Akademik Sosyal Araştırmalar Dergisi, Yıl: 4, Sayı: 34, Kasım 2016, s

Akademik Sosyal Araştırmalar Dergisi, Yıl: 4, Sayı: 34, Kasım 2016, s. 372-382 Yayın Geliş Tarihi / Article Arrival Date Yayınlanma Tarihi / Publication Date 08.11.2016 30.11.2016 Ph.D. Candidate Ahmet KAR Hacettepe Üniversitesi, Sağlık Kuruları Yönetimi ahmetkar67@hotmail.com Dr. Fatih ŞANTAŞ Hacettepe University, Faculty of Economics & Administrative Science, Department of Health Administration, Ankara fatih.santas@hacettepe.edu.tr Ph.D. Candidate Gülcan KAHRAMAN Hacettepe Üniversitesi, Sağlık Yönetimi gulcan.kahraman@hacettepe.edu.tr Ph.D. Candidate Yetkin GÜRVARDAR TECHNICAL EFFICIENCY OF GYNECOLOGY AND OBSTETRICS HOSPITALS IN TURKEY: MEASUREMENT BY DATA ENVELOPMENT ANALYSIS Abstract This study aims to evaluate the technical efficiency of gynecology and obstetrics hospitals, located in public and private sectors in Turkey. Research data belongs to 2014 and is obtained from Department of Statistics, Analysis and Information Systems of Ministry of Health. scope of the study constitutes of totally 40 gynecology and obstetrics hospitals, including 11 private and 29 public hospitals. Analyzes were conducted by using 3 input (physicians, nurses and beds) and 2 output (the number of outpatient and inpatients) variables. Data envelopment analysis was used for the data analysis. In the analysis, the average number of outpatient examination was found as three hundred thousand and the number of inpatients was twenty thousand. In addition, there were 72 nurses, 47 physicians and 181 registered number of beds per Gynecology and Obstetrics Hospitals in

Turkey. study states that 11 of 40 hospitals (6 public and 5 private hospitals) are efficient and 29 of 40 hospitals are found to be inefficient. According to the super efficiency scores, İstanbul Yakacık Obstetrics and Gynecology Hospital had the highest scores (CRR:1.55/BCC:1.70). This research shows that health care resources were used inefficiently. se findings are valid for both public and private hospitals. Keywords: Gynecology and Obstetrics Hospitals, Technical Efficiency, Data Envelopment Analysis TÜRKİYE DEKİ KADIN HASTALIKLARI VE DOĞUM HASTANELERİNİN TEKNİK ETKİNLİĞİ: VERİ ZARFLAMA ANALİZİ İLE ÖLÇÜM Öz Bu çalışmanın temel amacı Türkiye de kamu ve özel sektörde yer alan kadın doğum hastanelerinin teknik etkinliklerini değerlendirmektir. Araştırmanın verileri, 2014 yılına aittir ve TC Sağlık Bakanlığı İstatistik, Analiz ve Bilgi Sistemleri Daire Başkanlığından temin edilmiştir. Araştırmanın kapsamını 11 i özel 29 u devlet olmak üzere toplam 40 kadın doğum hastanesi oluşturmaktadır. Çalışmada 3 girdi (hekim, hemşire ve yatak sayısı) 2 çıktı (ayaktan muayene ve yatan hasta sayısı) olmak üzere toplam 5 değişken kullanılarak analizler yapılmıştır. Verilerin analizinde Veri Zarflama Analizi kullanılmıştır. Analiz sonucunda, Türkiye de kadın doğum hastanesi başına ortalama ayaktan muayene sayısı üç yüz bin ve yatan hasta sayısı yirmi bin olarak belirlenmiştir. Ek olarak, hemşire sayısı 72, hekim sayısı 47 ve tescilli yatak sayısı 181 olarak bulunmuştur. 40 hastaneden 11 inin (6 sı kamu, 5 i özel hastane) verimli olduğu, 29 unun ise verimsiz olduğu saptanmıştır. Süper etkinlik skoruna göre İstanbul Yakacık Kadın ve Doğum Hastanesi en yüksek (CRR:1,55/BCC:1,70) skora sahiptir. Bu araştırma sağlık kurumlarının kaynaklarının verimli kullanılmadığını göstermektedir. Bu durum, verimsizliğin hem kamu hem de özel hastaneler için geçerli olduğunu göstermektedir. Anahtar kelimeler: Kadın Doğum Hastaneleri, Teknik Verimlik, Veri Zarflama Analizi 373 1. INTRODUCTION Over the last decade in Turkey, recent developments in hospital sector has forced hospital management to produce better quality of service with less cost. Transfer of hospitals in Social Insurance Institution to the Ministry of Health, DRG as reimbursement methods and the increasing number of private hospitals are among these developments. In addition, more complex and dynamic political, legal and economic environments affect the service quality in health sector more than other sectors (De Simone, 2014; Koroglu & Yardan, 2016). refore, optimal usage of the limited resources of hospitals, i.e. the realization of the production efficiently, has become very important (Jacobs, 2001).

As well as there are various definitions of efficiency, it is widely defined as ''to obtain the maximum output with minimum input''. Whether or not pursuing profit, hospital managements are expected to ensure the efficient operations of hospitals. Many tools are used for determining whether hospitals are operated efficiently or not. Especially ratio analysis, regression analysis and data envelopment analysis are used for efficiency measurement of hospitals. Ratio analysis is a simple method that requires less data. Regression analysis is a further analysis and reveals more realistic results in contrast to ratio analysis. However, due to the difficulty in determining the efficiency function, only regression analysis is not sufficient for health care sector (Sherman, 1984). Data envelopment analysis (DEA) is a tool which is used for measuring the efficiency of hospitals, physicians and various units such as dialysis center etc. and developed by Charnes, Cooper and Rhodes (Charnes et al.,1978). Technical and allocation efficiency of organizations can be determined by DEA (Nayar & Ozcan, 2008). DEA method determines the best observations, that reveal maximum output by using the minimum input combinations in decision making units with similar production (Sahin, 2008). most important feature of the method is to determine the level and source of inefficiency in decision making units (Charnes et al., 1995). selection of input and output variables in DEA varies depending on the purpose of the research, availability of data and the nature of the decision units (Wagner & Shimshak, 2006). re are many national and international studies for measuring the efficiency of hospitals by data envelopment analysis. se studies have analyzed relative efficiency of input and output variables in terms of ownership structures, size and functions of health care organizations and other classifications (Bal & Bilge, 2010; Cakmak et al., 2009; Sahin, 2008; Kirigia et al., 2002; Gruca & Nath, 2001; Al-Shammari, 1999; Valdmanis, 1990; Grosskopf & Valdmanis, 1987). Moreover, different departments within a single organization can be selected as decision-making units and analyzed in terms of efficiency in some studies. 374 Al-Shayea (2001) has calculated the relative efficiency of departments by using 12-month data of a university hospital and has found that only two of 9 departments are efficient. Two input and three output variables are used in the study. Kakeman et al. (2016a) has studied the technical efficiency of public, private and social security hospitals in Tehran. study has discussed the number of beds, physician, nurse and other staff as inputs; the number of surgery, the average length of stay in hospital and the average inpatient as outputs. Mehrtak et al. has examined hospital performance measurement by Pabon Lasso Model and data envelopment analysis together; it was achieved similar results in both models. Data of 18 general hospitals in East Azerbaijan was used in the analysis of the study. study has discussed the number of beds currently used, physician, nurse and other staff as input variables; the number of surgeries, the number of patients discharged from the hospital and bed occupation rate as output variables. Min et al (2016) has evaluated the technical efficiency of nursing care by data envelopment analysis and have used 2267 nursing homes as a decision-making unit. study has examined 6 different variables related to labor as inputs; the quality and quantity of the services are considered as output variables. Katharaki (2006) has used data envelopment analysis in the study of measuring the effectiveness of tele-medicine applications in Obstetrics and Gynecology Services in Greece. study has carried out with data of 32 Obstetrics and Gynecology Hospital, located in rural

and urban areas of Greece. results have demonstrated that the average number of hospital beds in the scope of research need to be reduced by 12.1%. results have also revealed that the number of female obstetrician need to be reduced by 26.5%. In the study of Lai (2013), evaluating the efficiency of gynecology and obstetrics clinics in Taiwan, data of National Health Insurance Database for 2008 was analyzed by data envelopment analysis. Examination fee, the cost of medication and dosing days were taken as input variables; the number of patients treated was used as the output variable. re are several studies examining the efficiency of hospitals in Turkey by data envelopment analysis. Sahin (2008) has studied the technical efficiency of 352 general public hospitals belonging to Ministry of Health. 12% of hospitals according to the results of constant returns to scale (CRS) and 23% of hospitals according to the results of variable returns to scale (VRS) have been found efficient. Cakmak et al. (2009) has examined the efficiency of gynecology and obstetrics hospitals belonging to Ministry of Health. study has showed that a third (1/3) of those hospitals were found as efficient and two thirds (2/3) were operated inefficiently. In another study measuring the efficiency of teaching and research hospitals (9), 13 hospitals were found to be efficient, but 22 were inefficient. Various studies show the efficiency of health care organizations by data envelopment analysis. This study aims to contribute to the literature by measuring the technical efficiency of obstetrics and gynecology hospitals in the public and private sector in Turkey. In the study, it was chosen gynecology and obstetrics hospitals because of generally standard treatment practices and having similar outputs in these hospitals. Thus, it is easier to compare the hospitals with each other in contrast to other types of hospitals. 375 2. METHOD main objective of this study is to compare the technical efficiency of gynecology and obstetrics hospitals in public and private hospitals with the data of 2014 in Turkey. scope of the study includes totally 30 gynecology and obstetrics hospitals, consisting of 11 private and 29 public hospitals. In the data envelopment analysis, it is important for decision-making units to have similar input and output characteristics with each other. In addition, due to lack of studies evaluating the relative efficiencies of both private and public-owned gynecology and obstetrics hospitals, these hospitals were preferred in the sope of research. analysis was performed by using totally 5 variables, including 3 inputs and 2 outputs. Input and output variables in the study are given in Table 1. Table 1. Input and Output Variables Used in Research Variables Definition Input Variables total number of physicians number of specialist physicians and practitioners working in hospitals total number of nurses number of nurses working in hospitals total number of registered beds number of actual beds in hospitals which are readily available for use Output Variables number of outpatient examinations number of patients examined in outpatient clinics of hospitals within 2014 number of inpatients number of inpatients in hospital within

2014 data used in the study is obtained from the Department of Statistics, Analysis and Information Systems of Ministry of Health by written permission. Efficiency Measurement System (EMS) 1.3 and DEA Solver Learning Version 2.0 were used for the data envelopment analysis. dataset was fistly prepared through MS Excel and then transferred to the package for analyzing. Input-oriented and output-oriented analysis were performed separately by DEA Solver Software Package according to the model of Charnes, Cooper and Rhodes (CCR) and the model of Banker, Charnes and Cooper (BCC). At the same time, improvements, requiring to be made in the inputs and outputs, were calculated in order to make inefficient decision-making units become efficient. Super efficiency scores of decision making units were calculated with both input-oriented and output-oriented CCR ve BCC methods by EMS 1.3. Scale efficiency of decision-making units were calculated by percentage of CCR score to BCC score. 3. RESULTS Table 2 shows the statistics of input and output variables. While average 299839 patients were taken for outpatient services in gynecology and obstetrics hospitals across Turkey, the average number of inpatients was 19597 in 2014. inputs to achieve these outputs was including average 72 nurses, 47 physicians and 181 beds per decision making unit. Table 2. 2014 Statistics of Input and Output Variables Used in DEA number of outpatient examinations number of inpatients number of nurses number of physicians number of registered beds Mean 299839.10 19597.43 72.83 47.18 181.43 Median 273142.50 17370.00 57.00 39.00 155.00 Standard Deviation 219653.80 14468.61 63.14 30.93 137.39 376 Table 3 shows the results of data envelopment analysis for technical efficiency of totally 40 gynecology and obstetrics hospitals in Turkey. results demonstrate that 11 hospitals are efficient and remaining 29 hospitals are inefficient. 5 of 11 decision making units belongs to private sector and 6 of them belongs to public sector. Analysis has been carried out under two main headings as input-oriented and output-oriented. results are obtained from resuts of constant returns to scale (CRS) and variable returns to scale (VRS); scale efficiencies are calculated. Hospitals, whose efficiency scores are found as 1, are considered as efficient. lower the score, efficiency value decrease. super efficiency scores of hospitals in the scope of study are presented in Table 4. While hospitals, that have super efficiency scores above the value of 1, are regarded as efficient in the input-oriented approach, hospitals below the value of 1 are accepted as efficient in the output-oriented approach. In this way, efficient decision making units are examined in turn. "Big" statements, located in opposite the decision making units, represent extremely high value. Table 2 shows that efficient hospitals, that have received 1 as efficiency score, are İstanbul Yakacık, Gaziantep Cengiz Gökçek and and Batman. However, it does not offer convenient information to sort among these three hospitals. When examined the super efficiency scores in

Table 4, the scores of three hospitals are 1.55, 1.33 and 1.05 respectively. In this case, it is possible to identify which one is more efficient from this three hospitals. Table 3. Technical Efficiency Scores of Gynecology and Obstetrics Hospitals in Turkey Input-Oriented Output-Oriented CCR BCC CCR/BCC CCR BCC CCR/BCC Adana 0.85 1.00 0.85 0.85 1.00 0.85 Afyonkarahisar Zübeyde Hanım 1.00 1.00 1.00 1.00 1.00 1.00 Ankara Dr. Sami Ulus 0.49 0.65 0.76 0.49 0.85 0.58 Ankara Dr. Zekai Tahir Burak 0.43 0.58 0.74 0.43 0.81 0.54 Ankara Etlik Zübeyde Hanım 0.70 1.00 0.70 0.70 1.00 0.70 Aydın 0.75 0.79 0.95 0.75 0.86 0.87 Batman 1.00 1.00 1.00 1.00 1.00 1.00 Bingöl 0.92 0.96 0.96 0.92 0.95 0.96 Bursa Dörtçelik 0.96 1.00 0.96 0.96 1.00 0.96 Bursa Zübeyde Hanım 0.51 0.52 0.98 0.51 0.51 0.99 Diyarbakır 0.93 1.00 0.93 0.93 1.00 0.93 Erzurum Nenehatun 0.62 0.64 0.97 0.62 0.62 1.00 Gaziantep Cengiz Gökçek 1.00 1.00 1.00 1.00 1.00 1.00 Giresun 0.67 0.81 0.83 0.67 0.74 0.91 Hatay 1.00 1.00 1.00 1.00 1.00 1.00 Isparta 0.95 0.97 0.98 0.95 0.97 0.99 İstanbul Esenler 0.34 0.46 0.74 0.34 0.34 0.99 İstanbul Süleymaniye 0.77 0.78 0.98 0.77 0.80 0.97 İstanbul Yakacık 1.00 1.00 1.00 1.00 1.00 1.00 İstanbul Zeynep Kamil 0.59 0.69 0.85 0.59 0.84 0.70 İzmir Buca 0.78 0.82 0.95 0.78 0.91 0.86 Konya Dr.Faruk Sükan 0.70 0.77 0.90 0.70 0.84 0.83 Mardin 0.97 0.97 0.99 0.97 0.97 0.99 Mersin 0.93 1.00 0,93 0.93 1.00 0.93 Private Can 0.01 1.00 0.01 0.01 1.00 0.01 Private Diyar 1.00 1.00 1.00 1.00 1.00 1.00 Private Hrs Ankara 1.00 1.00 1.00 1.00 1.00 1.00 Private Hüma 0.84 0.88 0.95 0.84 0.86 0.98 Private İstanbul 0.66 0.66 0.99 0.66 0.66 1.00 Private Maya 1.00 1.00 1.00 1.00 1.00 1.00 Private Meltem 0.58 0.68 0.84 0.58 0.58 0.99 Private Mozaik 1.00 1.00 1.00 1.00 1.00 1.00 Private Tarsus 0.51 0.86 0.60 0.51 0.55 0.93 377

Private Telek 0.78 1.00 0.78 0.78 1.00 0.78 Private Vitale 1.00 1.00 1.00 1.00 1.00 1.00 Samsun 0.94 0.99 0.95 0.94 0.99 0.95 Siirt 0.62 0.74 0.84 0.62 0.67 0.92 Şanlıurfa 1.00 1.00 1.00 1.00 1.00 1.00 Van İpekyolu 0.58 0.59 0.98 0.58 0.63 0.92 Zonguldak 0.83 0.87 0.95 0.83 0.86 0.97 Table 4. Super Efficiency Scores of Gynecology and Obstetrics Hospitals in Turkey Input-Oriented Output-Oriented Decision Making Unit CCR BCC CCR/BCC CCR BCC CCR/BCC Adana 0.85 0.58 1.46 1.18 1.24 0.95 Afyonkarahisar Zübeyde 1.12 big big 0.90 0.92 0.98 Ankara Dr.Sami Ulus 0.49 1.13 0.44 2.03 0.87 2.32 Ankara Dr.Zekai Tahir Burak 0.43 0.65 0.66 2.31 1.18 1.97 Ankara Etlik Zübeyde Hanım 0.70 1.04 0.67 1.43 0.98 1.46 Aydın 0.75 0.79 0.95 1.34 1.16 1.15 Batman 1.05 1.05 1.00 0.95 0.95 1.00 Bingöl 0.92 0.96 0.96 1.09 1.05 1.04 Bursa Dörtçelik 0.96 1.02 0.94 1.04 0.98 1.06 Bursa Zübeyde Hanım 0.51 0.52 0.98 1.97 1.95 1.01 Diyarbakır 0.93 big big 1.08 0.71 1.53 Erzurum Nenehatun 0.62 0.64 0.97 1.62 1.61 1.00 Gaziantep Cengiz Gökçek 1.33 1.64 0.81 0.75 0.65 1.16 Giresun 0.67 0.81 0.83 1.49 1.35 1.10 Hatay 1.07 1.07 1.00 0.93 0.93 1.00 Isparta 0.95 0.97 0.98 1.05 1.04 1.01 İstanbul Esenler 0.34 0.46 0.74 2.93 2.91 1.01 İstanbul Süleymaniye 0.77 0.78 0.98 1.30 1.25 1.04 İstanbul Yakacık 1.55 1.70 0.91 0.64 0.64 1.00 İstanbul Zeynep Kamil 0.59 0.69 0.85 1.70 1.19 1.43 İzmir Buca 0.78 0.82 0.95 1.28 1.10 1.17 Konya Dr. Faruk Sükan 0.70 0.77 0.90 1.44 1.19 1.21 Mardin 0.97 0.97 0.99 1.04 1.03 1.01 Mersin 0.93 big big 1.07 0.84 1.27 Private Can 0.01 2.06 0.00 187.54 big Big Private Diyar 1.21 1.48 0.82 0.82 0.41 2.01 Private Hrs Ankara big big big 0.00 big Big Private Hüma 0.84 0.88 0.95 1.20 1.17 1.02 Private İstanbul 0.66 0.66 0.99 1.53 1.52 1.00 Private Maya 1.28 1.34 0.96 0.78 0.48 1.61 Private Meltem 0.58 0.68 0.84 1.74 1.72 1.01 Private Mozaik 1.03 1.21 0.85 0.97 0.85 1.14 Private Tarsus 0.51 0.86 0.60 1.94 1.81 1.08 378

Private Telek 0.78 1.25 0.62 1.28 big Big Private Vitale 1.33 1.33 1.00 0.75 0.70 1.08 Samsun 0.94 0.99 0.95 1.07 1.01 1.06 Siirt 0.62 0.74 0.84 1.62 1.49 1.09 Şanlıurfa 1.28 1.32 0.98 0.78 0.77 1.01 Van İpekyolu 0.58 0.59 0.98 1.72 1.58 1.09 Zonguldak 0.83 0.87 0.95 1.20 1.17 1.03 Table 5 displays the results of data envelopment analysis with the method of inputoriented CCR. It reveals the need to make the amount of input reduced and the amount of output increased for being organizations more efficient. In order to achieve the efficient scores, the number of nurses in Mersin needs to be reduced at least 31 units. number of outpatient examinations should be increased 94 units in Ozel Can. Table 5. Necessary Improvements for Inefficient Hospitals According to Method of Input- Oriented CCR Decision Making Unit Total Total number number number number of of outpatient number of of of registered examination nurses physician inpatien beds s s ts Adana 0.58 0.00 0.00 0.00 0.00 Ankara Dr.Sami Ulus 35.34 0.00 0.00 0.00 0.00 Bingöl 15.25 0.00 0.00 0.00 0.00 Bursa Dörtçelik 147.00 0.00 0.00 0.00 6979.12 Diyarbakır 32.52 0.00 38.45 0.00 0.00 Giresun 2.05 0.00 0.00 0.00 0.00 İstanbul Esenler 0.00 0.00 32.53 0.00 1990.91 İstanbul Süleymaniye 7.73 0.00 0.00 0.00 0.00 İstanbul Zeynep Kamil 48.86 0.00 0.00 0.00 0.00 İzmir Buca 0.26 0.00 0.00 0.00 0.00 Mersin 30.87 0.00 0.00 0.00 0.00 Private Can 0.00 0.00 0.00 93.85 0.00 Private Hüma 1.06 0.00 0.00 0.00 0.00 Private İstanbul 22.07 0.00 0.00 0.00 2075.86 Samsun 28.58 0.00 0.00 0.00 0.00 Siirt 14.96 0.00 0.00 0.00 0.00 Van İpekyolu 38.81 0.00 0.00 0.00 0.00 379 Table 6 shows the results of data envelopment analysis with the method of input-oriented BBC. It demonstrates the need to make the amount of input reduced and the amount of output increased for being organizations efficient, which are inefficient. In order to achieve efficient scores, the number of nurses in Ankara Dr. Zekai Tahir Burak should be reduced at least 4 and the number of physicians should be reduced at least 21 physicians.

Table 6. Necessary Improvements for Inefficient Hospitals According to Method of Input- Oriented BCC Decision Making Unit Total Total number number number of number number of of outpatie of of physicia register nt inpatien nurses ns ed beds examina ts tions Ankara Dr. Sami Ulus 52.62 14.42 0.00 0.00 0.00 Ankara Dr. Zekai Tahir Burak 3.49 20.86 0.00 0.00 0.00 Bingöl 19.08 0.00 0.00 0.00 0.00 Giresun 3.62 0.00 19.29 0.00 0.00 İstanbul Esenler 0.00 0.0 40.30 0.00 2533.58 İstanbul Süleymaniye 7.63 0.00 0.00 0.00 0.00 İstanbul Zeynep Kamil 67.62 20.95 0.00 0.00 0.00 İzmir Buca 1.39 5.54 0.00 0.00 0.00 Konya Dr. Faruk Sükan 0.00 0.00 15.65 0.00 0.00 Private İstanbul 15.08 0.00 0.00 0.00 1968.81 Private Meltem 0.00 0.00 0.00 0.00 1013.11 Private Tarsus 0.00 0.00 0.00 0.00 455.42 Siirt 24.03 0.00 9.27 0.00 0.00 Van İpekyolu 35.01 0.00 0.00 0.00 0.00 380 4. CONCLUSION In this study, relative efficiencies of Gynecology and Obstetrics Hospitals in Turkey are calculated by both input-oriented and output-oriented variables according to methods of CCR and BCC. According to the results of study, 11 of 40 decision making units are efficient and 29 hospitals are inefficient. Efficient hospitals consists of 5 private and 6 public hospitals. In this case, it is suggested that these findings are valid for both public and private hospitals. Inefficient hospitals should reduce the number of nurses at varying rates. According to method of BCC, the number of nurses, physicians and beds should be reduced. This research shows that health care organizations do not use their resources efficiently. In an increasingly competitive environment in the health care sector, the following matters may be offered for health care organizations, that require maintaining their operations: Despite limitations with legal regulations, the employment of managers, who has received training in hospital management, should be increased. Mechanisms should be established, which can be encourage efficient work and reward hospital managers that are determined to work efficiently. In addition, the hospitals, which are determined to work inefficiently, should be examined in detail.

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