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Measuring Efficiency of Reformed Public Hospitals in Saudi Arabia: An Application of Data Envelopment Analysis | OMICS International
ISSN: 2162-6359
International Journal of Economics & Management Sciences
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Measuring Efficiency of Reformed Public Hospitals in Saudi Arabia: An Application of Data Envelopment Analysis

Mohamed Said Abou El-Seoud*

University of Bahrain, Kingdom of Bahrain College of Business Administration, Economics and Finance Department

*Corresponding Author:
Mohamed Said Abou El-Seoud
University of Bahrain
Kingdom of Bahrain College of Business Administration
Economics and Finance Department
E-mail: [email protected]

Visit for more related articles at International Journal of Economics & Management Sciences

Abstract

The study aims at investigating the efficiency of public hospitals that have been reformed to operate under private sector management of through the full operating system in KSA. The study applied Data Envelopment Analysis(DEA) on the sample of reformed hospitals based on the ministry of health data for 2011. The study found that although the Saudi government achieved many positives results such as: attracting the excellence human resources, rising of the level of government health services, attracting national capital to the health sector, and reducing the administrative burden of the government sector, there are (60%) of the study sample had not achieved relative efficiency due to different reasons. The study concludes that health reform is complex, and requires policy consistency, stability, and government institutions with strong implementation capacity and government credibility. Public hospitals reform requires close coordination between policy design and implementation, and a matching of the scale and pace of reform to the capacity for implementation. Over time, through iterative evaluation, policy review and adjustment, KSA can identify what works best, and move to more consistent policies, standards of service delivery, and equity of access across its hospital system.

Keywords

Economics; efficiency; public hospitals, DEA, KSA.

INTRODUCTION

Since the late 1980s, many upper-income countries, transition economies, and developing countries have implemented organizational and management reforms of public hospitals, for example, UK, Germany, Singapore and Brazil. Many countries and local governments have undertaken pilot reforms in selected hospitals (often major tertiary teaching hospitals) for example, in Malaysia, and Thailand. Public hospital reform has been advocated in every region of the world because of common problems: inefficiency, waste, user dissatisfaction, brain drain of personnel to the private sector or emigration, failure to reach the poor, fraud, and corruption. These problems are often attributed to their nature as public hospitals, which are typically characterized as lacking incentives for good performance, penalties for poor performance, and managerial freedom for hospitals wishing to change.

Health car in Saudi Arabia has increased and improved significantly during recent decade. Especially in the areas of equity and accessibility of services, as well as the movement towards universal coverage. Thirty years later, the 5-year development plans were introduced by the government to improve all sectors of the nation, including health care system. Since then, substantial improvements in health care have been achieved in Saudi Arabia. (Walston et.al, 2008). Currently the Ministry of health (MOH) is the major government provider and financer of health care services in Saudi Arabia, with a total of 244 hospitals (33277 beds) and 2037 primary health care (PHC) centers. See table (1).

Years No. of hospitals No. of beds Gov. budget(SR. millions) MOH budget %
1990/1991 116 26886 245 12 4.9
1995/1996 176 26955 280 16.7 6
2000/2001 189 27826 335 19.7 5.9
2005/2006 209 28430 380 22.8 6
2010/2011 230 30214 4500 25.2 5.6
2011/2012 244 33277 475 29.52 6.2

Table (1): Evolution of MOH's hospitals and its share of total government spending

Before the year 1399 hospitals run and managed by MOH, which known " self-operating system", where MOH was responsible for hiring all Saudis and foreigners hospital staff according to the civil service system. But there are a number of factors led to the transformation from self-operating system to another system based on private sector participation in operating public hospitals, which are:(Al-Husaini,2006)

- MOH established new five large and modern high-tech hospitals during the years of National Development Plan II (1395-1400) in Jeddah, Jizan, Hofuf, Medina and Al Khobar, but it had not available qualified human resources capable to run these facilities, therefore, MOH held bilateral cooperation agreements between Saudi government and other countries, where the new hospitals had been running by specialized staff from these countries.

- MOH desire to carry out planning, organization, supervision and controlling functions, which eventually would lead to integration in providing high quality and efficient health services and ensuring its continuity.

- Saudi government desire to increase the opportunities for the private sector to manage, operate and maintenance projects that set up by the government in the framework of the free market orientation.

- The success of the first experiment to run some military hospitals affiliated to the Ministry of Defense and Aviation by the private sector for nearly 30 years ago, and it followed with running King Faisal specialist hospital and the research center by specialized international company. These experiments have achieved tangible success where medical center archived rapid advancement in the level of health services to cope with the technical development in the field of health care and providing the best possible quality services.

- MOH's desire to cope with modern methods that applied in many countries in North America, Europe and East Asian countries in the field of management, operation and maintenance hospitals which confirmed success of the private institutions in carrying out these tasks in reducing the cost and improving the level of service performance.

The previous factors led Saudi government to shift from "self-operating system" to new operating system by private companies as a method of privatization. Saudi government followed gradient progress policy which began running hospitals through bilateral cooperation agreements between Saudi government and the governments of other countries, then it followed by partial operation system and comprehensive operation system, and ended up to full operating system. (Ateeq,2002)

The current study focuses on the "full operating system" that followed by the Kingdom after the emergence of cons in other phases of operating systems. Saudi government started to adopt this system in 1400, when AMI company was responsible for full operation of King Fahd Hospital in Baha. In 1411, the MOH applied this system in many hospitals in KSA such as: King Abdul Aziz University in Jeddah , King Saud Hospital in Bisha, and Al-Amal hospitals group in Riyadh, Jeddah and Dammam. According to the MOH statistics , there are currently about 37 hospital affiliated with the MOH operated according to full operating system .

The study aims at evaluating and measuring the efficiency of a sample of public hospitals that have been managed by private sector in order to identify which hospitals are more efficient(According to providing the greatest quantity of services or outputs) by using available inputs, and which hospitals are not. In addition, determining the amount of inputs which should be reduced (or output which should to be raised) for less efficient hospitals in order to achieve the required efficiency.

The study adopt Data Envelopment Analysis (DEA)which is one of the benchmarking analysis technique that widely used for the efficiency measurement of hospitals. It is popular in evaluating hospital efficiency because it is applicable to the multiple input-output that is essential for the nature of a health care system. (Linna et al., 2006)

The reminder of the study is organized as follows: Section two reviews the relevant empirical literature on measuring efficiency of hospitals using DEA techniques. Section three illustrates data, and methodology. Section four describes and discusses the empirical results. Section five is conclusion and recommendations.

2. LITERATURE REVIEW

Efficiency has become one of the most attractive work areas of healthcare management literature. Studies on hospital efficiency mostly focus on the issue of maximum gain with limited resources (Sorkis and Talloru, 2002). The interest on hospital efficiency has increased because of the desire to control the increasing costs. Accordingly, hospital resources and their processes became critical and, as a result, the number of studies has increased in recent years. (Bakar et al., 2010).

Modeling efficiency measurement is a non-parametric way was introduced first by Farrell (1957) including that measurement of price and technical efficiencies and the derivation of the efficient production function.

Literature review of DEA studies on hospital efficiency shows that there many studies applied in both developed and developing countries. The first DEA model developed by Charnes et al. (1978), named the CCR model, was based on the assumption of Constant Return to Scale (CRS) in order to measure the efficiency of decision making units (DMU). Later, Banker et al. (1984) enhanced the CCR model and developed the BCC model using the Variable Return to Scale (VRS). They used empirical data from a sample of North Carolina hospitals to compare efficiency to characterizations obtained from DEA of econometric models. The DEA model was able to identify inefficiencies and uncover return to scale possibilities in individual hospitals that were not evident in the trans-log model. In addition, they reported that DEA’s efficiency estimates appeared to be more closely related to the degree of capacity utilization than were the trans-log estimates.

Vivian (1990) used DEA and he found that public non-profit hospitals were more efficient than private non-profit hospitals. Dittman et.al,(1991), attempted to demonstrate how DEA can be useful to hospital administrators and health care planners. They used actual data collected by the American hospital association through its monitored data service. They found that the efficiency with which a hospital operates may well depend on the local or regional labor market, the competition among health care providers in that market, and the demographics of the service area. They concluded that inefficiency score and the resource conservation potentials were based on a unit's so-called contraction path, where all of the controllable inputs were required to be reduced by the same factor.

Ozcan, Luke and Haksever (1992) used DEA to show that government and non-profit hospitals were indistinguishable from one another regarding their percentages of inefficient scores. Kooreman (1994) analyzed the technical efficiency of Dutch nursing homes with respect to the use of labor. White and Ozcan (1996) studied the effect of church-ownership on hospital efficiency, using a sample of California hospitals, and they found that religious hospitals were more efficient than secular (public) nonprofit hospitals.

Harris, Ozgen and Ozcan (2000) and Ferrier and Valdmanis (2004) applied DEA methods to analyze the effects of merger on hospital efficiency. Harris, Ozgen and Ozcan (2000) found scale efficiency to be the dominant source of efficiency improvements, but they did not find improvements in technical efficiency one year after a merger. Ferrier and Valdmanis (2004) used methods very similar to those used in this study to evaluate the effects of hospital mergers. They compared efficiency scores one year before, the year of and one year after the merger using matched pairs of hospitals. They found no significant change in technical efficiency in the year after a hospital merger. However, disruptions associated with consolidations likely take more than a single year to resolve themselves and improvements in technical efficiency can only then emerge.

Dino Rizzi and Vincenzo Rebba(2006) applied DEA method to measure the efficiency of 85 (public and private) hospitals in Veneto, a Northern region of Italy. They found that the imposition of a lower bound on the virtual weight of acute care discharges weighted by case-mix (in order to consider policy-maker objectives) reduced average hospital efficiency. Moreover, they showed that, in many cases, low efficiency scores were attributable to external factors, which were not fully controlled by the hospital management; especially for public hospitals low total efficiency scores could be mainly explained by past policy decisions makers on the size of the hospitals or their role within the regional health care service. Finally, non-profit private hospitals exhibited a higher total inefficiency while both non-profit and for-profit hospitals are characterized by higher levels of scale inefficiency than public ones.

Consequently, Nayar and Ozcan (2008) concluded that DEA is constructive technique for health care managers to investigate opportunities in accordance to efficiency improvement.

Al-Shayea (2011) applied DEA for measuring the relative efficiencies of units delivering similar services. This technique is applied to study the performance and efficiency of King Khalid University hospital departments. The results showed that only two departments out of nine have 100% efficiencies throughout the 12 months period. M. Sahin and Bulent (2011), investigated the efficiencies of hospitals in Turkey with respect to their ownerships for the years (2001-2006) by adopting DEA, they found that the average efficiencies of state hospitals remarkably increased while the average efficiencies of private hospitals decreased especially after the starting of reforms in the state-owned hospitals. Barnum, et al.(2011), developed efficiency indicators valid for non-substitutable variables by using a sample of 87 community hospitals, they compared the new measures' efficiency estimates with those of conventional DEA measures. DEA substantially overestimated the hospitals' efficiency on the average, and reported that many inefficient hospitals to be efficient. Further, it greatly overestimated the efficiency of some hospitals but only slightly overestimated the efficiency of others, thus making any comparisons among hospitals questionable.

It is clear from previous studies, they tried to measure the impact of ownership on hospital efficiency using DEA , but that there is rarity of studies that tried to measure the impact of privatization of management on the efficiency of public hospitals in general and Saudi Arabia in particular. This reflects the significant of the current study that attempt to measure the efficiency of the reformed Saudi government hospitals.

3. DATA AND METHODOLOGY

3.1 Data

To investigate efficiency in the reformed public hospitals in Saudi, we select a sample of (20) hospitals that operating under fully operational system, we obtain health data for the year 2011 from MOH's Statistical Yearbook. As shown in table (2)

Hospitals Inputs Output
No. of doctors No. of nurses No. of beds No. of Allied Health visits reviewers No.of inpatients patients benefiting
from radiography
laboratory tests
Al-Mojama 169 390 262 216 93066 11524 35081 73331
Al-Ola 100 251 133 132 36680 7851 23860 45495
King Fahd in Jeddah 515 904 612 320 270528 6776 73223 12038
King Khalid in Hail 664 165 939 861 27487 73732 13232 36581
King Fahd in Hofuf 163 336 150 186 60646 1614 39799 71287
Hira in Mecca 168 371 202 176 84244 9091 29122 665636
Hafer Al- Batin 480 788 439 369 12157 2655 88778 127643
King Khalid in Tabuk 782 166 951 703 52282 5787 200933 17823
Al Amal in Dammam 156 322 200 177 94334 14315 42426 82547
Prince Salmanin
Riyadh
533 977 910 681 36834 55327 13547 414305
King Fahd in Al-Baha 170 322 215 184 109379 11383 39070 114589
King Saud in Bisha 169 390 262 216 93066 11524 35081 73384
King Khalid in Najran 156 322 200 177 94334 14315 42426 82547
King Fahd in Medina 693 148 910 101 30069 55603 13212 215908
King Abdul Azizin
Jeddah
386 569 895 244 257002 6246 73223 10594
king Fahd in Jizan 230 145 310 101 30069 5560 13212 21590
Ohed in Medina 497 105 742 545 19886 27874 10666 18529
Al-Amal in
Jeddah
214 601 350 315 11197 49527 67332 133165
Al-Qurayyat 163 356 150 186 60646 16144 39799 71282
Al Amal in Riyadh 436 106 597 449 191196 24181 82759 125587

Table (2): Inputs and output of selected hospitals

3.2 Methodology

Data Envelopment Analysis (DEA) is an empirically based methodology that eliminates the need for some of the assumptions and limitations of traditional efficiency measurement approaches. The basic DEA model as introduced by Farrell in 1957 and later developed by Charnes, Cooper and Rhodes (CCR Model) uses an oriented radial measure of efficiency, which identifies a point on the boundary with the same mix of inputs (input orientation) or outputs (output orientation) of that of the observed unit.( Kontodimopoulos and Niakas, 2005)

DEA is a technique to measure relative efficiency of a set of decision-making units (DMUs) having similar multiple inputs to produce similar multiple outputs. The relative efficiency of a DMU is defined as the ratio of the sum of its weighted outputs, to the sum of its weighted inputs.

The objectives are to identify units that are relatively inefficient and setting targets for them based on examining the operational practices of the units classified as efficient. The underlying concept of DEA is based on Pareto optimality(Charnes et. al., 1994). A DMU is considered relatively efficient if there is no other DMU or a combination of DMUs which can produce at least the same amount of all outputs with less of one input and not more of any other input(Cooper et.al., 2003) It computes the comparative ratio of outputs to inputs for each unit, with the score expressed as 0–1 or 0–100%. A DMU with a score less than 100% is inefficient compared to other units. It is used to identify best practices and is increasingly becoming a popular and practical management tool.

In the first stage, DEA assesses efficiency by estimating a frontier based on input or output orientation. Then, each DMU is assigned an efficiency score by comparing the output and input ratio of the DMU on the efficient frontier. Mathematically, technical efficiency for each DMU is computed as follows: (Lavado and Cabanda, 2009)

Consider a system under evaluation, consisting of n DMUs. The inputs and outputs of every DMU are all nonnegative and every DMU has at least one positive input and one positive output, i.e., x ≥ 0, x ≠ 0 and y ≥ 0, y≠ 0. Then, the economic efficiency of DMU is defined as follows:

In this case, the DMUs can be easily compared. However, since the input costs and output prices are not always precisely available, DEA models are generally utilized for this purpose. It can be solved by one of two linear programming formulations:

The first formulation is (CCR) that maximizes the outputs that can be obtained and constrains the sum of the inputs to be unity.(AL-Shammari, 1999).

Subject to:

(2)

y r j: output r from unit j , X r j :input I to unit j and u and v are scalar values chosen for each production unit such that the efficiencies of each unit are maximized and no efficiencies are greater than one. (Charnes et.al, 1994)

• The second formulation is developed by Banker, Charnes and Cooper ( BCC model) that minimizes the inputs needed and constrains the sum of the weighted output at unity. The adopted DEA model represents the dual of the first linear programming formulation. The linear programming dual is expressed as the following: (AL-Shammari, 1999)

Min μ Z0

Subject to:

This model estimates the technical efficiency depending on the size of existing operations in the DMU to provide services to the beneficiaries at the time of the measurement. The model also allows the possibility of having constant or increasing or decreasing returns to scale of inefficient units resulting from the change in the amount of inputs in order to get efficiency.(Cooper & Joe Zhu, 2003)

The study will adopt the two models for measuring the efficiency of health services provided by the reformed hospitals in various regions of the Kingdom. Accordingly, number of specialists, number of nurses, number of allied health and number of beds are used as input variables; while the number of patients visit outpatient, number of patients admissions to hospital, number of laboratory tests, and number of beneficiaries of radiological imaging are used as output variables.

To make sure getting accurate results from DEA, we take into account the balance between the number of inputs and outputs and the number of hospitals involved in the evaluation, where the total number of inputs and outputs should not exceed the number of DUM. (Charnes, Cooper & Siford: 1994)

4. EMPIRICAL RESULTS

The results of measuring the relative efficiency in the selected sample of hospitals, using both (BBC) and (CCR) indicate that the number of hospitals that achieved general relative efficiency is (8) hospitals out of (20) which represents (40%). These hospitals are: King Fahd in Hofuf, King Fahd in Al-Baha, King Khalid in Tabuk, Al-Ola hospital, King Fahd in Medina , King Abdul Aziz university in Jeddah, Ohed hospital in Medina and Al Amal hospital in Riyadh. While there are (12) hospitals have not achieved general relative efficiency, as it shown on Figure(1). General efficiency index will be measured to identify the main reasons of inefficacy in the (12) hospitals. Is due to inefficiency of administrative process or the inability of the administrative system to overcome the external factors or environmental variables or due to both?

economics-management-sciences-productive

Figure 1: productive efficiency of public hospitals Index

General efficiency index divided into two sub-indices as follows:

1- Internal technical index or (internal operation efficiently): It reflects the level of efficiency of hospitals under assessment in using available resources (inputs) to provide the best services to the beneficiaries (outputs). This index was derived from the application of (BBC) model.

2- External technical index: It reflects the hospital level of efficiency to overcome the environmental factors that affect hospital. (such as: hospital location, mediation and nepotism, the nature of the civil presence in the hospital, the demographics of the city, the proportion of residents in the city,…. etc.). These factors have negative impact on the level of provided services (outputs).This index was calculated by dividing the general efficiency index by the efficiency of operations index.

The table (3) shows the general relative efficiency of the hospitals sample. The average relative efficiency of all hospitals is (84.6%). This means that, in order to achieve efficiency in these hospitals, they either have to provide the same level of output by using (84.6%) of the current inputs (number of doctors, nurses, medical assistance groups), or to reduce the inputs by (15.4%) to provide the current levels of services. While if the goal is to maximize output and maintaining the same amount of inputs, the selected hospitals should increase its services by (16.8%).

Hospitals G.T.E.index Hospitals
ranking
I.T.E.index E.T.E.index Causes of
inefficiency
Al-Mojama 71 15 74.1 89.2 B
Al-Ola 100 1 100 100 -
KingFahd    in
Jeddah
84 9 100 97.2 B
King Khalidin
Hail
84 9 100 91.5 B
King Fahd in Hofuf 100 1 100 100 -
Hira in Mecca 65 19 66.4 92.7 A&B
Hafer Al- Batin 80 12 99.6 99.2 B
KingKhalidin
Tabuk
100 1 100 100 -
Al    Amal   in
Dammam
82 12 82.7 99.1 B
PrinceSalman in
Riyadh
78 14 92.2 91.5 A&B
King Fahd in Al-
Baha
100 1 100 100 -
King Saud in Bisha 82 11 80.3 89.4 A&B
KingKhalidin Najran 70 17 74.1 98.7 B
King  Fahd in
Medina
100 1 100 100 -
King Abdul Aziz in
Jeddah
100 1 100 100 -
king Fahd in Jizan 71 15 81.8 97.2 B
Ohed in Medina 100 1 100 100 -
Al-Amal in Jeddah 60 20 66.4 92.6 A&B
Al-Qurayyat 65 18 72.4 90.5 A&B
Al Amal in Riyadh 100 1 100 100 -
Average 84.6 - 89.5 96.4 A&B

Table (3): Public hospitals' general, internal, and external technical efficiency indices and The causes of inefficiency for the year 1433H

In order to identify the main reasons for why these hospitals could not achieve efficiency, we calculate the external productive efficiency index , which reflects efficiency level of hospitals managers to overcome on the environmental factors.

Table (3) shows that the hospitals average general technical efficiency index reached (84.6%), while the average internal technical efficiency is (89.5%), and the average external technical efficiency is (96.4%). This means that these hospitals are inefficient because of administrative weakness to overcome the external environmental factors rather than inability to manage the internal operations.

Public hospitals that could not achieve the relative efficiency are two groups:

• Inefficient hospitals due to administrative weakness to overcome the environmental or external factors only, which are: Al-Mojama hospital , King Khalid hospital in Hail, hospital Hafer Al- Batin,,Al Amal Hospital in Dammam, King Khalid hospital in Najran, King Fahd hospital in Jizan, and King Fahd hospital in Jeddah.

• Inefficient hospitals due to administrative weakness to overcome both internal and external factors, and the weakness of external operations, which are: King Saud Hospital Bisha, hospital Hira in Mecca,Prince Salman hospital in Riyadh, Al-Amal in Jeddah, and hospital Qurayyat.

One of the main advantages of DEA (in addition to select inefficient DMU) is to specify the amounts inefficiency in these units and suggest suitable quantities in order to achieve the relative efficiency, Whether the goal is to maximize outputs or reduce inputs. The analysis also provides information about referred DUMs for every inefficient hospitals in the same area until it reaches the general efficiency level.

Table (4) shows the required or appropriate improvement in the King Fahd hospital in Jeddah, until it reaches the level of general efficiency, by adopting one of the following options:

Inputs and Outputs A- Reduce Inputs Target B- Maximizing Output Target
Actual
Value
Target
values
Optimization
required
% Target
values
Optimization
required
%
No. of beds 612 895 -174 -28.4 600 -12 -2
No.of doctors 515 386 -129 -25 515 0 0
No.of nurses 904 569 -335 -40 775 129 -14.3
No.of Allied Health 320 244 -76 -20 320 0 0
No.of visits reviewers 270528 257002 13526 5 347628 77100 28.5
No.of inpatients 6776 6246 530 10 9622 2846 42
No.of laboratory tests 12038 10594 1444 12 13843 1805 51
No.of patients benefiting from
radiography
73223 73223 0 0 97093 23870 32.6

Table (4): Optimization required in King Fahd hospital in Jeddah to reach efficiency

First option (increasing output): King Fahd Hospital should use its current inputs, and in order to reach general efficiency (compared with referred hospital: King Abdul Aziz University in Jeddah) it should increase reception of patients in different outpatient clinics by (28.5%) , increase the number of inpatients by (42%), increase the number of laboratory tests in its centers by (51%), and increase reception of patients benefiting from radiography at its center by (32.6%), and it can increase outputs by the lower level of inputs such as beds and nurses.

Second option( Reduce inputs): King Fahd Hospital could use its current inputs to achieve general efficiency, through decreasing the number of beds by (174) bed or (-28.4%), decreasing the number of doctors by (129) Doctor or (- 25%), reducing the number of nurses by (335) or (-40%), and also reducing the number of medical assistance by (76) or (-20%). It can also increasing the number of visitors, the number of inpatients auditors and laboratory tests by (5%), (10%), (12%), respectively.

5. CONCLUSION

The research question addressed in this paper is do full operating system of reformed public hospitals increase its technical efficiency? We used data from the MOH's Statistical Yearbook for the year 2011 to calculate and analyze technical efficiency by adopting Data Envelopment Analysis. The study found that (60%) of selected hospitals have low efficiency due to either internal factors or external factors or both. Therefore, the study suggested two options to raise the level of efficiency either increasing output or reducing input, and it applied these two options on one of inefficient hospitals compared with efficient hospital. The study concludes that health reform is complex, and requires policy consistency, stability, and government institutions with strong implementation capacity and government credibility. Public hospitals reform requires close coordination between policy design and implementation, and a matching of the scale and pace of reform to the capacity for implementation. Over time, through iterative evaluation, policy review and adjustment, KSA can identify what works best, and move to more consistent policies, standards of service delivery, and equity of access across its hospital system.

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