Technical Efficiency of Iranian Medicalservices Insurance Organization Using Data Envelopment Analysis Approach

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1 Technical Efficiency of Iranian Medicalservices Insurance Organization Using Data Envelopment Analysis Approach Amir Ashkan Nasiripour 1, Mohammad reza Maleki 2, Mohammad Hossien Mehrolhassani 3 1 Department of Health Services Management, Science and Research Branch, Islamic Azad University, Tehran, Iran, 2 Department of Health Services Management, Tehran University of Medical Sciences, Tehran, Iran, 3 Health Services Management, Science and Research branch, Islamic Azad University, Tehran, Iran. Abstract Background: Performance evaluation is one of the most important topics in management literature. There is a variety of criteria for such an evaluation including efficiency, effectiveness, and quality assessment. Meanwhile, efficiency is a current criterion with a different method of measurement. Objectives: Iran s Medical-services Insurance Organization (IRI- MSIO) is one of the major financial health institutions nationwide which, in this paper, has been studied to model its technical efficiency. Methods and material: This study was descriptive-practical. Technical efficiency of 31 provincial offices of MSIO was determined by using Data Envelopment Analysis (DEA). The information (2010 data) was constructed through the forms designed by researchers from Information center of IRI-MSIO. The efficiency of provincial offices was surveyed by MSIO-experts and classified the provincial offices. Differences between groups of technical efficiency were evaluated with ANOVA and Kruskall-Wallis Tests. Results: Total average technical efficiency of all provincial offices of MSIO was The estimation of province-efficiency was surveyed by MSIO-experts who, with regard to environmental factors influencing the efficiency of MSIO, classified the provincial offices into four groups (Kruskall-Wallis Tests: (P<0.001). Conclusion: Input-oriented BCC model to be the optimal efficiency measurement for IRI- MSIO. Based on the impact of efficiency environmental factors (access to services and overhead refers indices), the MSIO provincial offices can be classified into four main groups. Therefore, in order to increase the efficiency of MSIO in national level it is suggested to consider this classification as an on-factor in the referral system to delivery of medical services. Key words: Medical Services Insurance Organization, Iran, Technical efficiency, Managerial efficiency, Efficiency of scale, Data envelopment analysis (DEA) Introduction The Medical-services Insurance Organization (MSIO) is one of the major financial health sectors in I. R. Iran offering, in 2009, a wide range of financial security products with a mission to protect nearly 39,965,000 Iranians in medical services[1]. This organization was founded with a governmental nature in 1995 on Iranian Health Insurance Coverage law; its workflow basically consists of four main processes: investing, insuring target population, activating contracts with healthcare providers, and purchasing the medical services needed. The MSIO holds local offices in all 31 Iranian provinces with a wide network of agencies from the private sector nationwide. Limitations in resource and facilities have ever since adversely affected the socio-economic conditions of all communities[2]; enhancing the productivity level and improving the efficiency variables are increasingly critical points of concern for any nation planning to effectively manage such challenges[3]. This topic is even more important in health sector, being the core of sustainable development and a fundamental segment of infrastructure for all other sectors. In addition to moral ethical aspects of the above mentioned issue, health promotion has also a social-econo- Journal of Society for development in new net environment in B&H 603

2 mic aspect which must be taken into consideration by policy-makers as a part of holistic sustainable development plans[4]. Act no. 88 in the 4 th Development Plan of Islamic Republic of Iran ( )[5] has emphasized the enhancement of productivity and efficiency in health sector. Social policies of Iran s 5 th Development Plan(2009) has, too, focused on implementing an integrated system of policy-making, planning, evaluating and allocating public resources for the sake of human health; to achieve higher levels of health insurance both in quantity and quality are the targets of these Plans, out of packet must be decreased by 30 percentages[6]. One can consequently observe the attempts for increasing productivity level in health sector, particularly in health insurance systems, in recent Plans of IRI. Major organizations have been so far established to measure productivity and efficiency; therefore the objective of the present paper is to measure the technical and scale efficiency of Iranian Medical-services Insurance Organization; a Data Envelopment Analysis (DEA) method was applied with an eye to these reasons. There are two general approaches to measuring technical efficiency, namely parametric and nonparametric. Data Envelopment Analysis (DEA) is the nonparametric method, and can easily handle multiple input/output cases. Moreover, in DEA application, inputs and outputs can have significantly different units of measurement without requiring any a priori tradeoffs nor any input- output prices. Given these highly desirable features of the nonparametric methods, it is not surprising that they have recently become very popular among researchers[7]. The main DEA models have been described in details by several authors[8-10] thus a detailed description is not provided here. The first DEA model was developed by Charnes et al in This model, known as the CCR model, assumes constant returns to scale (CRS)[9]. The efficiency score ( ) in the presence of multiple- input and -output factor is defined as[9]: Or mathematically as[9]:... (1b) Let the DMU j to be evaluated on any trial be designated as DMU o where o ranges over 1, 2,..., n. To measure the relative efficiency of a DMU o based on a series of n DMUs, the model is structured as a fractional programming problem as follows[10]:... (2) where n is the number of DMUs in the comparison, s the number of outputs, m the number of inputs, u r (r = 1, 2,..., s) the weighting of output y r in the comparison, v i (i = 1, 2,, m) the weighting of input x i, and y rj and x ij represent the values of the outputs and inputs y j and x i for DMU j, Eq.(2) respectively can equivalently be written as a linear programming (LP) problem as follows[10]:... (3)... (1a) In reality, the dual (envelopment) form of the DEA-LP problem is simpler to solve than (3) due to fewer constraints. Mathematically, the dual li- 604 Journal of Society for development in new net environment in B&H

3 near problem is written in vector matrix notation as follows:... (4) Where y o is the s 1 vector of the value of original outputs produced and x o is the m 1 vector of the value of original inputs used by the oth greenhouse. Y is the s n matrix of outputs and X is the m n matrix of inputs of all n units included in the sample. is a n 1 vector of weights and is a scalar with boundaries of one and zero that determines the efficiency score of each DMU, i.e., = 1 shows a technically efficient DMU; < 1 shows a technically inefficient DMU [10]. For each inefficient DMU, target input and output levels have to be prescribed. These targets are the results of respective slack values added to outputs[11]. To calculate the target values for inputs ( ), the input value (x ) is multiplied with an optimal efficiency score ( ), and then slack amounts io io (s-) are subtracted from this amount,[11]:... (5a) Similarly, in an input-oriented model, efficient output targets are calculated as [11]:... (5b) If there is no restriction on λ, Eq.(4) represents constant returns to scale[12, 13]. CRS implies that a given increase in inputs would result in a proportionate increase in outputs and the feasible region of the envelopment problem becomes a conical hull. A restriction on λ leads to no condition on the allowable returns to scale, also called variable returns to scale (VRS). Under this condition, the performance frontier line is not then restricted to pass through the origin, and an increase in inputs may not result in a proportionate increase in outputs in this case [10]. Due to convexity, the efficient DMUs form a convex hull on which all inefficient points are projected. Because the VRS is more flexible and envelops the data in a tighter way than the CRS, the score or pure TE ( VRS) is equal to or greater than the CRS or overall TE score ( CRS). The relationship can be used to measure scale efficiency (SE ) of the oth greenhouse as:... (6) Where SE = 1 implies scale efficiency (or CRS) and SE < 1 indicates scale inefficiency [10]. In Iran, likewise, researchers have used DEA for efficiency measurement of different organizations[14, 15]; it has, in some cases, been carried out in insurance companies[16-18].however, few studies have so far involved health sector and even among this minority, most were carried out in hospitals concluding that data envelopment analysis should be applied rather than parametric methods [19-21].In a research on efficiency of health care among selected Islamic countries, Hosseini-nasab and Baskha (2010) evaluated the performance of 24 countries regarding their application of financial resources for improving health through data envelopment analysis method[22].in health-insurance sectors only one article was found with a topic of "Performance Evaluation in Tehran province branch of social security by merge of fuzzy data envelopment and balance score card (FDEA & BSC)" the indicators of which being designed by BSC and the performance of their organizations evaluated through FDEA[23]. In world level, there were several studies regarding performance of hospitals[24-26]in health insurance, however, only one study was found in which Wu et al. (2007) simulated the production and investment functions in Canadian health and life insurance companies by Data Envelopment Analysis; they presented a new model of data envelopment analysis which simultaneously measures the production and investment performances of Canadian health insurance companies[27]. Journal of Society for development in new net environment in B&H 605

4 Methods This study was descriptive-practical in nature and cross-sectional regarding the method of conduction. In the first step, DEA-model of MSIO was designed through holding several interviews with experts prior to doing system analysis; the information (2010 data) was then constructed through the forms designed by researchers from Information center of IRI-MSIO. In the next step, DEA-model was run in Deap 2 software and the estimation of province-efficiency was surveyed by MSIOexperts who, with regard to environmental factors influencing the efficiency of MSIO, classified the provinces into four groups. Finally, differences between groups of technical efficiency were evaluated by SPSS software (ver. 17) through parametric ANOVA and nonparametric Kruskall-Wallis Tests. To give a brief description of the DEA-model of MSIO, a non-parametric Data Envelopment Analysis (DEA) method is employed with variable return-to-scale (VRS) assumption to measure input-oriented technical efficiency of MSIO. MSIO intrinsically bears four main processes: investing, insuring target population, activating contracts with health care providers, and purchasing the medical services. As local offices of MSIO in Iranian provinces have no investing activity, this factor is eliminated in our model. Therefore, three variables were selected as input of BCC-model (the number of manpower, overhead costs, building and land area) as well as another three as output (namely, proportion of insured population from the total target population, proportion of contracted institutions from the total activity institutions, proportion of once-referring from the medical expenses)(figure 1). Figure 1. MSIO system analysis and inputs-output definitions for BCC model Results Total average technical efficiency of all provincial offices of MSIO was 0.513;the mean of managerial efficiency was 0.610; the scale efficiency was Relying on the results of Data Envelopment Analysis model with variable return to scale (VRS) assumption, there was % 16 capacities to promote efficiency in all provincial offices of MSIO, using the same amount of inputs without any increase in fees. 4 of the provincial offices (13%) were fully efficient. Efficiency of 12 of the offices (39%) were between 1 and 0.5 while 15 of them (8%) were less than 0.5; therefore, 87 percent (27) of province office of MSIO working inefficiently. Results also showed that 4 of the MSIO local (provincial) offices (13%) have constant returns to scale (CRS), 13 (42%) have descending returns to scale (DRS) and 14 of them (45%) have increasing returns to scale(irs).in 2008, the lowest rate of technical efficiency was reported to be that of Tehran provinceoffice(0.048) which has increasing returns to scale (IRS) (Table 1). Environmental factors are the variables which were not normal in delivery of service nor were under the control of management. In this study, following the interviews conducted with the MSIO experts, provincial nature indicated with access to services and the amount of overhead (i.e., the number of patients with insurance coverage who were referred to other provinces to receive services) was considered as environmental factor before expert classifying the provincial offices into four main groups (according to the experts views);according to Table 1, there was an increase in efficiency in provinces with: smaller size, less population, limited access to levels of public service accommodations, and smaller amounts of overhead, while, on the other hand, provinces with: larger size, more population, more access to public service accommodations, and larger amounts of overhead have lower efficiency. To examine the difference between these groups of offices, we performed a series of parametric (ANOVA and Scheffe) tests and non-parametric (Kruskal-Wallis and Mann-Whitney) ones. The results of ANOVA test supported the findings that the difference between the mentioned groups was statistically significant with a 95% confidence 606 Journal of Society for development in new net environment in B&H

5 Table 1. Efficiency measurement & Ranking of MSIO provincial offices by DEA-VRS model Name of province holding an MSIO Efficiency Rank office Type of returns to scale Technical Managerial Scale 1 ILAM crs 2 S. KHORASSAN crs 3 SIMNAN crs 4 N. KHORASSAN crs 5 BUSHEHR drs 6 KOHKILUYE & BOYERAHMAD drs 7 QOM irs 8 HORMOZGAN drs 9 CHAHAR MAHAL& BAKHTIARI drs 10 ZANJAN irs 11 QAZVIN irs 12 YAZD drs 13 MARKAZI irs 14 ARDABIL drs 15 KARAJ irs 16 GOLISTAN drs 17 KORDISTAN irs 18 LORISTAN drs 19 HAMADAN irs 20 KERMANSHAH irs 21 SISTAN & BALOOCHISTAN drs 22 KERMAN irs 23 W. AZARBAYJAN drs 24 GUILAN irs 25 RAZAVI KHORASSAN drs 26 MAZANDARAN drs 27 KHUZISTAN drs 28 ISFAHAN irs 29 E. AZARBAYJAN irs 30 FARS irs 31 TEHRAN irs Total mean level(p<0.001) and the results of Kruskal-Wallis test showed significant difference between the groups (P<0.001). So it can be concluded that the efficiency of provincial offices classified by the level of their access to services as well as the amount of overhead refers as environmental-factors was corrected. Result of Mann-Whitney test between pair groups is demonstrated in Table 2. Conclusion To adopt an appropriate model for measuring the efficiency of MSIO, we firstly measured the efficiency of provincial offices by running both output and input-oriented CCR and BCC models. Then, comparison of the findings indicated different results from CCR and BCC models, which means the assumption of constant return to scale (CRS) to measure efficiency of MSIO isn't applied. Since the inputs of provincial offices of MSIO are more controllable than their outputs, we concluded that the input-oriented BCC model to be the optimal efficiency measurement for IRI- MSIO. Also, based on the results of DEA input-oriented model, the total efficiency of MSIO was relatively low; to justify this, one reason could be that Journal of Society for development in new net environment in B&H 607

6 Table 2. Result of Mann-Whitney Test to calcify provinces s of provinces office Name of provinces ILAM, S. KHORASSAN, N. KHORASSAN, KOHKILUYE & BOYERAHMAD SIMNAN, MARKAZI, QAZVIN, BUSHEHR, KARAJ, ARDABIL, LORISTAN, HORMOZGAN, CHAHAR MAHAL& BAKHTIARI, ZANJAN, KORDISTAN, SISTAN & BALOOCHISTAN GOLISTAN, HAMADAN, W. AZARBAYJAN, QOM, KHUZISTAN, KERMAN, MAZANDARAN, GUILAN, KERMANSHAH, YAZD E. AZARBAYJAN, RAZAVI KHORASSAN, ISFAHAN, FARS, TEHRAN Mean of efficiency group Standard Deviation Result of Mann-Whitney Test short-term function was not positioned properly in relation with the long-term one, and the results of short-term function were not in appropriate situation. Structure and information systems of MSIO ought to be reformed in order to obtain an economically relevant production function moving to services associated with a small amount spent on resources. Therefore, it can be concluded that the low level of efficiency is due to a defect in managerial structure or a systemic failure. In this regard, structural reorganization and organizational re-engineering seem to be strategic changes necessary. As the provincial offices of MSIO all bear a surplus-capacity and in order to decrease this capacity, a comprehensive plan ought to be developed as a change-package with a capability to conserve all required aspects. For instance, more than 50% of the total costs of MSIO is related to manpower costs which is by itself a large fixed cost. Therefore, based on results of Data Envelopment Analysis, adjusting the surplus of manpower performs a pivotal role in reducing the costs of this organization; needless to say, attention to all other aspects of the organization should be devoted. Based on the impact of efficiency environmental factors (access to services and overhead refers indices), the MSIO provincial offices can be classified into four main groups. Therefore, in order to increase the efficiency of MSIO in national level it is suggested to consider the above-mentioned classification as an on-factor in the referral system to delivery of medical services. Acknowledgments The authors gratefully acknowledge the participation of Administrators and personnel of the Iran s Medical-services Insurance Organization (MSIO) and the support provided by Science & Research branch of Islamic Azad University of Tehran. References 1. MSIO, Iran s Medical-services Insurance Organization (MSIO) performance- report: 2009 (in Farsi), 1, Editor. 2010, (MSIO),Tehran. 2. Mensura Kudumovic, A.K., Economic analysis of health. HealthMED, (2): p Coelli, T., D.S. Prasada Rao, and G.E. Battese, An Introduction to Efficiency and Productivity Analysis. 2001, Boston: Kluwer Academic. 4. Marandi, A., Health in Islamic Republic of Iran Unicef, WHO. 608 Journal of Society for development in new net environment in B&H

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