzymes. Even imputing a cost as great

as the institution’s charge for these tests ($23 for

each isoenzyme, $7 for each total enzyme, and

$21 for an EKG) and assuming a complete workup for each of 4 days in the hospital, the cost

savings resulting from earlier discharge of the

additional “rule-outs” easily outweigh the diagnostic testing costs (18). This appears to be a

case where the induced savings in treatment

costs exceed the direct costs of testing.

Gerber’s analysis, however, assumes that the

current practice of hospitalizing patients unless

there is strong evidence against myocardial infarction will prevail. Given that state of affairs,

whether clinically justified or not, the evidence

seems to suggest that if physicians trust the

specificities of EKGs and enzyme and isoenzyme

tests, their net economic effect in symptomatic

patients who are candidates for hospitalization

may actually be to reduce costs. A limitation of

the Gerber study for purposes of evaluating the

enzymes and isoenzymes individually, however, is that the incremental contributions of the

enzymes and isoenzymes to the overall cost savings were not reported. It could be that the EKG

alone accounted for most of the savings. This

study does suggest, however, that this would be

a promising area for further research.

The net economic effect of enzyme determinations, if applied to patients asymptomatic for

myocardial infarction, as distinguished from

acute cases, is undoubtedly to increase costs,

while the corresponding health benefit is questionable. Therefore, the overall cost effectiveness of this use of LDH, SGOT, CPK, and

the isoenzymes rests on evidence not yet available on the health benefits of intervention in the

natural course of myocardial infarction.

The overall cost effectiveness of automating

any test must be based on an assessment of all of

its principal clinical uses. It is possible that the

uses of CPK, SGOT, and LDH in the diagnosis

of myocardial infarction alone can justify automating these tests. If

to examine as well

costs associated with

the use of LDH and

liver disease.

Efficiency

not, then one would have

the benefits and induced

other clinical uses, such as

SGOT in the diagnosis of

Whether it is efficient, given a particular testordering policy, to reserve three analyzer channels for cardiac enzymes may depend on whether the machine to be used is a continuous-flow

or discrete-sample analyzer. Reagent costs for

CPK are among the highest of all tests (see table

3). Therefore, if CPK is requested for a sufficiently small proportion of samples, the costs of

reagents consumed by continuous-flow analyzers could be considerable. For discrete-sample

analyzers, this would not be a concern because

reagents would be consumed only for those tests

actually ordered. However, the fixed costs associated with a CPK channel and its maintenance

would apply to both kinds of analyzers. Whether a continuous-flow multichannel analyzer, a

discrete-sample multichannel analyzer, or single-channel equipment is the most efficient

method of producing CPK, LDH, and SGOT results depends on the pattern of test ordering,

and this pattern, in turn, would optimally depend on an assessment of the effectiveness and

induced costs for each of their diagnostic uses.
Case Study #4: Cost Effectiveness of Automated Multichannel Chemistry Analyzers ● 35

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