A C Silveira

J W M Bassani

N F Oshiyama

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J. Vander Sloten, P. Verdonck, M. Nyssen, J. Haueisen (Eds.): ECIFMBE 2008, IFMBE Proceedings 22, pp. 1581–1584, 2008 
www.springerlink.com © Springer-Verlag Berlin Heidelberg 2009 
Health Technology Management: Medical Equipment Classification 
N.F. Oshiyama1, A.C. Silveira1 and J.W.M. Bassani1,2 
1 Universidade Estadual de Campinas (UNICAMP)/Departamento de Engenharia Biomédica,  
Faculdade de Engenharia Elétrica e Computação, Campinas, Brazil 
2 Universidade Estadual de Campinas (UNICAMP)/Centro de Engenharia Biomédica, Campinas, Brazil 
Abstract —  This paper presents two methods for medical 
equipment classification according to three maintenance indi-
cators (NCC = annual number of corrective maintenances 
divided by the median of the number of corrective mainte-
nances for the specific maintenance group; TMC = time spent 
on corrective maintenance per year divided by the median of 
the time for the specific maintenance group, $MC = annual cost 
of corrective maintenance divided by 6% of the depreciated 
acquisition cost of the equipment). The main tool was a simple 
database for each type of equipment. As shown in a previous 
paper, one of the methods is based on the fact that the indica-
tor values increase as the equipment gets older. The other 
method is based on the ABC analysis, in which three limits 
were established based on Pareto’s law. Data of syringe infu-
sion pumps from years 2004 - 2006 (database of the Center for 
Biomedical Engineering, University of Campinas) were used. 
One-way analysis of variance revealed that NCc, TMc and 
$Mc values increase significantly with age. Out of 50 syringe 
infusion pumps, 42% were classified as C, although 80% of the 
equipments were not in the 10-14 year category. The results of 
the ABC and age analyses were 96% coincident. Maintenance 
indicators seem to keep a relation with the equipment age and, 
if used in nationwide scale, they might be a robust tool for 
equipment classification. Unexpected behaviors may be rele-
vant warnings to the Clinical Engineering staff, regulatory 
agencies and manufacturers interested in data mining. 
Keywords — Clinical Engineering, equipment maintenance, 
medical equipment, maintenance indicators, ranking 
I. INTRODUCTION 
Current technological development increases the variety 
of medical equipment available to the market. Since these 
new technologies usually create a disproportion between the 
costs and the resources available, Clinical Engineering (CE) 
work becomes more important and requires more efficient 
management methods [1][2]. One of the main problems in 
this effort, however, is to find the appropriate way to obtain 
quantitative information on equipment performance along 
its lifetime. This type of information may be useful to help 
regulatory agencies to identify aberrant behavior in medical 
devices and investigate its causes before patients are harmed 
or deficient results are delivered to particular health pro-
vider. In practical terms, it is quite valuable for the CE 
group to know whether a specific device performs well 
according to some criteria that take into account a larger 
population of similar devices under care of different CE 
groups. 
This work proposes two methods for medical equipment 
classification based on three maintenance indicators, which 
could be used at any hospital, independently of the number 
of beds or pieces of equipment. One of the methods is based 
on the hypothesis that the maintenance indicators increase 
as equipment ages (Aging Method, AM). The second 
method is based on the ABC (“Always Better Control”) 
analysis and is presented as a simpler, more limited, but yet 
useful method (in  short, ABCM). 
II. MATERIALS AND METHODS 
In order to calculate the three indicators in the AM, data 
were collected from the Center for Biomedical Engineering 
(CEB) of the University of Campinas (UNICAMP), Campi-
nas, Brazil, database from 2004 to 2006. A Microsoft® 
Excel™ spreadsheet was prepared with medical equipment 
attributes, such as identification, manufacturer, model, serial 
number, location, acquisition price and date, as well as the 
number of corrective maintenances (NC), total maintenance 
cost ($M) and time (TM). 
The three annual indicators are then calculated as fol-
lows: 
 Year
NCmedian
NCNC
L
C ⎟⎟
⎠
⎞
⎜⎜
⎝
⎛
=
)(
 (1), 
where NCL is the number of corrective maintenances from 
the local maintenance group. 
 Year
TMmedian
TMTM
L
C ⎟⎟
⎠
⎞
⎜⎜
⎝
⎛
=
)(
 (2), 
where TML is the total corrective maintenance time from 
the local maintenance group. 
 Year
A
MM
D
C ⎟⎟
⎠
⎞
⎜⎜
⎝
⎛
⋅
=
$06,0
$$   (3), 
where $AD is the depreciated acquisition cost. 
1582 N.F. Oshiyama, A.C. Silveira and J.W.M. Bassani 
_______________________________________________________________  IFMBE Proceedings Vol. 22  _________________________________________________________________  
In the first method, equipments of a given type are di-
vided in three age ranges (categories 0-4, 5-9 and 10-14 
years) and the mean ± SEM is calculated  for each indicator, 
resulting in )$,,( CCCi MTMNCI = , where i is the age 
range, and the standard errors (SEM) of each indicator rep-
resent the classes limits (A, B, C).   
The significant increase in indicator values, as the 
equipments age [3], was detected by one-way analysis of 
variance (P<0.05). 
Then, for each equipment indicator (IE), the following 
analysis was made:  
 Class A: SEMIISEMI AEA +≤≤−    (4), 
where AI  is the average class A indicator value.   
 Class B: SEMIISEMI BEB +≤≤−    (5), 
where BI  is the average class B indicator value.  
 Class C: SEMIISEMI CEC +≤≤−     (6),  
where 
CI  is the average class C indicator value. 
If IE is a value between the interval of two classes, the 
indicator is classified as the class to which the distance from 
the average value is smaller. 
The final equipment classification is A if all the indica-
tors attained A, and C if at least one of the indicators was C; 
otherwise, the equipment was classified as B. 
The second method requires the normalization of the 
three indicators, in which they are divided by the respective 
maximum individual value (equations 7-9). Subsequently, 
they are linearly added (equation 10), resulting in a single 
maintenance performance indicator. 
 100
)max(
⋅⎟⎟
⎠
⎞
⎜⎜
⎝
⎛
=
C
CN
C NC
NCNC  (7) 
 100
)max(
⋅⎟⎟
⎠
⎞
⎜⎜
⎝
⎛
=
C
CN
C TM
TM
TM   (8) 
 100
)max($
$$ ⋅⎟⎟
⎠
⎞
⎜⎜
⎝
⎛
=
C
CN
C M
MM     (9) 
  NC
N
C
N
C MTMNCP $++=         (10) 
All P values are summed to calculate the total P in the 
population of a given equipment type. Then, the percent 
contribution of the P value of each equipment (P(%)) to the 
total P value is calculated (equation 11) and P(%) values are 
ordered from the highest to the lowest. The next step is to 
calculate the cumulative sum of P(%) values (CS) from the 
first value in the sequence to that of a specific equipment 
(equation 12).  
 
100(%)
1
⋅=
∑
=
n
i
iP
PP
       (11), 
where n is the number of equipments. 
 ∑
=
=
i
j
ji PCS
1
(%)        (12), 
The CS value of each piece of equipment is used for its 
classification. If %70≤CS , the equipment is classified as 
C; if %90%70 ≤< CS , the equipment is classified as B. 
Otherwise, it is classified as A, because, according to 
Pareto’s law, a small fraction of the equipments is responsi-
ble for the major loss of profits due to maintenance, i.e, 
10% of the equipments are responsible for 70% of the CS, 
20% are responsible for 20% of the CS and the other 70% 
for only 10% of the CS [4][5][6]. 
III. RESULTS 
Table 1 show the spreadsheet data for syringe infusion 
pumps, including equipment attributes, calculated values of 
the indicators, the final score for classification using both 
methods, and the results of the one-way analysis of  vari-
ance.  
Figure 1 shows that, as the equipment ages, the three in-
dicators tend to increase significantly, monotonically in 
most cases. 
NCc x Age range
A B C
0
1
2
1.49
0.79
1.15
TMc x Age range
A B C
0
1
2
3
4
5
6
4.87
1.040.95
$Mc x Age range
A B C
0.0
0.5
1.0
1.5
1.21
0.52
0.27
 
Fig. 1 Each indicator was plotted as a function of the age range, which 
represented the behavior for each category (0-4 years as category A,  
5-9 years as category B and 10-14 years as category C) 
Health Technology Management: Medical Equipment Classification 1583 
_______________________________________________________________  IFMBE Proceedings Vol. 22  _________________________________________________________________  
Table 1Data for 26 out of 50 syringe infusion pumps. Id= equipment identification; Local= place where the equipment is used;   the indicators as defined in 
the text. The results of one-way analysis for each indicator were:  TMc: F=58.64, DF =47, p< 0.001; $Mc: F=26.01, DF = 47, p< 0.001; NCc:  F=6.04; DF 
=47; p< 0.005. Results for the classification by using the age method (Age Class), as well as the ABC method (ABC Class), are also presented. 
Id Local Dept Manufacturer Model SN Age TMc TMc 
Class
$Mc $Mc 
Class
NCc NCc 
Class
Age 
Class
TMC
N $MC
N NCC
N P P(%) CS ABC 
Class
1 L1 D1 M1 Mod1 1 13 5,45 C 2,00 C 1,49 C C 83,61 82,34 60,00 225,94 4,96 4,96 C
2 L1 D2 M1 Mod1 2 12 5,04 C 0,89 C 1,00 B C 77,28 36,67 40,00 153,95 3,38 48,03 C
9 L1 D2 M1 Mod1 9 11 4,72 C 0,66 B 1,00 B C 72,37 27,39 40,00 139,76 3,07 51,09 C
10 L1 D1 M1 Mod2 10 10 0,92 A 2,42 C 2,49 C C 14,05 100,00 100,00 214,05 4,70 9,65 C
11 L5 D5 M2 Mod4 11 9 3,65 C 0,62 B 1,99 C C 55,97 25,50 80,00 161,47 3,54 41,17 C
12 L2 D5 M2 Mod4 12 9 0,11 A 0,13 A 0,50 A A 1,64 5,56 20,00 27,20 0,60 99,42 A
13 L5 D5 M2 Mod4 13 9 0,31 A 0,27 A 1,00 B B 4,68 11,13 40,00 55,81 1,22 87,47 B
14 L5 D5 M2 Mod4 14 9 0,31 A 0,13 A 0,50 A A 4,68 5,56 20,00 30,25 0,66 98,19 A
16 L5 D5 M2 Mod4 16 9 1,54 B 0,74 B 1,00 B B 23,65 30,60 40,00 94,26 2,07 70,29 B
21 L5 D5 M2 Mod4 21 8 3,97 C 0,15 A 0,50 A C 60,89 6,14 20,00 87,03 1,91 72,20 B
22 L5 D7 M3 Mod5 22 6 0,00 A 0,00 A 0,00 A A 0,00 0,00 0,00 0,00 0,00 100,00 A
23 L4 D1 M1 Mod2 23 5 1,01 B 1,17 C 2,49 C C 15,46 48,32 100,00 163,78 3,59 37,63 C
25 L4 D1 M1 Mod2 25 5 1,86 B 0,74 B 1,49 C C 28,57 30,49 60,00 119,06 2,61 59,40 C
26 L4 D1 M1 Mod3 26 5 0,67 A 1,17 C 2,49 C C 10,30 48,32 100,00 158,62 3,48 44,65 C
27 L5 D5 M2 Mod5 27 4 2,05 B 0,81 B 1,49 C C 31,38 33,41 60,00 124,79 2,74 56,78 C
28 L5 D5 M2 Mod5 28 4 0,27 A 0,10 A 0,50 A A 4,22 4,20 20,00 28,42 0,62 98,82 A
29 L5 D5 M2 Mod5 29 4 0,90 A 0,57 B 0,50 A B 13,82 23,39 20,00 57,21 1,25 86,25 B
30 L3 D6 M4 Mod7 30 3 0,63 A 0,23 A 0,50 A A 9,60 9,45 20,00 39,06 0,86 90,14 A
31 L1 D3 M1 Mod1 31 0 0,66 A 0,15 A 0,50 A A 10,07 6,19 20,00 36,26 0,80 91,76 A
33 L1 D3 M1 Mod1 33 0 1,21 B 0,43 B 1,49 C C 18,50 17,63 60,00 96,13 2,11 66,15 C
37 L1 D3 M1 Mod1 37 0 1,15 B 0,14 A 0,50 A B 17,56 5,88 20,00 43,44 0,95 88,42 B
42 L1 D3 M1 Mod1 42 0 0,34 A 0,14 A 0,50 A A 5,15 5,88 20,00 31,03 0,68 96,18 A
45 L1 D3 M1 Mod1 45 0 0,87 A 0,14 A 0,50 A B 13,35 5,88 20,00 39,23 0,86 89,28 B
46 L1 D3 M1 Mod1 46 0 0,73 A 0,56 B 1,49 C C 11,24 23,16 60,00 94,40 2,07 68,22 C
49 L1 D3 M1 Mod1 49 0 0,52 A 0,14 A 0,50 A A 7,96 5,88 20,00 33,84 0,74 95,50 A
50 L1 D3 M1 Mod1 50 0 1,30 B 0,43 B 1,49 C C 19,91 17,63 60,00 97,54 2,14 64,04 C  
 
Results from the analysis using the two methods were   
96% coincident for the present data sample. Even though 
only 20% of the syringe infusion pumps belonged to the 10-
14 year-old category, 42% of the total were classified as C. 
Since 48% of this equipment inventory was in the 0-4 year-
old category, this result represents a typical warning to the 
CE staff. An in depth analysis is then required for safety 
purposes [7].  
Equipments from different manufacturers presented dif-
ferent scores. For instance, relatively new equipment (0-4 
year-old) from M1 definitely underscored, as only 23% 
obtained final classification as A.  In this particular case, a 
real problem was reported by the CE maintenance team, 
namely the frequent spontaneous variation of infusion rate, 
which required an excessive number of safety tests and 
prolonged repair times.  
IV. DISCUSSION  
The two methods presented here are easy to use and can 
be adapted to the different peculiarities of the health sys-
tems in different country regions or countries. 
In Brazil, for example, the range of medical equipment 
ages is quite wide in the majority of public hospitals, as 
seen in the present set of data. In this case, it is mandatory 
that one knows how the equipment age may impact its reli-
ability and maintenance cost, so that its replacement may be 
planned in advance. 
There are advantages and disadvantages inherent to each 
of the present methods. When the AM is used, predeter-
mined limits for the scores are based on an intrinsic prop-
erty of the medical devices: aging increases the maintenance 
frequency, time to repair and cost [8]. When the number of 
equipments in the inventory is large (as in a nation-wide 
integrated system), robustness is improved and the informa-
tion extracted from a simple spreadsheet filtering becomes 
extremely valuable for many purposes, as, for example, 
warning the regulatory agencies and institutions on type- 
and/or manufacturer-dependent equipment misbehavior. 
Additionally, the same data could be used to establish better 
standards for CE service delivery: once the indicators are 
normalized by median of a given CE staff, an elevated per-
centage of poorly classified equipment localized in only one 
medical institution could indicate that equipment might be 
mishandled by the users or be under deficient maintenance 
procedures. If the number of equipments of each type is not 
large enough, statistics will fail to prove that AM works and 
allows the age class separation.  In this case, the appropriate 
1584 N.F. Oshiyama, A.C. Silveira and J.W.M. Bassani 
_______________________________________________________________  IFMBE Proceedings Vol. 22  _________________________________________________________________  
numbers could be taken from any larger and reliable data-
base available (if a national database exists, it could be the 
gold standard). Nevertheless, if the relation age vs. indicator 
is monotonically crescent, AM could still be applied even 
for small databases.  
If the hospital does not have heterogeneous equipment 
age distribution (which could happen in less than 1% of the 
hospitals in countries like Brazil) it would be difficult to use 
the AM because of the insufficiency of well-defined age 
ranges. In this case, the classification based on the ABC 
analysis could be used, since the class limits are established 
based only on the indicators values of the equipment sample 
under analysis. The indicators could also be weighted (as to 
comply with mission-dependent hospital directives) but 
since they are linearly added one should be careful in defin-
ing these weights to avoid biased results.  
These classification methods could also be used by 
manufacturers interested in data mining, since the indicators 
could call attention for potential problems related to the 
interaction of a particular technology with the environment 
in which the technology is applied.  
From 50 syringe infusion pumps, 42% were classified as 
C, even though only 20% were in the older category (10-14 
years). This shows that many infusion pumps had a behav-
ior that was not expected for their ages. Out of 24 new (0-4 
year-old) infusion pumps, only 41% were classified as A 
and, when the analysis included comparison among manu-
facturers, it was interesting to see that M1 produced  59% of 
the new equipment, but only 23% were classified as A. If 
such an observation was made in a large database, including 
several hospitals, it would be sufficient to set an alarm for 
regulatory agencies, which could initiate prompt investiga-
tion on this particular type, brand and/or model of equip-
ment. In a local scale, the information provided by this 
classification could also help hospitals plan equipment ac-
quisition, not only regarding replacement timing, but also 
allowing selection of better performing equipments (accord-
ing to manufacturer and/or model).   
The fact that AM and the ABC analysis produced similar 
results, as concluded from equal classification in 96% of the 
cases, means that the maintenance indicators seem to hold a 
close relationship with the equipment age and perhaps the 
strategy  used to grade the equipments by using  AM  is 
reasonable. 
V. CONCLUSIONS 
This paper presents and compares two methods for 
equipment classification that can be used in any healthcare 
system, independently of the number of beds or equipment. 
Both methods, AM or ABCM, can easily be imple-
mented and used. If the equipment ages are not heterogene-
ous and equipment inventory small, ABC analysis is ade-
quate and sufficient. On the other hand AM, was proved 
valid and tends to become more robust as the number, vari-
ety and age heterogeneity of the equipment sample increase. 
The idea that medical equipment management can be im-
proved by measuring maintenance data seems valid. The 
main results of the equipment classification are the possi-
bilities of producing alerts both for the local CE staff, as 
well as for regulatory agencies, provided that a nation-wide 
standard database is available. 
ACKNOWLEDGMENT 
 Authors are indebted to Dr. Rosana A. Bassani and to 
Clinical Engineer Ryan P. Ferreira for the criticism and 
suggestions. Financial Support: (CNPq, Grant. N. 
300632/2005-3, JWMB), SAE/UNICAMP scientific initia-
tion scholarship, TECSER Facilities Management. 
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Author:  Natália Ferreira Oshiyama 
Institute:  Universidade Estadual de Campinas 
Street: Centro de Engenharia Biomédica CP 6040 
City:  Campinas 
Country:  Brazil  
Email: naty.engclinica@gmail.com 
 


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