EVALUATION OF BIOMEDICAL LABORATORY PERFORMANCE 
OPTIMISATION USING THE DEA METHOD
Nejc LAMOVŠEK
1*
, Maja KLUN
2
1
University Medical Centre Ljubljana, The Division of Stomatology, Hrvatski trg 6, 1000 Ljubljana, Slovenia
 
2
University of Ljubljana, Faculty of Public Administration, Gosarjeva ulica 5, 1000 Ljubljana, Slovenia
Received: Nov 13, 2019
Accepted: Jun 1, 2020 
Original scientific article
*Corresponding author: Tel. + 386 51 267 246; E-mail: nejc.lamovsek@gmail.com
10.2478/sjph-2020-0022 Zdr Varst. 2020;59(3):172-179
OVREDNOTENJE OPTIMIZACIJE DELOVANJA 
BIOMEDICINSKIH LABORATORIJEV Z METODO DEA
Lamovšek N, Klun M. Evaluation of biomedical laboratory performance optimisation using the DEA method.  
Zdr Varst. 2020;59(3):172-179. doi: 10.2478/sjph-2020-0022.
ABSTRACT
Keywords: 
optimisation, 
biomedical 
laboratory, DEA 
method, efficiency, 
consolidation
IZVLEČEK
Ključne besede: 
optimizacija, 
biomedicinski 
laboratoriji, metoda 
DEA, učinkovitost, 
konsolidacija
Introduction: The Slovenian Resolution on the National Healthcare Plan notes that the country’s medical 
laboratory activities are fragmented, which may result in cost-inefficiency and a reduction in the quality of the 
services provided. Defining the efficiency of laboratory service providers can therefore help us to pursue the 
objectives of the Resolution, i.e. to consolidate and integrate laboratory activities.
Methods: Using the DEA method, we conducted an analysis of the efficiency of 20 biomedical laboratories in 
Slovenia, and made a comparison with a “virtual” laboratory, i.e. a merger of laboratories within a selected 
organisational unit. By testing different DEA models, we sought to determine whether the use of different input 
variables caused significant differences in the laboratories’ efficiency scores.
Results: The research results show that inefficiency resulting from the size of the units is 1.5 times greater than 
process inefficiency. Using a non-parametric Wilcoxon Signed Rank test, we determined, at a risk level of 0.05, 
that there was no difference between the efficiency results when using two different technical efficiency DEA 
models. When evaluating the virtually merged laboratory, we determined that, under all three models, the virtual 
laboratory achieved 100%  VRS efficiency. However, when the CRS methodology was used, the laboratory showed 
a certain degree of scale inefficiency.
Conclusions: When evaluating merger of medical laboratories we note that the DEA method is methodologically 
suitable for evaluating the effects of health policy implementation, and is an appropriate tool for identifying 
where the field of laboratory medicine might be further developed and improved.
Namen: Resolucija o nacionalnem planu zdravstvenega varstva v Sloveniji ugotavlja, da je medicinska 
laboratorijska dejavnost pri nas razdrobljena, kar ima lahko za posledico slabšo kakovost opravljenih storitev in 
stroškovno neučinkovitost laboratorijev. Opredelitev učinkovitosti izvajalcev laboratorijskih storitev lahko tako 
pomaga zasledovati cilje resolucije, tj. konsolidacijo in integracijo laboratorijske dejavnosti.
Metode: Z metodo analize ovojnice podatkov (metoda DEA) smo opravili analizo učinkovitosti dvajsetih 
biomedicinskih laboratorijev v Sloveniji ter primerjavo z navideznim laboratorijem, ki predstavlja združitev 
laboratorijev v izbrani organizacijski enoti. S preizkusom različnih vhodno orientiranih modelov DEA smo 
želeli ugotoviti, ali zaradi uporabe različnih vhodnih spremenljivk obstajajo bistvene razlike pri določitvi 
končne učinkovitosti laboratorijev. Določili smo tako tehnično kot tudi stroškovno učinkovitost biomedicinskih 
laboratorijev.
Rezultati: Rezultati raziskave kažejo, da je na primeru analiziranih laboratorijev kar 1,5-krat višja neučinkovitost, 
ki izhaja iz velikosti obravnavanih enot, od procesne neučinkovitosti. Z uporabo neparametričnega testa 
Wilcoxon Signed Rank smo pri stopnji tveganja 0,05 ugotovili, da ne obstaja razlika med rezultati učinkovitosti z 
uporabo dveh različnih modelov določanja tehnične učinkovitosti. Pri oceni navidezno združenega laboratorija 
smo ugotovili, da navidezni laboratorij dosega 100-odstotno čisto tehnično učinkovitost, vendar pa pri uporabi 
metodologije CRS izkazuje določeno stopnjo neučinkovitosti in tudi ni na meji proizvodnih možnosti.
Zaključek: Kadar ocenjujemo vplive združevanja medicinskih laboratorijev ugotavljamo, da je metoda DEA 
metodološko primerna za evalvacijo učinkov izvajanja zdravstvene politike ter tudi ustrezno orodje za opredelitev 
nadaljnjega razvoja in izboljšav na področju laboratorijske medicine.
172
© National Institute of Public Health, Slovenia. 

1 INTRODUCTION
For centuries, laboratory medicine has been one of 
the most important factors in providing proper patient 
care and promoting technological development in the 
field of medicine. The development of automation and 
information technology has led to the development of 
sensitive and specific laboratory tests that are invaluable 
to doctors when diagnosing and confirming diseases (1, 2).
As a part of the public healthcare network, medical 
laboratories provides services that directly affect patients; 
furthermore, it provide services to other stakeholders, 
i.e. healthcare professionals, healthcare payers and health 
policy-makers (3). While the tendency to improve quality 
and productivity in the field of laboratory medicine 
is a constant one, it is important to bear in mind the 
constraints, which include staff shortages and limited 
funding. It is these constraints that compel laboratory 
managers to constantly consider introducing work 
processes and technologies that could optimise laboratory 
organisation and performance (4). 
The issue of achieving efficiency is considered to be 
one of the most important intermediate objectives of 
health system effectiveness (5). Achieving efficiency 
in healthcare is often equated with the introduction of 
a free market economy, which can affect equal access 
to services, fairness in resource allocation and the 
achievement of quality standards (6). As a result of this 
dilemma, laboratory activities are treated differently in 
different systems. When measures to improve efficiency 
are adopted, a great deal of attention is devoted to 
laboratory concentration, which is essential if capacity is 
to be better utilised.
The efficiency of public service providers can be 
determined by means of different methods. However, 
data envelopment analysis (DEA) is one of the most 
commonly used methods in the field of healthcare (7, 8). 
The introduction of management systems, management 
processes and resource management planning aimed 
at improving efficiency in the healthcare sector is an 
important element in reducing costs and increasing 
productivity in healthcare organisations (9). One further 
aspect can be considered when our aim is to achieve 
greater efficiency, namely the use of technology in business 
processes. Technological development means that there 
is an increased need for greater specialisation of work, 
which leads to shorter turnaround times in laboratory 
medicine. In such cases, innovative ideas can significantly 
boost the efficiency and effectiveness of biomedical 
information systems (10). The laboratory work process 
is divided into several important and interconnected 
phases. By using the ABC process diagram, we can 
describe the individual phases in laboratory workflow (11, 
12). One of the most important aspects of the renewal 
of work processes in the field of laboratory medicine is 
the introduction of functional automation. The merging 
of laboratories into a central hospital laboratory reduces 
overall costs because the number of biomedical analyzers 
(and therefore the associated labour costs) can be reduced 
(13). Our conclusion is that work process improvements 
resulting from the consolidation of laboratories and the 
determination of surpluses in the inputs used can only 
be identified through a thorough knowledge of the work 
process in biomedical laboratories.
The aim of our study is to determine the efficiency of 
biomedical laboratories by testing different specifications 
of DEA models. Our research question is whether 
the consolidation of laboratory activity can increase 
laboratory efficiency and, at the same time, reduce the 
costs allocated to laboratory services. The study also aims 
to evaluate the effects of a potential merged biomedical 
laboratory, and offer an optimally constructed biomedical 
laboratory at the tertiary healthcare level. The study is 
based on data from 20 biomedical laboratories in Slovenia.  
1.1 Definition of Laboratory Activities and their 
Necessary Development in Slovenia
Slovenian health policy is determined by the Resolution 
on the National Healthcare Plan 2016–2025 (“Together 
for a Healthy Society”), the Health Services Act, and 
the Healthcare and Health Insurance Act. The Resolution 
identifies laboratory medicine as part of the health network 
and as an important factor in ensuring proper, high-
quality patient management. It also notes that laboratory 
activities in Slovenia are fragmented and that they fail 
to provide sufficient test concentration; this results 
in incomparability of laboratory test results between 
individual laboratories and may lead to a reduction in the 
quality of the services provided and to cost inefficiency. 
One of the objectives of the Resolution is therefore to 
highlight the need to integrate and consolidate laboratory 
activities (14).
Slovenian experts in the field of laboratory medicine 
have identified the non-consolidation of laboratories 
and indirect payment for laboratory services as the 
main problems currently being faced. The consolidation 
and coordination of laboratories and the introduction of 
direct payment for laboratory services were identified as 
possible solutions to the current situation (15).
According to the records of the Ministry of Health, 
Ljubljana University Medical Centre (UKC) has eight 
separate biochemical laboratories, each under its own 
organisational unit. The Merged Laboratories Project, 
which aims to bring together the four largest UKC medical 
laboratories under one organisational unit and one 
location, is currently under way at the hospital. 
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1.2 Literature Review
Among the most widely used methods for efficiency 
analysis are ratio analysis, total productivity factor, the 
least squares method (COLS), stochastic frontier analysis 
(SFA) and data envelope analysis (DEA). The DEA method 
offers some advantages – specifically, an empirically 
determined frontier of production possibilities without a 
predetermined production function, and the possibility of 
creating goals for less efficient units (16). 
A few healthcare DEA efficiency studies in the field of 
primary medical care (17-19) do exist, and they all come 
to a similar conclusion: that smaller primary healthcare 
centres are more scale inefficient. DEA efficiency studies 
of hospitals at the secondary healthcare level (5, 20-22) 
are also quite common. The results of the studies suggest 
that an understanding of hospital functioning, together 
with the results obtained from efficiency studies, can help 
us determine optimal hospital size. 
While efficiency research has not been conducted to any 
great extent in the field of laboratory medicine, there are 
studies (23, 24) that identify laboratory efficiency using 
the DEA methodology and that provide a basis for the 
further development of efficiency research in laboratory 
activities.
Research on the impact of hospital mergers on 
determining hospital efficiency and potential surpluses 
in the inputs used is also important for our study. 
Efficiency improvements can usually only be achieved by 
improving scale efficiency (25–28). Several studies use 
virtual hospital mergers as a tool to show the differences 
between unconsolidated and combined hospital activity 
(25, 27, 29). However, (30) identifies, in theoretical and 
methodological terms, the contributions of the virtually 
merged unit model used. This indicates the possibility of 
using the method of virtual merger of DMUs as a tool for 
identifying potential improvements and defining surpluses 
in the inputs used at potentially merged biomedical 
laboratories. 
2 METHODOLOGY 
Historically, the development of methods for assessing 
efficiency began with Debreu (1951), Koopmans (1951) 
and Farrell (1957), who laid the foundations for assessing 
the relative efficiency of DMUs (decision making units). 
A technically efficient input-oriented manufacturer 
therefore produces a constant output by minimising 
production factors. The appropriate ratio of inputs in 
terms of their price further defines allocative efficiency. 
Economic efficiency is a product of both technical and 
allocative efficiency (7, 26, 31).
In 1978 Charnes developed a linear program based on a 
non-linear program, i.e. a DEA method for analysing the 
efficiency of DMUs. The method has become particularly 
well-established in the public sector, since the frontier 
of linearly linked segments is determined on the basis 
of empirical data without a pre-specified production 
function. The CRS (constant return to scale) or VRS 
(variable return to scale) model can be used in the analysis 
to predict the technology used (7, 8, 32). 
The input-oriented CRS model can be mathematically 
presented in the form of a linear program derived from 
a fundamental fractional program. The CRS model (Eq, 
1), also named the CCR model, is a constant return model 
and is for n DMUs, of which each DMUj uses m inputs 
x
ij
, (i=1,...,m) and s outputs y
rj
, (i=1,...,s), written in linear 
form (33).
Eq.1
Values y
rj
, and x
ij
 are the given values of outputs and 
inputs, indicating past operating results. Values µ
r
 and v
i
 
are variable weights enabling each unit to be weighted for 
its optimal benefit function within the given limitations, 
determined by the values of variable y and x with all 
units. Efficient units are those that reach the ratio 1. 
The definition is written in the input form; therefore, the 
proportionally inefficient units reach the ratio <1.
The difference between the CRS and VRS models is present 
in the free variable, which is a dual variable associated 
with the constraint. The CRS therefore represents 
technical (cost) efficiency, while the VRS result represents 
pure technical (cost) efficiency. Scale efficiency (SE) is a 
ratio between CRS and VRS efficiency and enables us to 
define whether the cause of inefficiency lies in the non 
optimal size of the observed unit. We can also define cost-
based scale efficiency (CSE).
DEA research on efficiency in healthcare is mostly input-
oriented; this is because healthcare managers only control 
used resources, which is also supported by systematic 
reviews of DEA research (16, 34). 
The selection of input and output variables was based 
on the research (23, 24, 35, 36). Laboratory output is 
produced by means of capital, labour and the use of 
consumables. In all the models presented, we define 
output as the number of basic, special and reference 
tests. Therefore, in our case, Model 1 (M1) represents 
the number of working hours, the number of biomedical 
analyzers, and the cost of laboratory material and 
reagents. Model 2 (M2) represents the number of working 

hours, the total purchase price of laboratory equipment, 
and the cost of laboratory materials and reagents. The 
cost model (M3) is defined by labour costs and the cost 
of laboratory material and reagents. In the technical 
efficiency model, we tried to determine whether there 
was a difference in determining technical efficiency by 
using a different capital input.
Because laboratories have more control over the inputs 
they consume than the outputs they produce, we used 
the input-oriented CRS and VRS DEA method to determine 
the efficiency of medical laboratories with an additional 
calculation of SE. By determining the surplus in the inputs 
used, we quantified the redundant resources used.
We constructed a virtual laboratory, based on the 
expected merger of four UKC laboratories at a single 
location, which consists of the sum of the actual values 
of the inputs and outputs of the four UKC laboratories. 
Using an ABC laboratory work process chart, we identified 
individual phases within which the work process could be 
optimised.
2.1 Empirical Data and Analysis
In our research, we determined the efficiency of 
laboratory service providers in Slovenia at the primary, 
secondary and tertiary healthcare levels. Eleven hospital 
biochemical laboratories (B), the laboratories of three 
tertiary institutions (T) and three major laboratories at 
the primary level (Z) were included in the study. All four 
UKC laboratories slated for merger were included in the 
analysis. The data for our research is not publicly available, 
and we obtained it from public healthcare institutions 
solely for the purposes of our research. We analysed data 
from 20 Slovenian medical laboratories for 2017. Due to 
the restricted set of laboratories analysed and the use of 
multiple inputs and outputs, our estimation of laboratory 
efficiency could potentially be higher than it actually is.
Data for our study was analysed using the Frontier program 
and the SPSS statistical program.
10.2478/sjph-2020-0022 Zdr Varst. 2020;59(3):172-179
175
The smallest B1 laboratory has six employees who carry 
out a total of 7,458 effective working hours. The largest 
T1 laboratory has 107 laboratory workers who carry out 
the largest number of basic laboratory tests annually, i.e. 
5,616,624. Its labour costs are EUR 2,290,144. The labora-
tories have 21 biomedical analyzers on average and spend 
an average of EUR 942,724.43 on reagents and laborato-
ry material. A certain degree of expected heterogeneity 
is present on the side of output, since SDs may be 50% 
higher than average values. As is apparent from the value 
of the outputs, heterogeneity is present because of the 
specialised services of individual health institutions that 
go beyond the scope and sphere of influence of laborato-
ry activity, which adjusts the range of laboratory tests it 
offers and performs. 
3 RESULTS
Table 2 shows the results of efficiency analysis using the 
CRS and VRS methodologies. As the aim of our study is to 
determine the optimal size of the merged UKC laboratory, 
we will primarily focus on presenting the results of the 
UKC laboratories in defining the results of the analysis.
Table 1. Statistical analysis.
Source: own.
INPUTS
OUTPUTS
Number of working hours
Number of biomedical analyzers
Cost of laboratory equipment
Labour costs
Cost of laboratory reagents and material
Number of basic tests
Number of special tests
Number of reference tests
42,604.85
21
601,112.50
657,896.07
942,724.43
941,786.90
121,054.15
18,066.60
7,458
6
49,616
97,332.54
134,683
49,985
2,651
24
35,947.78
16
590,995.57
591,933.26
945,991.67
1,277,335.30
170,071.16
26,016.38
157,746
68
2,437,057
2,290,144
4,520,071
5,616,624
784,245
111,940
Average Min. SD Max.

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176
As Table 2 shows, the average technical efficiency of lab-
oratories under Model 1 is 81.43%. As expected, average 
pure technical efficiency is higher (92.63%). Under Model 
2, we observe higher average technical efficiency than un-
der Model 1 (82.71%). However, the average pure technical 
efficiency is lower (92.23%). A comparison between labo-
ratories when using the two models shows that SD is the 
highest in CRS technology prediction, i.e. for laboratories 
B11 and T2.  
Table 2 shows that, when using cost Model 3, the aver-
age cost-efficiency score is, as predicted, lower (76.65%) 
than the technical efficiency score. Similarly, we observe 
a pure cost-efficiency score (87.36%) that is lower than the 
pure technical efficiency score.
We can establish that the laboratories observed show 
scale inefficiency that is 1.5 times higher than process in-
efficiency. This suggests that in the laboratories examined 
in our study, a greater share of inefficiencies result from 
suboptimal laboratory size than from the way laborato-
ries work. They could therefore improve their technical 
efficiency mainly by adjusting their process size. The B3, 
B4, B6, T1, T3, Z1 and T6 laboratories are both CRS- and 
VRS-efficient, which means that they are of optimal size. 
They form the frontier of production possibilities in the 
use of both models and, furthermore, the B7 and B11 labo-
ratories are scale efficient under Model 1. The lowest effi -
ciency was observed in connection with the B1 laboratory 
under all three efficiency models.
Our simulation was carried out in response to the planned 
merger of laboratories T1, T2, T3 and T4. The merging of 
these laboratories is therefore easier to carry out because 
the laboratories already operate within a single institu-
tion. The potential consolidation of other laboratories in 
our analysis could be based on their geographical proxim-
ity. The T1 and T3 laboratories are efficient when using 
both the CRS and VRS methodologies, which means they 
are also of optimal size. The T2 laboratory does not show 
process inefficiency when using Model 1. All inefficiency 
results from incorrect size. As the RTS of laboratories T2 
and T4 is increasing, they could only achieve scale efficien-
cy by increasing their size. This could be achieved through 
an increase in the resources used, as well as through a 
proportional increase in the number of laboratory tests 
provided. Model 2 produces a similar result. 
In terms of determining technical efficiency, the potential 
merger of the T1, T2, T3 and T4 laboratories would have 
Table 2. Efficiency results.
DMU
B1
B2
B3
B4
B5
B6
B7
B8
B9
B10
B11
Z1
Z2
Z3
T1
T2
T3
T4
T5
T6
x(%)
SD
CRS (%)
34.93
38.78
100
100
69.81
100
100
75.14
64.53
88.82
100
100
76.90
66.40
100
72.28
100
80.87
60.04
100
81.43
21.01
CRS (%)
39.86
45.25
100
100
70.54
100
78.08
80.15
66.66
99.94
81.03
100
86.97
75.73
100
92.33
100
77.59
60.04
100
82.71
18.87
CRS (%)
37.26
40.36
100
100
64.05
100
55.75
71.74
63.83
89.61
97.54
87.36
76.9
66.4
100
77.62
100
46.14
58.44
100
76.65
21.78
VRS (%)
100
68.93
100
100
88.59
100
100
86.14
77.56
97.37
100
100
77.56
96.40
100
100
100
87.49
72.64
100
92.63
10.60
VRS (%)
100
71.79
100
100
80.64
100
86.62
90.16
79.06
100
96.73
100
87.34
100
100
100
100
79.57
72.64
100
92.23
10.29
VRS (%)
100
66.74
100
100
73.40
100
60.32
74.01
75.18
100
100
100
77.56
100
100
100
100
52.87
67.17
100
87.36
16.68
RTS
1
1
-
-
1
-
-
1
1
1
-
-
-1
1
-
1
-
1
1
-
RTS
1
1
-
-
1
-
1
1
1
1
1
-
1
1
-
1
-
1
1
-
RTS
1
1
-
-
1
-
1
1
1
1
1
-1
-1
1
-
1
-
1
1
-
SE (%)
34.93
56.26
100
100
78.80
100
100
87.23
83.20
91.22
100
100
99.15
68.88
100
72.28
100
92.43
82.65
100
87.35
17.79
SE (%)
39.86
63.03
100
100
87.48
100
90.14
88.90
84.32
99.94
83.77
100
99.58
75.73
100
92.33
100
97.51
82.65
100
89.26
15.42
SE (%)
37.26
60.47
100
100
87.26
100
92.42
96.93
84.90
89.61
97.54
87.36
99.15
66.40
100
77.62
100
87.27
87
100
87.56
16.38
Model 1 Model 2 Model 3
Source: own.

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a positive impact, mainly due to the more optimal size of 
the T2 and T4 laboratories, which would now be part of a 
larger laboratory. 
A comparison between the technical efficiency and 
cost-efficiency of the laboratories shows that the T4 lab-
oratory has the biggest difference (34%). Further analysis 
should be carried out to determine whether the cause of 
the inefficiency may lie in the sub-specialisation of the T4 
laboratory.
We used the Wilcoxon Signed Rank non-parametric test to 
determine statistical difference in the efficiency results. 
Using a significance level of 0.05, we established that the 
efficiency results for the two technical efficiency models 
in the case of our sample were not different (W 25>W 
critical 14). 
more specialised laboratory tests. A laboratory construct-
ed in such way would achieve and create a new produc-
tion possibility frontier through the use of both CRS and 
VRS methodologies. 
We used the ABC flowchart to show the work process in 
the laboratory. The essential stages of the process are: 
test ordering, admission of sample, sample triage, anal-
ysis, result validation, and the issuing of accurate and 
high-quality laboratory results.
Table 3. Efficiency of the virtually merged laboratory.
Organisational chart 1. Work process in the laboratory.
MODEL 1
MODEL 2
MODEL 3
x
SD
Pseudo 1
Pseudo 2
Pseudo 3
93.33
95.89
89.60
92.94
3.16
100
100
100
100
0
DMU CRS (%) VRS (%)
-1
-1
-1
93.33
95.89
89.60
92.94
3.16
RTS SE (%)
As Table 3 shows, a virtually merged laboratory (Pseudo) 
would achieve 100% VRS efficiency under all three mod-
els. However, the laboratory shows a certain degree of 
scale inefficiency when the CRS methodology is used. The 
size of the process in the consolidated laboratory would 
be too large from the standpoint of decreasing returns 
to scale (RTS). The laboratory should reduce the amount 
of inputs it consumes and maintain the level of outputs 
produced. Despite the relatively low level of inefficien-
cy identified, the surpluses in inputs used would be high, 
mainly due to the size of the merged laboratory. 
Taking the economic efficiency theory into account, we 
further defined the surpluses in inputs and deficits in 
outputs produced at the virtual laboratory. The values of 
the surpluses were defined using the DEA method, i.e. by 
predicting the optimal size of the laboratory. However, 
one should be aware that our set of analysed laboratories 
shows a certain degree of heterogeneity, which can make 
it more difficult to accurately estimate the target values 
of the input variables. A virtual laboratory could reduce 
the number of working hours by 16,686.01, labour costs by 
EUR 374,852.16, the number of analyzers by 11, the pur-
chase price of laboratory equipment by EUR 205,256.91, 
and the cost of laboratory materials and reagents by EUR 
1,265,576.49. On the output side, the laboratory could 
carry out 860,520 more basic laboratory tests and 112,344 
Source: own.
Based on knowledge of the work process in medical lab-
oratories, we suggested the process improvements in 
individual phases of laboratory work that could possibly 
result from the consolidation of laboratories (Organisa-
tional chart 1). Irrespective of the number of collected 
samples, laboratories must ensure continued presence 
of trained laboratory personnel at the sample collection 
and admission point. A reduction in the workforce could 
be accomplished by unifying sample collection as well as 
sample admission points. Different biomedical analyzers 
are required to perform different diagnostic laboratory 
tests. In the case of scale inefficient laboratories, this 
fails to achieve a sufficiently high level of analyzers utili-
sation. Despite lower analyzers utilisation, daily, monthly 
and annual maintenance of devices must be carried out, 
along with the daily testing of control material. This pre-
sents additional costs resulting from the consumption of 
laboratory materials and reagents. In our study, we ob-

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178
serve that automation can improve laboratory efficiency 
only if biomedical analyzers are fully utilised, something 
that can be achieved if laboratories are of the appropriate 
size. The merging of laboratories within a single health 
organisation would therefore lead to lower purchase pric-
es through the joint procurement of laboratory reagents 
and materials, and biomedical analyzers. The uniform use 
of diagnostic equipment ensures greater comparability of 
results, and thus directly affects the quality and reliability 
of the laboratory findings issued. The envisaged merger of 
laboratories would result in greater specialisation of work 
tasks. Added value also represents the implementation of 
special tests and research activities. 
In the case of separate laboratories, only larger laborato-
ries have their own support staff, i.e. administration and 
service personnel. Administrative tasks in smaller labora-
tories are carried out by laboratory workers. Rationalisa-
tion of the number of support staff, i.e. service personnel 
and administration, would reduce labour costs in the con-
solidation process.
4 DISCUSSION
Under the Slovenian Resolution on the National Healthcare 
Plan, laboratory activity is unconsolidated. This adversely 
affects the concentration of the laboratory tests conduct-
ed, the quality of work and, not least, the technical and 
cost-efficiency of laboratories. The DEA method enables 
us to identify best practices and to quantify the surpluses 
in the inputs used. By calculating scale efficiency, we can 
further determine whether the cause of the inefficiency 
lies in the size of the unit analysed or the results of the 
process inefficiencies. 
While a constructed, virtually merged laboratory does not 
show process inefficiency, it does show some degree of 
scale inefficiency. Similarly, studies on hospital mergers 
show the importance of determining the optimal size of 
hospitals in their merger (26, 27). In light of the findings of 
these studies, we also observe that, due to the decreas-
ing RTS, merged laboratories should reduce the resources 
used. The biggest advantage of merged laboratories is the 
reductions they bring in the cost of laboratory reagents 
and materials. Work process optimisation resulting from 
a more appropriate laboratory size would have a posi-
tive impact on achieving economies of scale. In this part, 
therefore, our study concurs with previous findings (13). 
Similarly, the results of our study show that labour costs, 
the costs of laboratory reagents and materials and the 
number of biomedical analyzers used would be reduced. 
The merged laboratory could thus reduce labour costs by 
10% and the costs of laboratory reagents and materials by 
17%. Labour force optimisation could be accomplished by 
unifying sample collection points, as well as admission and 
management staff at the laboratory. The harmonisation of 
technology used at the same location would consequently 
lead to greater utilisation of laboratory equipment and im -
prove the comparability of the laboratory tests provided.
5 CONCLUSIONS
An evaluation of the efficiency of medical laboratories 
should be in the interest of health service payers and 
health policy-makers alike. We can conclude that efficien-
cy evaluations are indispensable when planning a network 
of laboratory services, i.e. consolidation of laboratory 
services.
We note, like other researchers who have used the DEA 
method to evaluate health policy and reform (37, 38), 
that the method is methodologically suitable for evaluat-
ing the effects of health policy implementation, and is an 
appropriate tool for identifying further development and 
improvements in the field of laboratory medicine. The re-
sults of the analysis can help public healthcare institution 
managers to identify surpluses in the inputs used and the 
resulting process optimisation. 
If decision-makers want easily accessible laboratory ser-
vices, they must accept that this has a negative impact on 
the optimal performance of smaller medical laboratories 
(because their size is non-optimal). In conclusion, we must 
ask ourselves how much efficiency we are willing to sac-
rifice to ensure that health services are accessible to the 
greatest possible extent.
The main gap in the study is its inability to identify cost-ef-
ficiency using several models because public healthcare 
institutions’ records are incomplete, particularly the as-
pect that defines capital cost. An international compar -
ison of the models used to determine efficiency would 
provide additional benefits to this field of study.
CONFLICTS OF INTEREST
The authors declare that no conflicts of interest exist.
FUNDING
None.
ETHICAL APPROVAL
All data used in this study was provided by Slovenian lab-
oratories. All personal data was anonymised.

10.2478/sjph-2020-0022 Zdr Varst. 2020;59(3):172-179
179
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