This document provides an overview of the Analytic Hierarchy Process (AHP) decision support system and presents a case study on using AHP to make medical equipment scrapping decisions. The key points are: 1) AHP breaks down a complex decision problem into a hierarchy, then uses pairwise comparisons to determine criteria weights and rank alternatives. It was used in this case study to evaluate 9 dialysis machines for potential scrapping. 2) Criteria for the dialysis machine scrapping decision included age, performance, safety record, and costs. Data was incomplete so the study simulated different scenarios to examine the impact. 3) AHP derived local and global priorities to determine each machine's overall priority for scra

1. Abstract— Decision Support Systems (DSS) are very
important in clinical engineering (CE). The Analytic Hierarchy
Process (AHP) is one of such systems that has been extensively
and successfully used in many areas, including CE. In this
paper, we provide an overview of different DSS and explain
AHP in details. At the end, a case study using AHP for taking
medical equipment scrapping/ retirement decision is presented.
I. INTRODUCTION
N the busy environment of any healthcare facility,
decisions have to be made all the time. Most decision
problems fall into the category of multi-attribute / criteria
decision problems, i.e. decisions involving a finite number
of alternatives and a finite number of criteria upon which
such alternatives are evaluated. Having an organized,
structured way of evaluating alternatives or different courses
of action available as a solution to the decision problem is
crucial to produce reasonable, justified, and unbiased
decisions.
The selection of a DSS for a certain problem depends on
several criteria, including the complexity of the problem, the
simplicity of the method, the type of data available
(quantitative or qualitative or both), the expertise of the
decision makers etc.
Some work has been done to evaluate different DSS.
Peniwati suggests 16 criteria for such evaluation and draws a
comparison between them [1].
In this paper some of the most common multi-criteria DSS
will be described, followed by a detailed description of one of
them; AHP, and then an example of using AHP to implement
equipment scrapping decision is presented.
II. LITERATURE REVIEW
Multi-criteria DSS vary in complexity from simple,
elementary methods to sophisticated ones. Follows is a brief
description of some of these methods.
A. Pros and Cons Analysis
In this method the pros, i.e. positive aspects, of each
alternative are compared against its cons, i.e. negative
aspects, such that the alternative whose pros are more and
stronger that its cons is the preferred one. This method
depends on qualitative comparisons and is suitable for
simple decisions with few criteria and alternatives.
B. Kepner-Tregoe (K-T) Decision Analysis
This method depends on the judgments/ assessments of a
Manuscript received September 26, 2010
Asmaa Ahmed Kamel is with the Department of Systems & Biomedical
Engineering, Faculty of Engineering, Cairo University, Giza, Egypt (e-mail:
asmaaakamel@hotmail.com).
Bassel Sobhi Tawfik is the head of the Department of Systems &
Biomedical Engineering, Faculty of Engineering, Cairo University, Giza,
Egypt.
group of experts, where the less the quality of the data, the
larger the group required [2].
The most important criterion is identified and given a
score of 10 and then the rest of criteria are evaluated relative
to it, where less important criteria can be given scores down
to 1. In the same manner, the alternatives are evaluated
relative to each other against the previously weighted criteria
and given scores from 1 to 10. The final score of an
alternative is determined by multiplying its score under each
criterion by the score of that criterion and adding for all
criteria. The alternative with the highest score is the most
preferred.
C. Cost-benefit Analysis
This method is used when the primary criterion for
making the decision is money or cost of a given alternative
vs. its benefit, such that finally the alternative with the
largest net present value is preferred. Note that, all other
methods treat cost like any criterion.
D. Multi-Attribute Utility Theory (MAUT)
This method uses “utility” or preference functions to
transform criteria from different scales into a common,
dimensionless scale with values from 0 to 1; where for each
criterion a utility function is created [3]. Utility functions
can be derived from studies or statistics related to the type of
alternatives involved. Once these functions are determined,
they are used to convert the alternatives raw data, whether
subjective or objective, into a dimensionless utility score.
The criteria are weighted according to their importance, then
the alternatives normalized utility scores are multiplied by
these weights and added for all criteria, such that the
preferred alternative has the highest score.
E. Analytic Hierarchy Process (AHP)
Using AHP, decisions involving both qualitative &
quantitative data can be effectively made. AHP was
developed by T.L. Saaty in the 1970s [4] and has been used
and also refined extensively since then.
In general, AHP applications can be split into the main
categories of choice/selection; a best alternative is selected
among a group, prioritization/ evaluation; a combination of
alternatives are selected instead of one, benchmarking;
different processes/entities are compared with one another or
with the best of breed and rated accordingly.
In healthcare, AHP was used for multiple applications. A
university hospital used it to help patients take decisions
regarding undertaking a prostate cancer screening test [5]. It
was also used for the selection of residents for a five-year
general surgery program at a major metropolitan hospital
[6], where in the study the correlation of the results of the
AHP and the traditional scoring system were found to be
statistically valid. AHP was used to select neonatal
ventilators to be used in a planned expansion of a neonatal
Decision Support Systems in Clinical Engineering
Asmaa Ahmed Kamel, Bassel Sobhi Tawfik
I

2. intensive care unit [7]. The use of AHP is such
multidisciplinary decision was found satisfactory and the
authors conclude that it should be used for future healthcare
technology assessment decisions.
Besides healthcare, in 2001 AHP was used to determine
the best relocation site for the earthquake that devastated the
Turkish city Adapazari. Also in 1998, the British Airways
used it choose the entertainment system vendor for its entire
fleet of airplanes. Xerox Corporation has used it to allocate
close to a billion dollars to its research projects [8].
1) Structure
In AHP, the decision problem is decomposed into a
number of small problems and structured in the form of a
hierarchy; with the goal of the decision at top, followed by
the criteria and sub-criteria (if there are any) upon which the
alternatives are evaluated. Alternatives are always found at
the bottom of the hierarchy as in Fig. 1
The criteria at each level are compared in pairwise
comparison matrices using the absolute scale of
measurements in Table I, such that the criteria at the first
level are compared against each other with respect to the
goal (i.e. they are compared as to which is more important
with respect to the goal) and the criteria at subsequent levels
are compared with respect to the higher level parent
criterion. Finally, the alternatives are compared with each
other with respect to all lower level criteria (also known as
covering criteria).
2) Priorities derivation methods
Different methods are available for deriving priorities or
weights from the comparison matrices. A debate over the
best method to be used started as early as the AHP was
created and hasn’t been resolved till today.
Saaty shows that the principal eigenvector is the priority
vector for a consistent matrix [9]. He calls it “the only
plausible candidate for representing priorities derived from
a positive reciprocal near consistent pairwise comparison
matrix”.
We use the eigenvalue method because in agreement with
Saaty we think that the principal eigenvector best describe
the matrix properties. Also the eigenvalue method is the only
method that uses the indirect estimations in a pairwise
comparison matrix for the calculation of the priorities and
thus the only method that takes into consideration the
transitivity property of the matrices.
Fig. 1. General AHP Hierarchy
TABLE I
THE FUNDAMENTAL SCALE
Intensity of
Importance
Definition Explanation
1 Equal Importance Two activities contribute
equally to the objective
3 Moderate importance Experience and judgment
slightly favor one activity
over another
5 Strong importance Experience and judgment
strongly favor one activity
over another
7 Very strong or demonstrated
importance
An activity is favored very
strongly over another; its
dominance demonstrated in
practice
9 Extreme importance The evidence favoring one
activity over another is of
the highest possible order
of affirmation
2,4,6,8 For compromise between the
above values
Sometimes one needs to
interpolate a compromise
judgment numerically
because there is no good
word to describe it.
Reciprocals of
above
If activity i has one of the
above nonzero numbers
assigned to it when compared
with activity j, then j has the
reciprocal value when
compared with i
A comparison mandated by
choosing the smaller
element as the unit to
estimate the larger one as a
multiple of that unit.
1.1-1.9 For tied activities When elements are close
and nearly
indistinguishable; moderate
is 1.3 and extreme is 1.9.
3) Different types of priorities
The priorities obtained from each comparison matrix are
the local priorities, i.e. the priorities driven for a set of nodes
with respect to a single criterion. Global priorities are
obtained by multiplying these local priorities by priority of
the parent criterion. The overall priorities for an alternative
are obtained by adding its global priorities throughout the
hierarchy.
4) AHP modes
In general, there are two types of measurements or
comparisons, relative and absolute. In relative comparisons,
alternatives are compared in pairs against a common
attribute, whereas in absolute comparisons alternatives are
compared against a standard in memory developed through
experience.
Following this concept, AHP has two types of methods to
deal with different problems, the Relative Method and the
Absolute or Ratings Method.
a) The Relative Method: In this method, the criteria and
alternatives are compared and priorities derived in the way
described at the beginning.
b) The Ratings Method: In this method, the criteria are
pairwise compared, and then rating categories are made for
each covering criterion. After that these ratings are
prioritized by pairwise comparing them for preference.
Alternatives are then evaluated by selecting the appropriate
rating category on each criterion. For example, in a decision
problem to select the best job from a number of alternatives,

3. the rating categories for a “Job Security” criterion was High,
Medium and Low. For a “Reputation” criterion, it was
Excellent, Above Average, Average and Poor.
The relative method is used in case of dependence among
the alternatives, whereas the absolute method is used when
the alternatives are independent of each other. This is why
the relative method allows the change of the ranking of the
alternatives in case of the addition of a new one, while the
absolute method doesn’t allow rank reversal.
Also the relative method where alternatives are compared
with each other under the various criteria is more accurate,
while the ratings method has the advantage of rating a large
numbers of alternatives rather quickly.
5) Consistency Index
A human judgement in real life situations may be
inconsistent. AHP provides a way of measuring the
inconsistency of each comparison matrix judgements [4].
High values of the inconsistency ratio may call for refining
the hierarchy, revising user’s judgements, collecting more
data etc. before taking the decision. However, higher
consistency doesn’t necessarily mean better or more accurate
decisions.
6) Sensitivity Analysis
Sensitivity analysis is performed to test the stability of the
final ranking of the alternatives, to make sure that no criteria
are overlooked, and to detect any errors that might have
occurred while rating the alternatives. In this analysis the
priorities of all criteria or at least the most important ones
are varied, e.g. from 0.01 to 0.99 in six steps, then
alternatives new priorities are obtained, and finally the effect
of varying the criteria priorities on the final ranking of the
alternatives is examined.
III. CASE STUDY: EQUIPMENT SCRAPPING/ RETIREMENT
DECISION
Equipment scrapping is one of the classical problems
facing clinical engineers. Whether to retire a piece of
equipment or not, when to retire it, and what can be done
with the retired equipment are some of the questions
frequently encountered while taking such a decision. The
need to scrap a piece of equipment may result from
equipment being unsafe, its maintenance is beyond
economical repair or simply because its technology is
obsolete.
A. Equipment scrapping criteria
The possible criteria upon which equipment scrapping
decision can be made were examined and gathered as shown
in Table II. Some of these criteria are applicable to different
medical equipment types, while others may be device-
specific.
The subject of our decision is nine hemodialysis machines
(Fresenius 4008B, installed in a local healthcare facility)
that were to be evaluated for scrapping. So the criteria
applicable to hemodialysis machines were extracted from
Table II and arranged in the hierarchy shown in Fig. 2.
TABLE II
EQUIPMENT SCRAPPING POSSIBLE CRITERIA
Criteria Sub-criteria Definition
Device age Time since installation, in terms
of years or working hours
Technology
status
Degree of maturity of device
technology (from visionary to
obsolete technology)
Performance
(this criterion is
highly dependent
on equipment
type and thus
can have
more/different
sub-criteria)
Equipment
down-time
Mean Time
Between
Failures
(MTBF)
The expected time between two
consecutive failures for a
repairable system
Accuracy/
Calibration
This sub-criterion may also
descend from safety
Failures Types One possible categorization is
failures that can be eliminated &
those that can’t be
Safety Number of
hazardous
incidents
Incidents resulting in the injury/
harm of patient/operator
Device Recall The action of correcting a
product-related problem or
removing it from the market, as a
result of being either defective or
potentially harmful.
Support
availability
Service support The know-how to troubleshoot a
problem & diagnose the defect
Spare parts
Backup
equipment
i.e. if the facility has a large
number of equipment doing the
same function, it may be more
towards scrapping the less
performing ones.
Cost Spare parts
Repair/
maintenance
Equipment
upgradability
Upgradability can help eliminate
or reduce an existing problems
Equipment
utilization
percentage
i.e. if the equipment utilization
percentage is low and it’s already
in a bad condition, then it might
be scrapped (and it even needn’t
be replaced)
Some criteria are inapplicable because of hemodialysis
machines properties, for example “Equipment
upgradability”, while others because of this study specific
machines, for example “Device recall” (4008B machine is
not FDA-approved) and “Service and spare parts
availability” (the manufacturer still supports this model).
However, other criteria were excluded because data are
unavailable at the healthcare facility. Data deficiency was
dealt with in a number of ways as will be explained later in
the paper.
B. Model Implementation
The hierarchy was implemented using the Super
Decisions Software (designed by Bill Adams and the
Creative Decisions foundation) [10]. It implements the AHP
and its generalization, in case of dependence and feedback
between the criteria and alternatives, the Analytic Network
Process.

4. Fig. 2. Equipmen
Fig. 3 shows
was used eva
priorities.
C. Problem S
Evaluating
should be car
policy of the
technology f
comparison
technology le
equally high
first think o
“Technology
“Technology
the ratings fo
and Establish
weight to the
finally estab
follower will
reverse order.
While the
given to the
such that fin
device, the h
have a feeling
added a fictiti
under each o
idealized prio
machines prio
D. Simulatio
As a result
data to crea
differences b
simulated the
1) No data
incidents
However
criteria th
calibratio
respectiv
biomedic
nt Scrapping Dec
a snap shot of
aluate the mac
Statement
a given dev
rried out whil
involved heal
follower or
of the prim
eader is more
priority, whe
of “Performa
status & A
status” criteri
or this criterio
hed”, a tech
e state of the
blished techn
l arrange the
.
ratings were
factors in fa
nally the high
higher its prio
g of how high
ious machine
of the cover
orities its prio
orities are a fr
on Scenarios
of data defici
ate new situ
between the m
following sce
were availab
s, calibration v
r, while evalu
hey were rate
on limits,
vely. This wa
cal engineerin
ision Hierarchy
f the program
chines and de
ice using the
e keeping in
lthcare facility
leader, as th
mary criteria
likely to hav
ereas a techno
ance & Saf
Age”. Also t
ion will be di
n are “State o
hnology leade
e art, followe
nology, whe
ese ratings f
compared, m
avor of scrapp
her the weig
ority for scrap
h or low such
“H” which w
ring criteria s
rity equals “1
raction of this
iency, we dec
uations and b
machines. To
enarios:
ble on the nu
values or mai
uating the m
ed as were ra
and >40%
as based on w
ng technician
m. The ratings
erive their res
e hierarchy in
mind the tech
y, i.e. whethe
his will affe
a, for exam
ve the 4 criter
ology follower
fety” and le
the ratings f
ifferent, for ex
of the Art, Ma
er will give
ed by maturin
ereas a tech
for importanc
more importan
ping the equi
ght/score of a
pping. Howe
h final weight
was rated as th
such that usi
1” and the res
“1”.
ided to add fi
better delinea
owards this e
umber of haz
ntenance/repa
machines unde
ated as none,
of device
what the resp
n at the hea
method
spective
n Fig. 2
hnology
er it is a
ect the
mple, a
ria with
r might
east of
for the
xample,
aturing,
higher
ng, and
hnology
ce in a
nce was
ipment,
a given
ever, to
t is, we
he worst
ing the
t of the
ictitious
ate the
end we
zardous
air cost.
er these
outside
value
ponsible
althcare
2)
A.
F
we
sho
ob
the
be
can
we
55
fol
sce
me
mo
the
the
B.
the
sam
“D
wh
pri
the
facility said
these machi
We once
technology-
technology
primary c
“Technolog
explained e
Simulation S
Following the
ere found to
ows the idea
btained by div
em (fictitious
tter in underst
n also be con
e can say that
.8%.
Changing th
llower in the
enario increa
eaning that a
ore towards s
e machines ch
e 2nd became
FI
Machin
1
2 0X
3 7V
4 1X
5 7V
6 0X
7 7V
8 0X
9 0X
10 0X
SEC
Machin
1
2 0X
3 7V
4 7V
5 1X
6 0X
7 7V
8 0X
9 0X
10 0X
Sensitivity A
From the sen
e machines ra
me order sho
Device safety”
hile the rankin
imary criteria
e second scena
d about the rea
ines.
assumed the
-leader health
follower one
riteria as w
gy status” c
earlier.
IV. R
Scenarios Res
e first scenari
be as shown
alized prioritie
viding all the
machine H).
tanding the re
nverted into a
machine 0XF
he problem s
first scenario
ased the tota
technology le
scrapping the
hanged, where
3rd etc. as sh
TAB
IRST SCENARIO R
ne Serial No. R
H
XFE0829
V5E6787
XFE1223
V5E6795
XFE0355
V5E6786
XFE352
XFE351
XFE354
TAB
COND SCENARIO
ne Serial No. R
H
XFE0829
V5E6795
V5E6787
XFE1223
XFE0352
V5E6786
XFE355
XFE351
XFE354
Analysis
nsitivity analys
anking was f
own in Table
” and “Techno
ng varied unde
a priorities. Si
ario.
asons that cal
machines to
hcare facility
e, such that t
well as the
riterion will
RESULTS:
sults
io, the nine m
in Table III.
es of the ma
priorities by
The idealized
esults than the
percentage fo
FE0829 needs
statement fro
o to a technol
al priorities
eader healthca
machines. Al
e the 4th mac
hown in Table
BLE III
RESULTING PRIORI
Raw Priorities I
0.222071
0.123941
0.100459
0.098610
0.085670
0.082898
0.081320
0.080893
0.064379
0.059761
BLE IV
RESULTING PRIOR
Raw Priorities I
0.169861
0.117287
0.103202
0.101344
0.092848
0.091013
0.088481
0.083401
0.078031
0.074532
sis results for
found to be s
III) under th
ology status”
er the variatio
imilar results
lled for scrapp
o be workin
y and then
the ranking o
ratings of
be differen
machines prio
. The last col
chines, which
the largest am
d priorities ca
e raw priorities
orm. For exam
to be scrappe
om a techno
logy leader in
of the mach
are facility wi
lso the rankin
chine became
IV.
ITIES
Idealized Priority
1
0.558117
0.452372
0.445047
0.385778
0.373294
0.366189
0.364265
0.289902
0.26911
RITIES
Idealized Priority
1
0.690488
0.607569
0.59663
0.54661
0.535807
0.520899
0.490993
0.459383
0.43878
r the first scen
stable (having
he variation in
criteria prior
on in the rest o
were obtaine
ping
ng in
in a
f the
the
nt as
rities
lumn
h are
mong
an be
s and
mple,
ed by
ology
n this
hines,
ill be
ng of
2nd,
y
y
nario,
g the
n the
rities,
of the
d for

5. Fig. 3. Hierarchy implementation by the super decisions software
V. DISCUSSION:
The nine machines showed relatively close final priorities;
one possible reason for this is that for the
“Accuracy/Calibration”, “Technology Status”, and
“Repair/Maintenance cost” criteria, all the machines had the
same rating. This indicates the need to add more criteria
under which the machines can score differently to have more
discriminative values.
The main differences between the machines while rating
them were found to be in the Equipment down-time, MTBF,
and Age; this indicates that more weight might be given to
these criteria.
As shown in Table IV, changing the problem statement
from a technology follower to a technology leader increased
the total priorities of the machines besides slightly changing
their order. This means that a technology leader healthcare
facility will be more towards scrapping these machines. It
also suggests that the simulation of more scenarios can
further discriminate the machines and provide more
indicative results.
A possible scenario may assume that some of the
machines are running in a private hospital and the rest in a
public hospital. Regarding the “Maintenance/repair cost”
criteria, for a private hospital it is more economical to
replace the equipment when its maintenance/repair cost is
beyond a certain ratio of the device cost (which is the right
thing to do), while a public/governmental hospital will keep
maintaining the machine despite the high cost because it
doesn’t have the capital investment required to replace the
machine. So for the first type of healthcare facilities, more
weight will be given to the “Maintenance/repair cost” and
the ratings threshold is more likely to be less than the 40%
used in the second type.
The instability of the machines ranking under the
variation of some criteria is not a good thing and reasons for
this need to be further investigated.
VI. CONCLUSION
We found AHP relatively easy to use for the discussed
decision problem. It provided deeper analysis than the
manual methods that are usually implemented using very
few criteria or even one criterion, for instance, only cost.
However, care must be taken while building the decision
hierarchy such that only important criteria discriminating the
alternatives should be included. Also the experience and
knowledge of involved decision makers is very important to
have meaningful comparisons. The lack of experience may
be compensated for by having as much and as accurate data
as possible; concerning the different criteria and alternatives.
When scrapping takes place, management should seek
statistics showing the frequency with which each cause has
contributed to the situation, in order avoid the same
problems in the future. This can be derived from the ratings
of the device under different criteria. Also such ratings, in
addition to the criteria weights can help in determining what
to do with the scrapped equipment. For example, if
“Technology status” criterion had the highest weight and the
equipment was rated as being of established technology,
then the problem isn’t with the device but with the
healthcare facility policy and so the equipment can be put to
use elsewhere, for example, donated to a charity clinic.
For future work and in order to ensure the adequacy of
AHP for this kind of decisions, machines of different models
and from different manufacturers shall be evaluated. Also
other external factors affecting hemodialysis machines
condition, like quality of water from treatment units and
other environmental conditions shall be incorporated in the
model.
A software tool is being developed to implement the
decision hierarchy and carry out the necessary mathematical
operations to derive priorities and calculate alternatives final
weights.
REFERENCES
[1] Kirti Peniwati, “Criteria for Evaluating Group Decision-Making
Methods”. Mathematical and Computer Modelling, vol. 46, no. 7-8,
pp. 935-947, October 2007.
[2] C.H. Kepner and B.B. Tregoe, “The New Rational Manager”,
Princeton Research Press, Princeton, NJ, 1981.
[3] R.L. Keeney and H. Raiffa, “Decisions with Multiple Objectives:
Preference and Value Tradeoffs”, John Wiley, New York, 1976.
[4] T. L. Saaty, "How to Make a Decision: The Analytic Hierarchy
Process", Interfaces, vol. 24, no. 6, p. 19-43, 1994.
[5] E.B. Sloane, M. J. Liberatore, and R. L. Nydick, “Medical Decision
Support Using the Analytic Hierarchy Process”, Journal of
Healthcare Information Management, vol. 16, no. 4
[6] M. S. Weingarten, R. L. Nydick, F. Erlich, and M. J. Liberatore “A
Pilot Study of the Use of the Analytic Hierarchy Process for the
Selection of Surgery Residents.” Academic Medicine, vol. 72, no. 2,
pp. 400-401, 1997.
[7] E. B. Sloane, M.J. Liberatore, R.L. Nydick, W. Luo, and Q.B. Chung,
“Using the analytic hierarchy process as a clinical engineering tool to
facilitate an iterative, multidisciplinary, microeconomic health
technology assessment”, Computers & Operations Research, vol. 30,
pp. 1447–1465, 2003.
[8] T.L. Saaty, “Decision making with the analytic hierarchy process”,
Int. J. Services Sciences, vol. 1, no. 1, 2008
[9] T. L. Saaty, “Decision-making with the AHP: Why is the principal
eigenvector necessary?” Proceedings of the sixth International
symposium on the Analytic Hierarchy Process, Berne-Switzerland,
August 2-4, 2001.
[10] Super Decisions, version no. 2.0.8, 01 Jun 2009. Available:
http://www.superdecisions.com/index_tables.php3
[11] J. Tobey Clark, Healthcare Technology Replacement Planning,
Clinical Engineering Handbook, ELSEVIER academic Press, ch. 42