Iranian Red Crescent Medical Journal
The Efficiency and Budgeting of Public Hospitals: Case Study of Iran
1
1, *
2
Hasan Yusefzadeh , Hossein Ghaderi , Rafat Bagherzade , Mohsen Barouni
3
1 School of Health Management and Information Sciences, Department of Management and Health Economics, Tehran University of Medical Sciences,
Tehran, IR Iran
2 School of Health Management and Information Sciences, Department of Foreign Lamguages, Tehran University of Medical Sciences, Tehran, IR Iran
3 Research Center for Health Services Management, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, IR Iran
*Corresponding author: Hossein Ghaderi, School of Health Management and Information Sciences, Department of Management and Health Economics,
Tehran University of Medical Sciences, Tehran, IR Iran. Tel: +98-2188635606, Fax: +98-2188635606, E-mail: hoss_ghaderi@yahoo.com.
A B ST R A C T
Background: Hospitals are the most costly and important components of any health care system, so it is important to know their economic
values, pay attention to their efficiency and consider factors affecting them.
Objective: The aim of this study was to assess the technical scale and economic efficiency of hospitals in the West Azerbaijan province of Iran,
for which Data Envelopment Analysis (DEA) was used to propose a model for operational budgeting.
Materials and Methods: This study was a descriptive-analysis that was conducted in 2009 and had three inputs and two outputs. Deap2, 1
software was used for data analysis. Slack and radial movements and surplus of inputs were calculated for selected hospitals. Finally, a model
was proposed for performance-based budgeting of hospitals and health sectors using the DEA technique.
Results: The average scores of technical efficiency, pure technical efficiency (managerial efficiency) and scale efficiency of hospitals were
0.584, 0.782 and 0.771, respectively. In other words the capacity of efficiency promotion in hospitals without any increase in costs and with
the same amount of inputs was about 41.5%. Only four hospitals among all hospitals had the maximum level of technical efficiency. Moreover,
surplus production factors were evident in these hospitals.
Conclusions: Reduction of surplus production factors through comprehensive planning based on the results of the Data Envelopment
Analysis can play a major role in cost reduction of hospitals and health sectors. In hospitals with a technical efficiency score of less than one,
the original and projected values of inputs were different; resulting in a surplus. Hence, these hospitals should reduce their values of inputs
to achieve maximum efficiency and optimal performance. The results of this method was applied to hospitals a benchmark for making
decisions about resource allocation; linking budgets to performance results; and controlling and improving hospitals performance.
Keywords: Efficiency; Hospital; Budgets
Copyright © 2013, Iranian Red Crescent Medical Journal; Published by Kowsar Corp.
1. Background
In their economic efforts, human beings have always focused on maximum results using minimal facilities and
resources. This is called, ‘achieving a better performance’.
Efficiency is a comprehensive concept whose increase has
always been considered by politicians and economists to
improve quality of life, welfare, peace and human prosperity. Some people believe that survival and persistence
of certain political and economic systems depend on efficiency and productivity (1). Economic and social development of health sectors and the distribution of facilities
are critical. Inefficiency and ineffectiveness of services,
not only reduces the quality of life, but also hinders improvement and productivity in other economic sectors
and results in an increase of inequality, social injustice
Article type: Research Article; Received: 05 Mar 2012; Revised: 09 Jan 2013; Accepted: 10 Apr 2013; Epub: 05 May 2013; Ppub: 05 May 2013
Implication for health policy/practice/research/medical education:
This study was a descriptive-analytical research that was conducted in 2009 with three inputs and two outputs. Deap 2, 1 software
was used for data analysis. Slack and radial movements and surplus of inputs were calculated for selected hospitals. Finally a model
was proposed for performance-based budgeting in hospitals and health sectors using the DEA technique.
Please cite this paper as:
Yusefzadeh H, Ghaderi H, Bagherzade R, Barouni M, The Efficiency and Budgeting of Public Hospitals: Case of Iran. Iran Red Cres Med
J.2013;15(5):393-9. DOI: 10.5812/ircmj.4742
Copyright © 2013, Iranian Red Crescent Medical Journal; Published by Kowsar Corp.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Yusefzadeh H et al.
and political dilemmas. Health sector is the most important part of the service sector and serves as an indicator of
development and social welfare, thus, consideration of its
economy is essential. Hospitals as the most expensive and
important component of health care systems require special attention, so much that in developing countries more
than 70% of health resources are allocated to hospital services (2). An increase in public expectation of economic
welfare has led to an increase in demand for health services. Therefore, regarding scarce resources and facilities,
it is crucial to make the best use of the existing facilities
to reduce the gap between supply and demand. Efficiency
is the most important and common mechanism for evaluating and measuring the performance of enterprises
such as hospitals, so in the past few decades, researchers
in different fields of social sciences, particularly economics and management have focused on the performance of
different economic sectors, firms and economic entities
at micro levels through measuring and estimating their
efficiency (3). Productivity and efficiency are important resources for economic development; therefore, they must
be reviewed and analyzed in the health sector. Calculation
of technical efficiency and recognition of factors affecting
hospital efficiency are complementary measures for quality and quantity improvements. Elimination of factors involved in hospital inefficiency can, without adding agents,
increase efficiency, enhance service delivery and help hospital administrators make more realistic, efficient and
better decisions (4). The Data Envelopment Analysis (DEA)
method can be an appropriate model for the operational
budgeting of governmental departments, such as schools,
banks, hospitals and so on, for which information on prices rarely exists or are incomplete (5). Operational budgeting process (performance based budgeting) is estimated
and calculated based on operational classification of organizations' current costs and in terms of functions and
activities in form of workload of each organizational unit
and the measurement of the costs of each activity for efficient production of goods or services. The most important
feature of operational budgeting is that it shows the relationship between the allocated funds of each program and
the results of its implementation. Moreover, operational
budgeting adds saving and effectiveness factors to aspects
of traditional budgeting. This type of budgeting identifies
and lists all direct or indirect activities in any program
and offers accurate estimation of the costs of each activity. It also seeks to link performance indicators to resource
allocation based on achieving obvious and measurable results. Establishing logical and technical links between performance indicators and resource allocation is necessary
in this method of budgeting (6). Calculating efficiency and
quantifying performance allows managers to oversee the
trend of changes, identify potential problems and take
timely corrective actions. Hospital systems as one of the
most important and influential sectors in the society have
a critical role in health promotion and because of the in-
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Efficiency and Budgeting of Hospitals
creased demands and limited resources it is necessary to
accurately calculate efficiency and productivity. Studies
conducted by Gannon in Ireland (2005), Hofler and Folland in the United States (1995), Lina in Finland (1997), Parkin and Hollingsworth in Scotland (1995), Goodarzi (2007),
Saber Mahani (2009), Zohreh Kazemi (2009) and Mohsen
Barouni (2012) in Iran, all emphasized the use of the DEA
method for assessing hospital efficiency. In this study the
DEA technique was implemented to measure efficiency
and estimate optimal use of resources in hospitals (7, 8).
2. Objective
The aim of this study was to assess the technical scale
and economic efficiency of hospitals in the West Azerbaijan province of Iran and Data Envelopment Analysis (DEA)
was used to propose a model for operational budgeting.
3. Materials and Methods
In their paper, Charnes, Cooper and Rhodes, operation
research specialists, (1978), measured productivity and
efficiency via Data Envelopment Analysis (DEA) which
was based on a series of optimization and a linear programming known as the non-parametric method. In this
method, an efficient frontier curve is made of a series of
points determined by linear programming (9). Farrell,
for the first time, showed how to get the same production
function through geometry. He stated that if each point in
figure 1 represents the use of production factors X1 and X2
to produce the output Y in different enterprises, the connection of the points closer to the origin and axes creates
a convex function with no points under it; this curve is
called efficient isoquant production function. This coating
surface encompasses Pareto optimum points (Pareto Efficiency) and a series of efficient units in production. If the
production of an output Y requires more than two production factors X1 and X2, the geometrical drawing of isoquant
production function curve would be very difficult; indeed
DEA was produced to overcome such problems (10).
X2/y
S
S'
X1/y
0
Figure 1. Same Production Function of Farrell
Iran Red Crescent Med J. 2013;15(5)
Yusefzadeh H et al.
Efficiency and Budgeting of Hospitals
In cases where firms require more than two production
factors to produce outputs, each decision making unit
is considered as a point in space. The dimension of each
point are determined by the number of production factors and its coordinates are specified by production factor level. Furthermore, using a linear programming, a
decision making unit is selected as an investigation unit
which is compared with other decision making units
(other space points). Therefore, it is possible to assess the
efficiency of points off or on this curve, which are called
set of efficient points (11). To determine the points, two
assumptions of fixed and variable returns to scale are
used to either maximize the objective functions (output),
considering certain inputs, or minimize the inputs using
its duality, that is the given outputs. The linear programming method, after a series of optimization, specifies
whether the desired decision making unit is located on
the efficiency line or otherwise. Thereby, efficient and
inefficient units are distinguished. DEA in the isoquant
production function estimation does not require a particular default shape. This method is used to compare the
efficiency of a firm relative to others (12). In this study, the
efficiency of selected hospitals was estimated through a
non-parametric approach of input orientated of DEA and
variable returns to scale assumption. Linear programming is defined by:
Minλ,OS,IS (M1′ ∙ OS+K1′ ∙ IS)
St:-yt+Yλ-OS = 0‚
Өxt-Xλ-OS = 0
N1′∙λ ≤ 0, λ ≥ 0, OS ≥ 0 ‚ IS ≥ 0
In the above equation, the first constraint shows that
the product surplus for each firm would be zero, if -yt +
Yλ equals zero. The second limitation indicates that the
production factors surplus will be zero, if the term Өxt - Xλ
is zero. The third constraint expresses variable returns to
scale. λis a ×N1 vector of fixed numbers indicate weights
of the reference set. IS and OS refer to input and output
slacks. DEA model with variable returns to scale (VRS) assumption can distinguish between scale efficiency and
pure efficiency. In other words, technical efficiency can be
analyzed from pure efficiency and scale efficiency through
solving linear programming models with two assumptions of constant and variable returns to scale, that is:
Value of technical efficiency (with CRS assumption) =
amount of technical efficiency (with VRS assumption) ×
scale efficiency
Studies have shown that operational budgeting system
is a changing and evolving concept that cannot be examined in simplified terms and non-dynamic relationships.
Budgeting process is an activity affected by political options and numerous environmental variables, that is, the
allocation of limited resources to meet needs and priorities. Performance information can only be one of the
factors that make up infrastructure decisions. Therefore,
hospitals budget is divided into inevitable and efficiency
Iran Red Crescent Med J. 2013;15(5)
budget (13). The inevitable budget of hospitals is determined according to indicators such as the number of official staff, active beds, regional balanced indices (as area
deprivation index), historical trends and so on; therefore,
it is necessary to distribute a part of the overall budget
of health sectors to hospitals based on efficiency criteria. Obviously, the share of each case of inevitable and
efficiency budget can vary in line with the policy of the
executive institute and the suggestions made by managers. If the total distributable budget among hospitals is
shown with I, the inevitable budget with A and the efficiency budget of institute with B; and if w and v indicate
their weight coefficients; then, the distributing budget is
equal to:
I = Av + BW
Where w and v are arbitrary coefficients and are annually determined in accordance with management suggestion and current situation and different values between
zero and one can be selected. Thus:
V+W = 1
This was a descriptive-analytical (cross-sectional and
retrospective) study conducted in 2009. The research
population consisted of 23 hospitals affiliated to Urmia
University of Medical Sciences including Imam Khomeini, Motahhari, Taleghani and Psychiatry of Urmia; Khatam of Salmas; Qamar bany Hashem; Madani and Ghra
zyaaldyn of Khoy; Beheshti of Chaldoran; Fajr, Qods of
Maku; shohada of Showt; Imam Khomeini of Poldasht;
Imam Khomeini of Naqadeh; Imam Khomeini of Mahabad; Abbasi and Hazrat Fatima of Miandoab; Rasy of
Shahyndez; Shohada of Takab; Qolypor of Bukan; Imam
Khomeini of Piranshahr and Nabi akram of Oshnavieh.
Input variables included the number of active beds, doctors and other personnel and output variables encompassed out patients admission and occupied day beds in
studied hospitals. The data were collected using available
documents in hospitals and were analyzed using DEA
and Deap2,1 software. In this study, both slack and radial
movements of inputs were estimated; and in addition to
determining the efficiency of the selected hospitals, the
surplus or excessive use of inputs was calculated as well.
Finally, a new model was designed with variable returns
to scale (VRS) assumption for operational budgeting of
hospitals and health sectors. In order to observe ethical
considerations, the results are shown with relevant numbers and if necessary, the information for each hospital
will be presented to their managers.
4. Results
In this study, the DEA model (based on the minimization
method of production factors and with the assumption
of variable returns to scale (VRS)) was used with two outputs and three inputs. Deap2,1 software results are given
in the following tables. According to Table 1, it is possible
to evaluate hospital performance based on the technical
395
Yusefzadeh H et al.
Efficiency and Budgeting of Hospitals
efficiency index and compare this between hospitals. The
average technical efficiency score of the hospitals calculated with DEA method was 0.584, which indicated that
some hospitals did not work effectively and their capacity for efficiency promotion without any increase in costs
and with the same amount of inputs was about 41.5 %.
Table 1. Rating of Studied Hospitals Based On Technical Efficiency Using the DEA Model and VRS
Hospital
Efficiency
Technological Managerial Scale
9
1
1
1
Returns to
Scale
CRS
19
1
1
1
CRS
21
1
1
1
CRS
22
1
1
1
CRS
4
0.944
1
0.944
IRS
20
0.871
0.997
0.874
IRS
18
0.733
0.768
0.955
DRS
7
0.618
1
0.618
DRS
14
0.612
1
0.612
DRS
15
0.585
0.879
0.666
DRS
23
0.582
0.597
0.975
IRS
8
0.576
0.588
0.98
DRS
13
0.505
1
0.505
DRS
2
0.413
1
0.413
DRS
16
0.413
0.559
0.739
DRS
10
0.398
0.542
0.734
DRS
17
0.368
0.386
0.954
IRS
12
0.36
0.742
0.486
DRS
6
0.359
1
0.359
DRS
1
0.3
1
0.3
DRS
5
0.299
0.357
0.837
DRS
3
0.269
0.315
0.853
DRS
11
0.238
0.256
0.932
DRS
Mean
0.584
0.782
0.771
Hospitals 9, 19, 21 and 22 were the most efficient hospitals (with index 1) while hospital 11 was the least efficient
one (0.238); in fact 17.3 % of hospitals were fully efficient.
Also in this part of the research, return to scale was measured which shows the rate of increase in production
provided that all other resources are equally increased.
It also revealed three cases: 1) constant return to scale
(CRS) in 17.3 % of hospitals, where equal increase in all
production factors led to the same amount of increase
in production, 2) increasing returns to scale (IRS) in 17.3
% of hospitals, where equal increase in all production
factors resulted in more production and 3) Decreasing
396
returns to scale (DRS) in 65.2 % of hospitals, where equal
increase in all production factors led to less production.
For inefficient hospitals, DEA method identified some
production factors and products which indicated a decrease in the use of production factors or an increase in
the amount of products (minimization is used in health
sectors). According to the results obtained from the
Deap2,1 software, most surplus of doctor and bed inputs
were related to hospital 11 and the highest rate surplus
of other staff was related to hospital 3. Tables 2 and 3
show the weight rate of all reference hospitals (hospital
peers) for non-efficient hospitals and indicate that the
use of production factors in each reference hospital is
less than a non-efficient hospital. For example, reference
hospitals for a non-efficient hospital ( 8 ) are hospitals (
14 ) ( 9 ) whose weights are 0.631 and 0.369, respectively.
The optimal values of inputs are determined with the
application of these coefficients and hospital ( 8 ) can
achieve maximum efficiency. Moreover, information related to reference hospitals can be used for a better assessment of inefficient hospitals.
Table 2. Reference Hospitals (hospital peers)
Hospital
Efficiency
Technological Managerial Scale
Returns to
Scale
1
1
2
2
3
7
9
4
9
21
5
9
13
22
6
7
2
13
1
7
7
8
19
9
9
10
7
13
9
19
9
14
11
7
9
12
14
13
9
13
13
14
14
15
13
14
9
16
7
9
17
21
9
9
18
19
19
19
20
21
21
21
22
22
23
21
22
9
22
9
22
Iran Red Crescent Med J. 2013;15(5)
Yusefzadeh H et al.
Efficiency and Budgeting of Hospitals
Table 3. Hospital peer weights
Hospital
Efficiency
Technological
Managerial Scale
Returns
to Scale
1
1
2
1
3
0.203
0.599
4
0.939
0.061
5
0.627
0.159
0.215
6
0.226
0.103
0.46
0.211
7
1
8
0.369
9
1
10
0.327
0.072
0.023
0.579
11
0.078
0.922
12
0.694
0.158
0.148
13
1
14
1
15
0.067
0.123
0.811
16
0.458
0.542
17
0.234
0.565
18
0.26
0.74
19
1
20
0.301
21
1
22
1
23
0.72
0.198
5. Discussion
0.631
0.201
0.177
0.522
0.148
0.132
In this study, Koopmans definition was used for calculating efficiency. In other words, slack and radial
movements were both estimated and finally surplus or
excessive use of inputs was calculated. The results are
summarized in Table 4.
Table 4. Average Amount of Over the Need Utilization to Separate Input Using the DEA-VRS Model
Input/average
Physician Other personnel Active Bed
Original value
26
115
116
Projected value
17
86
85
Slack movement
9
29
31
According to the results, the most surplus of input was
related to bed input and the lowest to physician input. As
was expressed distributing budget is equal to: I = Av + BW,
where BW shows efficiency budget of an institute. If C is
all allocated funds to hospitals based on efficiency criteria
and C = BW, hospital I will take the CI currency based on the
proposed allocated pattern; that is the sum of allocated
budget to independent sectors which is equal to the total
budget allocated to state institutes or universities:
Iran Red Crescent Med J. 2013;15(5)
C =∑n=23ᵢ=1 ci
The following steps should be taken to determine each
hospital's share of efficiency budget: if the sum of technical efficiency is E and EI represents technical efficiency
index of hospital I:
(I = 1,………,11)
E = ∑n=23ᵢ=1 ei
CI share of each hospital of efficiency budget is equal to:
Ci = C × ei/E
It was found that the evaluated hospitals did not work efficiently and the capacity for efficiency promotion without any increase in costs and with the same amount of
inputs was about 41.5 %. Thus, the hospitals had surplus
capacity. Most excess use of resources or additional inputs
was related to active bed input. The lowest technical efficiency was related to hospital 11 with a technical efficiency
of 0.238 and a decreased return to scale. The average technical efficiency score of the hospitals was 0.584. In other
words, these hospitals can provide the same current
level of outputs using 58.4 % of their resources. The mean
score for pure technical efficiency (managerial efficiency)
of hospitals was 0.782, that is without increasing inputs
and only with good and wise management and the effort
of employees; the efficiency can be increased up to 21.8%.
The average scale efficiency score for hospitals was 0.771,
so hospitals should act efficiently to have increasing return to scale and increase their services, because with the
assumption of constant factors for production, output
will exceed inputs. Therefore, long-term marginal cost
and thereinafter long-term total cost will decrease and
there will be an economic justification for the increased
services. In studies conducted by Goodarzi et al. in hospitals affiliated to the Tehran University of Medical Sciences,
Saber Mahani in Kerman University of Medical Sciences
hospitals and Zohreh Kazemi in hospitals of South Khorasan province, the calculated mean scores for technical
efficiency were 0.972, 0.912 and 0.886, respectively, which
were more than those of Urmia hospitals. Barouni et al.
performed the Provincial human development index, a
guide for resource allocation using the DEA method. The
results showed the national mean for the HDI in 2001
was 0.717 while it increased to 0.747 in 2009, showing an
improvement of 4.2%. Except for one province, all others
had an improved human development index; although
the level of improvement was very small in some provinces. Low ranked provinces, such as Sistan & Baluchistan
and Kurdistan remained at the bottom in 2009. However
some provinces such as Bushehr with developing oil industries, or those purposively benefited from national oil
income showed good growth. In some provinces, such as
Hormozgan, out-migration of manpower to its neighboring province, Bushehr, was associated with a decrease of
the provincial income level. The number of efficient provinces increased from 5 to 13 (43% of all provinces) in 2009.
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Yusefzadeh H et al.
According to these findings, in hospitals with maximum
technical efficiency (with index 1), the original and projected values of inputs were the same and input surplus
was zero. On the other hand, in hospitals with a technical
efficiency less than one, the original and projected values
of inputs were different and they had surplus of input.
Therefore, they should reduce their surplus from original
values to reach optimal performance. Hospitals with an
efficiency less than one, for example, hospital 8, had surplus in physician, other personnel and active bed inputs,
and should reduce 72.8 % of their original values of doctor
input, 45.9 % of other personnel input and 41.2 % of active
bed input. In fact, they should reduce doctor input from
25 to 6, other staff from 71 to 38 and active bed from 70 to
41 and eliminate 19 doctors, 33 other personnel and 29 active beds that have no roles in production. Since the output (number of patients) is not controlled by hospitals, it
is not quite practical to use output maximization, but it
is possible to find information on optimized output and
take measures in competing with other hospitals to increase service quality, enhance customer satisfaction, attract patients and improve service volume. Considering
the surplus capacity of production factors in hospitals,
it seems that reduction of these factors should be done
through comprehensive planning, taking all aspects into
consideration. More than half of the health staff work in
hospitals which consume a major part of the fixed costs
of the health sector. Therefore, proper planning on how
to use the resources and remove surplus manpower
based on the DEA will have a significant role in reducing costs of hospitals and health sectors (while in some
hospitals, despite the surplus capacity, new staff are still
recruited). One of the major limitations of this study was
the exclusion of the severity of diseases and quality of
care provided to patients because there were no data related to cases across hospitals in the country. As a result,
cases that had significant influence on hospitals performance were not included in the study. Hence, the studied
indices could not determine the complexity of activities
or show hospital performance in real terms; for example,
some hospitals may treat some easy and daily cases and
refer complicated ones to other hospitals (15). Therefore,
it is recommended to conduct studies to achieve and
define case mix indices (the combination of different
patients treated in a particular hospital) in Iran. Operational budgeting system is a system for producing and
exchanging current and future functional information
(real and expected results); on the other hand it is a system of purchasing the expected results with government
funds. The DEA technique can be used as a framework for
the inclusion of performance indicators in the process of
operational budgeting and can be implemented in hospitals. Performance indicators should include comparative criteria to determine hospitals status with reference
to their competitors, partners and what is considered
by experts. Comparison is very effective in determining
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Efficiency and Budgeting of Hospitals
goals and performance motivation and recognizing organizational excellences, and thus it is the only way to
determine the adequacy of development (16). The DEA
technique can be used to link budget to performance and
compare each hospital's performance with the others. It
is also possible to assess the existing and former status
of hospitals. Considering the deficiencies of this model
and the lack of statistical tests to confirm the findings, it
is suggested to use the results of parametric approaches
in modeling non-parametric models, to consider inputs,
which are not located in the third area of production and
are meaningful in parametric methods.
Acknowledgements
Hereby, the authors would like to thank the staff and
management of hospitals affiliated to Uromieh University of Medical Sciences for their cooperation, without
which this study could not be done. Also it is essential
to thank the school of health management and information sciences, Tehran University of medical sciences for
helping in electronic sources and Ph.D. site.
Authors’ Contribution
All authors contributed equally.
Financial Disclosure
There is no financial disclosure.
Funding Support
There is no funding or supports.
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