The main objective of the study was to compare technical efficiency between affiliated and non-affiliated hospitals of Turkey. There was six otput variables and four input variables to evaluate hospitals efficiencies using with Ministry of Health (MoH) data from 2013 to 2017. Totally, 25 affiliated hospitals and 53 non-affiliated hospitals were analysed using input-oriented Data Envelopment Analysis (DEA) model under variable return to scale assumptions. The study results that, affiliated hospitals efficiency scores were higher than non-affiliated training and research hospitals. These results showed us the application of the affiliation system was successful in Turkey. Furthermore, this is the first study which has been performing whole affiliated and non-affiliated hospital efficiency measures together in Turkey and it offers an original contribution to literature with this direction.
Research in practice
Is affiliation system successful in healthcare at Turkey?
https://doi.org/10.21203/rs.3.rs-140051/v1
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Affiliated Hospitals
Data Envelopment Analysis (DEA)
Health Services
Efficiency
Turkey
The restructuring of health systems with the effect of neo-liberal reforms after 1980 has become the main subject of the countries' political agendas. This development was triggered by demographic, socio-economic and political factors as well as advances in the field of new drugs and medical technology, qualitative and quantitative increase in health manpower, and better service expectation of patients. Special reforms with competition, profitability, efficiency and flexibility in health care have led to fundamental changes in the quality and organization of services (Küçük, 2018: 57).
The policies and practices for improving the efficiency of hospitals were used with Health Transformation Program (HTP), which has been ongoing since 2003 in Turkey (Sulku, 2011; Barış et al., 2011). Before 2003, Turkish healthcare services had disorganised with regard to the financing, operating and insurance systems which divided by five parts. This parts has different opportunities and have a big inequalities for accessing the services and a quality of services with their individual networks. After 2003, Turkey has broadened the financial protection with health reforms for expanding service deliveries and accessing health services despite of the high population increments and high health insurance costs (OECD,2008; WHO,2012). So that, HTP in Turkey was intended to promote important results and enhance healthcare system performance and improve equal attainment to health services as in other countries (MoH, 2003).
In 2009, the new program, which called ‘affiliation system’, is the establishment of cooperation among institutions for common purposes and there are similar applications in various fields and different ways in the world (Pardes,2010). This system in the health care services means that hospitals and universities are expected to carry out their duties and services, and to act together in the fields of training, delivering medical care to patients, technical and cooperation in order to realize their common goals and interests in Turkey. It is given the opportunity to be affiliated with MoH hospitals and relevant faculties of universities in Turkey to benefit from each other. Education in physicians is provided in medical faculties and training and research hospitals which is related to health ministries. With this dual structure both its lecturers and institutional culture and quality of medical training has been the subject of controversy over the years. Various problems have arisen with the insufficiency of educational staff and the opening of many new medical faculties. Besides, transferring to the university that lecturers, who are not already enough in public hospitals, has started to hinder the medical education in many of the training and research hospitals. For all these reasons, there is a need for affiliation system for providing efficient and stable utilization of manpower of university and the technical potentiality of the training and research hospitals in Turkey (Samastı, 2018).
There are some important studies in the literature about affiliation systems in the health care services. Such as, Ulger et al. (2015) was examined effect of affiliation on employee satisfaction. It was emphasized that there were conflicts between the doctors of the public and university hospitals participating in the study due to the application of affiliation. In general, hospital workers suggested that due to the insufficient number of staff, employees were working overtime and human resources could not be used effectively. It was emphasized that conflicts arise in the state hospital and university hospital doctors participating in the study due to the affiliation application. Also, employees complained of the affiliation system that they increased the workload and motivation. A study was published by Uğurluoğlu (2015) regarding the affiliation model within the scope of management models applied in university hospitals. It is argued that university hospitals should be managed with a personalized and flexible organization model that envisages functional independence from each other.
The studies for assessing work performance of affiliated hospitals relative to the other hospitals. The findings of these research point to little or no operational efficiency benefit for affiliated hospitals (Coyne, 1982; Menke, 1997; Carey, 2003; Burns et al., 2015). Nevertheless, there is increasing concern that this type of consolidation will boost hospital prices increased hospital debates about healthcare programs (Gaynor and Town, 2012; Cutler and Morton, 2013; Dafny, 2014). Owing to giving better results of affiliation system in hospitals without strong proof led to an increase in the antitrust review of establishment and expansion of the health system (Daly, 2014).
Ciliberto and Dranove (2006) examined the effect of the affiliation hospital system which interacts between physicians on hospital pricing in the 1990s. According to the study, it is revealed that labour force affiliation has no direct effect on hospital pricing. However, in some rural hospitals, large price decreases have been observed. In a study by Cutler and Morton (2013), it was revealed that the accounts of providing health care service enlargement by hospitals which have been affiliated in the United States and this has positive effects on the profitability of hospitals. It was argued that in the Kirchhoff (2013) study, the affinity between physician and health institution contributed to increase the integration in the institution, to provide price control of the state and to decrease health expenditures.
Zepeda et al. (2016) examine the possible impacts of survivability of hospital affiliation system handling with the efficiency of inventory operations. The findings indicate that national level, regional parts arrangements in affiliation system is counteracting results behind the poor resources of logistics facilities, the reducing consequence is highest for local system membership. Also, these outcomes lead to opportunities for enhanced performance of affiliation structure and not regarded the policy debates in hospital system formation.
Most of the studies on the effects of the affiliation system has appeared in Iran. Rafiei and Pourreza (2013) examined the job satisfaction of corporate culture of hospitals affiliated with Tehran University. Ebrahimipour et al. (2013) analysed the patients’ complaints with big training hospital which was affiliated to Mashhad University. Mahdavi et al. (2018) evaluated the health service quality of 700 affiliated hospitals of Iran for understanding the impact of the Iranian Health System Reform program. Farzianpour et al. (2011) studied the assesment of performance of affiliated hospitals of Tehran University using with Baldrige Excellence Model. Goudarzi et al. (2014) calculated the technical efficiency of affiliated hospitals in Iran between 1999–2011 using with Stochastic Frontier Analysis. Farzianpour et al. (2012) analyzed performance of affiliated hospitals in Tehran University with DEA.
İlgün et al. (2019) worked on training and research hospital in Turkey. They examined only 47 units training and research hospitals, in spite of being 78 training research hospital in Turkey. Another study in Turkey is realized by Küçük et al. (2019). In this study, they did the provincial and regional analysis with the efficiency of 669 public hospitals of Turkey with DEA and present the relationship between health reform program and their analysis results.
In this study, technical efficiencies of affiliated and non-affiliated hospitals are examined by several dimensions using an input-oriented DEA model under the variable return-to-scale assumption. There are 78 training and research hospital in Turkey. The 25 of them are affiliated and 53 of them are non-affiliated hospitals. Therefore, this study using data collected from MoH Public Hospitals Directorate General from 2013 to 2017 for providing to evaluate the efficiency of 78 training and research hospital in Turkey with affiliated and non-affiliated discrimination.
One of the methods used to evaluate the performance of Decision Making Units (DMU) using with several outputs and inputs is DEA. In the literature, CCR model which was putforwarded by Charnes et al. (1978) and BCC model by Banker et al. (1984) are the common DEA models. DEA based on a non-parametric mathematical programming can measure the performance of DMUs. It calculates a score which is limited to 0 to 1 for each DMU. DMU indicates efficient with these scores is equivalent to 1. Otherwise, DMU is inefficient (Charnes et al., 1978). When the production process of a DMU is complex, due to imposes of main objective function of DEA is preferred rather than stochastic frontier, one of other approach without any limitations (Aigner et al., 1977).
Some of the important publications which is interested in hospital efficiencies are examined to understand hospital performance better. The country, number of hospitals and years information are reviewed owing to comprehend the time period, country coverage and working size of the studies. Also, method and orientation which is used for analysing the efficiency of the hospitals is presented in Table 1.
Table 1. Summary of Systematic Review on Hospital Technical Efficiency Using DEA.
|
Authors |
Country |
Number of Hospitals |
Years |
I - O Oriented |
Method |
|
Ersoy et al. (1997) |
Turkey |
573 |
1994 |
Input |
Standard DEA Model |
|
Junoy (2000) |
Spain |
94 |
1990-1997 |
Input |
Standard DEA Model |
|
Athanassopoulos and Gounaris (2001) |
Greece |
98 |
1992 |
Input |
Linear Programming Based Model |
|
Giokas (2001) |
Greece |
91 |
1992 |
Input |
Standard DEA Model |
|
Chang et al. (2004) |
Taiwan |
276 |
1994-1997 |
Output |
Standard DEA Model |
|
Watcharasriroj and Tang (2004) |
Thailand |
92 |
2003 |
Input |
Mann–Whitney test; Tobit regression analysis |
|
Linna et al. (2006) |
Finland, Norway |
98 |
1999 |
Input |
Standard DEA Model |
|
Aletras et al. (2007) |
Greece |
51 |
2000-2003 |
Input |
Standard DEA Model |
|
Vitikainen et al. (2009) |
Finland |
40 |
2005 |
Input |
Standard DEA Model |
|
Berta et al. (2010) |
Italy |
134 |
1998-2007 |
Input |
Cobb Douglas, Random Effect Model |
|
Caballer-Tarazona et al. (2010) |
Spain |
22 |
2005 |
Input |
Standard DEA Model |
|
Chang et al. (2011) |
Taiwan |
31 |
1998-2004 |
Input |
Malmquist Index |
|
Chu NG (2011) |
China |
463 |
2004-2008 |
Input |
Malmquist Index |
|
Farzianpour et al. (2012) |
Iran |
16 |
2010 |
Input-Output |
Standard DEA Model |
|
Hu, Qi and Yang (2012) |
China |
30 |
2002-2008 |
Output |
Standard DEA and Tobit Model |
|
Kirigia and Asbu (2013) |
Eritre |
19 |
2007 |
Output |
Standard DEA and Tobit Model |
|
De Nicola et al. (2013) |
Italy |
390 |
2004-2005 |
Output |
Bootstrap DEA and CART |
|
Mitropoulos et al. (2013) |
Greece |
32 |
2001 |
Input |
Integer Programming (IP) Location Allocation Models |
|
Gök and Sezen (2013) |
Turkey |
348 |
2008 |
Input |
Multiple Regression Analysis |
|
Kacak et al. (2014) |
Turkey |
245 |
2008 |
Output |
Standard DEA Model |
|
Yang and Zeng (2014) |
China |
70 |
2006-2010 |
Output |
Three Stage Malmquist Index |
|
Chowdhury et al. (2014) |
Canada |
113 |
2002-2006 |
Output |
Boot- Strapp, Malmquist Index (Case Mix), Kernel Density Estimation Tests |
|
Harrison and Meyer (2014) |
USA |
165 |
2007-2011 |
Input |
Standard DEA Model |
|
Leleu et al. (2014) |
USA |
138 |
2005 |
Input-Output |
Standard DEA Model |
|
Mitropoulos et al. (2015) |
Greece |
117 |
2009 |
Output |
Markov chain Monte Carlo (MCMC), DEA with Gibbs Sampler (GS) Algorithm. |
|
Matranga and Sapienza (2015) |
Italy |
116 |
2009 |
Output |
Standard DEA Model |
|
Alonso et al. (2015) |
Spain |
25 |
2009 |
Input-Output |
Bootstrap-DEA; Mann–Whitney tests. |
|
Jola-Sanchez et al. (2016) |
Colombia |
163 |
2007-2011 |
Output |
Standard DEA Model |
|
Erus and Hatipoglu (2017) |
Turkey |
2003-2006 |
Output |
Malmquist Index |
|
|
Campanella et al. (2017) |
Italy |
50 |
2010 |
Input |
Standard DEA and Tobit regression analysis |
|
Flokou et al. (2017) |
Greece |
107 |
2009-2013 |
Input |
Standard DEA and Malmquist Productivity Index |
|
Gimenez et al. (2018) |
Colombia |
602 |
2009-2013 |
Input-Output |
Standard DEA Model |
|
Kucuk et al. (2019) |
Turkey |
669 |
2013-2017 |
Output |
Standard DEA Model |
Either developing or developed countries included from China to Spain, USA to Italy, Colombia to Taiwan, Iran to Greece in the summary of the systematic review process on hospital efficiency. According to the size of the hospital, the number of DMUs in these studies ranged from 16 to 669 DMUs. According to Input - Output oriented model status, the studies before 2010 were mostly oriented towards input-oriented DEA models, but in recent years number of output-oriented models was higher, for the concepts of cost inefficiency and especially resource efficiency became more important until the end of 2000s. The cases focusing on quality, such as patient satisfaction, are used in the studies. According to recent studies such as Gimenez et al. (2018), hospital efficiency has extended productivity analysis to include minimization of output inefficiency. One of the important issues of measuring efficiency in the hospitals is appropriate inputs and outputs. According to literature, inputs were commonly used by researchers in analysing hospital technical efficiency include the number of doctors, nurses, beds and total expenditure. The number of total patients and inpatients, average daily admission, the number of surgeries, bed occupancy rate and total revenue are commonly the outputs of the hospitals.
Several dimensions were used for measuring and assessing of hospital performance. Output and input variables identification process also the most important issue in DEA context. Output and input variables for hospitals in DEA have been determined with preceding empiric and theoretical studies which is given in Table 1 to capture the performance as accurately and comprehensively. There are six output and four input variables in this study are described below.
Output variables:
- Total revenue (TR),
- Outpatients number (O),
- Total inpatients (TI),
- Number of surgeries (S),
- Number of visits to emergency departments (VE),
- Number of outpatient visits per physicians (VO)
Input variables:
- Total expenditures (TE),
- Number of doctors (D),
- Number of beds (B),
- Number of magnetic resonance imaging units (I).
Output and input variables summary data is shown at the Table 2. The inputs of number of doctors, number of beds, number of MRI units and scanners are increased 10–25% in these five years period. This means that the physical, technical and labour opportunities of the hospitals are become better. However, total expenditures of the hospitals are increased more than these inputs with 74% increase. On the other hand, the total revenue of the hospitals are increased 50%. This means that, the hospitals costs are higher than their revenues.
In addition, despite the increase in human resources and physical infrastructure, it is seen that the expected increase in output is not realized with total inpatients variables in operation and hospitalization. This indicates with physicians concentrate on outpatient services rather than labour intensive services based on inpatient and surgery.
In 2017, while minimum number of bed was 85 as well as maximum number of bed was 1500 and the number of doctor was minimum 13, maximum 515. In this case, it is seen that affiliation application is not a standard application and it cannot be transformed into a completely similar structure in terms of scale and capacity.
| Year | Variable | Number of beds | Number of doctors | Number of MRI units and CT scanners | Total expenditures | Outpatients Number | Number of visits to emergency departments | Number of outpatient visits per physicians | Total inpatients | Number of surgeries | Total revenue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2013 | min | 85 | 7 | - | 5.018.547 | 33.416 | - | 4,66 | 1.399 | - | 4.945.208 |
| max | 1.434 | 462 | 4 | 218.755.547 | 2.915.346 | 802.662 | 29,25 | 90.414 | 167.259 | 449.041.735 | |
| mean | 546 | 179 | 1 | 96.578.525 | 1.039.596 | 251.884 | 17,97 | 31.949 | 42.390 | 103.337.323 | |
| std dev | 293 | 102 | 1 | 57.030.877 | 635.529 | 201.106 | 5,29 | 18.933 | 35.566 | 69.828.065 | |
| 2014 | min | 85 | 14 | - | 6.148.157 | 45.005 | - | 4,88 | 1.535 | - | 5.315.884 |
| max | 1.434 | 451 | 9 | 263.951.113 | 3.221.968 | 799.633 | 28,95 | 95.548 | 152.880 | 267.122.525 | |
| mean | 544 | 190 | 2 | 111.199.774 | 1.096.361 | 260.371 | 17,98 | 32.778 | 41.731 | 110.548.974 | |
| std dev | 288 | 107 | 1 | 65.402.246 | 657.372 | 210.929 | 5,48 | 19.136 | 30.652 | 64.724.079 | |
| 2015 | min | 85 | 8 | - | 7.997.367 | 51.662 | - | 4,65 | 1.403 | - | 5.933.537 |
| max | 1.414 | 467 | 9 | 294.365.789 | 3.293.988 | 853.430 | 37,90 | 93.988 | 101.202 | 280.649.953 | |
| mean | 539 | 203 | 2 | 123.233.487 | 1.135.525 | 269.637 | 17,89 | 32.197 | 33.045 | 116.994.710 | |
| std dev | 288 | 113 | 1 | 71.757.757 | 670.280 | 217.667 | 5,90 | 18.729 | 22.794 | 67.551.112 | |
| 2016 | min | 85 | 10 | - | 10.707.574 | 48.902 | - | 2,89 | 1.403 | - | 5.999.539 |
| max | 1.489 | 507 | 9 | 347.335.039 | 3.754.271 | 969.021 | 32,33 | 94.969 | 103.911 | 301.148.561 | |
| mean | 559 | 211 | 2 | 149.554.919 | 1.236.114 | 284.111 | 18,41 | 32.520 | 32.188 | 132.772.737 | |
| std dev | 312 | 122 | 1 | 86.477.799 | 783.392 | 241.017 | 5,54 | 19.896 | 23.561 | 77.567.536 | |
| 2017 | min | 85 | 13 | - | 11.231.960 | 54.725 | - | 7,17 | 1.520 | - | 7.300.235 |
| max | 1.500 | 515 | 9 | 407.281.073 | 4.046.361 | 1.254.225 | 32,64 | 94.410 | 103.550 | 344.114.177 | |
| mean | 604 | 222 | 2 | 168.060.097 | 1.342.539 | 303.675 | 18,95 | 33.193 | 33.262 | 155.739.943 | |
| std dev | 337 | 123 | 1 | 98.209.226 | 838.582 | 255.744 | 5,25 | 19.880 | 23.498 | 92.880.062 | |
| Change % | 10,57 | 23,51 | 20,09 | 74,01 | 29,14 | 20,56 | 5,45 | 3,90 | -21,53 | 50,71 |
Technical efficiencies of affiliated hospitals given at Table 3. In the first year 8 hospitals were efficient, respectively; 11 hospitals were efficient in 2014, 10 hospitals were efficient in 2015, 10 hospitals were efficient in 2016, 8 hospitals were efficient in 2017. Totally, 3 hospitals were efficient in these 5 years from 25 affiliated hospitals. Bolu Izzet Baysal Physical Therapy and Rehabilitation Hospital, Ankara Yıldırım Beyazıt University Yenimahalle Hospital and Giresun Women's Maternity and Children' s hospitals have been efficient in 5 years. It is also observed that the financial situation of public hospitals has been decreasing in parallel with the general increase in expenses since 2016.
| HOSPITALS | 2013 | 2014 | 2015 | 2016 | 2017 |
|---|---|---|---|---|---|
| KIRŞEHIR AHI EVRAN UNIVERSITY | 1,0000 | 1,0000 | 0,9831 | 1,0000 | 0,9196 |
| ISTANBUL MARMARA UNIVERSITY | 0,8911 | 1,0000 | 1,0000 | 1,0000 | 1,0000 |
| ISTANBUL MEDENIYET UNIVERSITY | 0,7290 | 0,8814 | 0,9022 | 0,8678 | 0,8435 |
| ORDU UNIVERSITY | 0,7144 | 0,8626 | 0,8087 | 0,7086 | 0,8222 |
| SAKARYA UNIVERSITY | 0,8731 | 0,9614 | 0,9372 | 1,0000 | 1,0000 |
| RIZE RECEP TAYYIP ERDOĞAN UNIVERSITY | 0,8258 | 0,8974 | 0,9063 | 0,9203 | 0,8627 |
| ANKARA YILDIRIM BEYAZIT UNIVERSITY ATATÜRK | 0,9293 | 1,0000 | 0,8739 | 0,9186 | 0,9319 |
| IZMIR KATIP ÇELEBI UNIVERSITY | 0,5410 | 0,8573 | 0,8683 | 0,8079 | 0,9204 |
| MUĞLA SITKI KOÇMAN UNIVERSITY | 0,5930 | 0,8933 | 0,9193 | 0,8691 | 0,8786 |
| ERZINCAN UNIVERSITY | 0,8463 | 0,9748 | 0,9369 | 0,8488 | 0,9443 |
| ADIYAMAN UNIVERSITY | 0,9938 | 1,0000 | 1,0000 | 1,0000 | 0,9360 |
| KÜTAHYA DUMLUPINAR UNIVERSITY | 0,6085 | 0,9756 | 0,8294 | 0,8409 | 0,9303 |
| ÇORUM HITIT UNIVERSITY | 0,7939 | 0,9660 | 0,9620 | 0,8450 | 0,7848 |
| AMASYA UNIVERSITY | 1,0000 | 1,0000 | 1,0000 | 0,8804 | 0,9123 |
| KARABÜK UNIVERSITY | 1,0000 | 0,9545 | 0,9870 | 0,8615 | 0,8991 |
| BOLU IZZET BAYSAL UNIVERSITY | 0,9582 | 0,7856 | 1,0000 | 0,7887 | 0,8396 |
| BOLU IZZET BAYSAL PHYSICAL THERAPY | 1,0000 | 1,0000 | 1,0000 | 1,0000 | 1,0000 |
| BOLU MENTAL HEALTH AND DISEASES | 1,0000 | 0,9168 | 0,8548 | 1,0000 | 1,0000 |
| ANKARA YILDIRIM BEYAZIT UNIVERSITY YENIMAHALLE | 1,0000 | 1,0000 | 1,0000 | 1,0000 | 1,0000 |
| ANTALYA ALANYA ALAADDIN KEYKUBAT UNIVERSITY | 1,0000 | 1,0000 | 1,0000 | 0,8292 | 0,8923 |
| AKSARAY UNIVERSITY | 0,9951 | 1,0000 | 1,0000 | 1,0000 | 1,0000 |
| GIRESUN UNIVERSITY | 0,9219 | 1,0000 | 0,9200 | 0,9904 | 0,7867 |
| GIRESUN UNIVERSITY MATERNITY AND CHILDREN | 1,0000 | 1,0000 | 1,0000 | 1,0000 | 1,0000 |
| NIĞDE ÖMER HALISDEMIR UNIVERSITY | 0,9206 | 0,9468 | 1,0000 | 0,9715 | 1,0000 |
| UŞAK UNIVERSITY | 0,9704 | 0,9750 | 0,9634 | 1,0000 | 0,9293 |
Technical efficiencies of non-affiliated hospitals given at Table 4. In the first year 10 hospitals were efficient, respectively; 16 hospitals were efficient in 2014, 16 hospitals were efficient in 2015, 12 hospitals were efficient in 2016, 11 hospitals were efficient in 2017. Totally, 4 hospitals were efficient in these 5 years from 53 non-affiliated hospitals. Ulucanlar Eye, Bağcılar, Prof. Dr. N. Reşat BELGER Beyoğlu Eye, Kanuni Sultan Süleyman hospitals have been efficient in 5 years.
| HOSPITALS | 2013 | 2014 | 2015 | 2016 | 2017 |
|---|---|---|---|---|---|
| ZEKAİ TAHİR BURAK MATERNITY AND CHILDREN | 1,0000 | 1,0000 | 0,9909 | 0,8955 | 0,9355 |
| ANKARA | 0,8782 | 0,9479 | 0,8769 | 0,8888 | 0,8398 |
| ANKARA PHYSICAL THERAPY | 0,3640 | 0,8929 | 0,9522 | 0,9008 | 0,8620 |
| ANKARA NUMUNE | 0,6530 | 0,8615 | 0,8226 | 0,7706 | 0,7559 |
| TÜRKİYE HIGH SPECIALIZED HOSPITAL | 0,6025 | 0,8804 | 0,8568 | 0,8795 | 0,8297 |
| ULUCANLAR EYE | 1,0000 | 1,0000 | 1,0000 | 1,0000 | 1,0000 |
| ATATÜRK CHEST | 0,4798 | 0,8652 | 0,7775 | 0,8117 | 0,8025 |
| ANKARA CHILDREN AND ONCOLOGY | 0,5930 | 0,7938 | 0,8440 | 0,8189 | 0,7713 |
| ANKARA DIŞKAPI YILDIRIM BEYAZIT | 0,8188 | 0,9608 | 0,9470 | 0,8779 | 0,8454 |
| DR.SAMİ ULUS MATERNITY AND CHILDREN | 0,6044 | 0,8752 | 0,8523 | 0,7144 | 0,6916 |
| ANKARA ETLİK ZÜBEYDE HANIM | 0,9613 | 1,0000 | 1,0000 | 1,0000 | 1,0000 |
| ANKARA KEÇİÖREN | 1,0000 | 0,9926 | 1,0000 | 1,0000 | 1,0000 |
| DR. ABDURRAHMAN YURTASLAN ONCOLOGY | 0,5847 | 0,9248 | 0,8809 | 0,9638 | 0,8953 |
| ANTALYA | 0,7786 | 1,0000 | 0,9576 | 0,8914 | 0,8108 |
| BURSA HIGH SPECIALIZED HOSPITAL | 0,9740 | 0,9115 | 0,8450 | 0,8291 | 0,8831 |
| DİYARBAKIR GAZİ YAŞARGİL | 0,6265 | 0,8221 | 0,8079 | 0,8229 | 0,9077 |
| ELAZIĞ | 1,0000 | 1,0000 | 0,8136 | 0,8962 | 0,9976 |
| ERZURUM REGIONAL | 0,7876 | 0,9289 | 0,9265 | 0,8246 | 0,8466 |
| KARTAL DR.LÜTFİ KIRDAR | 0,8267 | 1,0000 | 1,0000 | 1,0000 | 1,0000 |
| KARTAL KOŞUYOLU HIGH SPECIALIZED HOSPITAL | 0,7840 | 1,0000 | 1,0000 | 1,0000 | 1,0000 |
| SÜREYYAPAŞA CHEST HEART VESSEL | 0,4291 | 0,8256 | 0,7784 | 0,8775 | 0,9152 |
| DR.SİYAMİ ERSEK CHEST HEART VESSEL | 0,5005 | 0,8428 | 0,8953 | 0,8765 | 0,8056 |
| ERENKÖY MENTAL AND NEUROLOGICAL | 0,6183 | 0,8933 | 0,8347 | 0,8707 | 0,9035 |
| FATİH SULTAN MEHMET | 0,8297 | 0,9371 | 0,9559 | 0,9439 | 0,8880 |
| HAYDARPAŞA NUMUNE | 0,6862 | 0,8835 | 0,7931 | 0,8489 | 0,8565 |
| ÜMRANİYE | 1,0000 | 1,0000 | 1,0000 | 0,9045 | 0,8326 |
| ZEYNEP KAMİL MATERNITY AND CHILDREN | 0,9163 | 1,0000 | 1,0000 | 0,7869 | 0,9002 |
| BAĞCILAR | 1,0000 | 1,0000 | 1,0000 | 1,0000 | 1,0000 |
| BAKIRKÖY DR.SADİ KONUK | 1,0000 | 0,9638 | 0,9698 | 1,0000 | 1,0000 |
| PROF.DR.MAZHAR OSMAN MENTAL | 0,7234 | 1,0000 | 0,9002 | 0,9837 | 0,9316 |
| İSTANBUL PHYSICAL THERAPY | 0,6432 | 1,0000 | 1,0000 | 1,0000 | 1,0000 |
| BALTALİMANI METİN SABANCI | 0,7754 | 1,0000 | 1,0000 | 1,0000 | 1,0000 |
| GAZİOSMANPAŞA TAKSİM | 0,6949 | 0,9618 | 0,9306 | 0,8514 | 0,9208 |
| OKMEYDANI | 0,8189 | 0,9629 | 0,9279 | 0,8583 | 0,9403 |
| PROF.DR.N.REŞAT BELGER BEYOĞLU EYE | 1,0000 | 1,0000 | 1,0000 | 1,0000 | 1,0000 |
| ŞİŞLİ HAMİDİYE ETFAL | 0,7767 | 0,9433 | 0,9594 | 0,8601 | 0,8950 |
| KANUNİ SULTAN SÜLEYMAN | 1,0000 | 1,0000 | 1,0000 | 1,0000 | 1,0000 |
| MEHMET AKİF ERSOY CHEST HEART VESSEL | 0,9160 | 0,9688 | 1,0000 | 1,0000 | 0,9839 |
| İSTANBUL | 0,7349 | 0,9085 | 0,9317 | 0,9002 | 0,8943 |
| HASEKİ | 0,9035 | 1,0000 | 0,9811 | 0,8522 | 0,8630 |
| YEDİKULE CHEST | 0,5526 | 0,8438 | 0,8685 | 0,8249 | 0,8230 |
| BOZYAKA | 0,6081 | 0,8621 | 0,8831 | 0,8097 | 0,9473 |
| DR.BEHÇET UZ MATERNITY AND CHILDREN | 0,6408 | 0,8956 | 0,9353 | 0,8236 | 0,8899 |
| DR.SUAT SEREN CHEST | 0,5590 | 0,8616 | 0,8811 | 0,8081 | 0,7948 |
| İZMİR TEPECİK | 0,7223 | 0,8427 | 0,9363 | 0,8028 | 0,7893 |
| KAYSERİ | 0,7694 | 0,9421 | 0,9559 | 0,8809 | 0,9263 |
| KOCAELİ DERİNCE | 0,9675 | 0,9149 | 0,8901 | 0,9075 | 0,8998 |
| KONYA | 0,7233 | 0,8457 | 0,8349 | 0,9048 | 0,9208 |
| SAMSUN | 0,5778 | 0,8821 | 0,8721 | 0,9752 | 0,8372 |
| MEHMET AKİF İNAN | 0,9581 | 0,9938 | 1,0000 | 0,8924 | 0,9436 |
| AHİ EVREN CHEST HEART VESSEL | 0,6183 | 0,9993 | 1,0000 | 0,9592 | 0,9909 |
| KANUNİ | 0,7215 | 0,8387 | 0,8645 | 0,7975 | 0,7643 |
| VAN | 1,0000 | 0,8972 | 1,0000 | 0,9573 | 0,9685 |
The trend of training and research hospitals between affiliated hospitals and non-affiliated hospitals appears to be consistent at from 2013 to 2017 in Fig. 1. However, hospitals that are non-affiliated; it has shown a lower performance than other training and research hospitals due to the fact that the structuring of educational staff, physical and infrastructural conditions take a long time, and the income-expense balance resulting from the increase in additional payment is deteriorated.
Average technical efficiency comparison of affiliated and non-affiliated hospitals and percentage difference between affiliated and non-affiliated hospitals were shown in Table 5. Average technical efficiency of affiliated hospitals were increased 4,20% from 2013 to 2017. Average technical efficiency of non- affiliated hospitals were increased 17,19% from 2013 to 2017. These rising trends showed us the Turkish health system become more higher performance than before. Also, from 2014 to 2017 the two type of hospitals average technical efficiency difference were approximately 2% except 2013.
| YEAR 2013 | YEAR 2014 | YEAR 2015 | YEAR 2016 | YEAR 2017 | |
|---|---|---|---|---|---|
| Average Efficiency Score of Affiliated Hospitals | 0,8842 | 0,9539 | 0,9461 | 0,9179 | 0,9213 |
| Average Efficiency Score of Non-Affiliated Hospitals | 0,7680 | 0,9315 | 0,9232 | 0,8971 | 0,9000 |
| Difference Between Affiliated & Non-Affiliated Hospitals (%) | 13,14% | 2,35% | 2,42% | 2,27% | 2,31% |
Efficiency results of affiliated hospitals were higher than those of non-affiliated training and research hospitals. Training and research hospitals which is affiliated to have more organized structure in the production of services is an important point advantage, considering that the general hospital also they have with equivalent effectiveness analysis was performed to compare the general hospital. In this context, the hospital which inputs and outputs similar with the affiliated hospitals, it is seen that the performance of general hospitals are higher than others. This shows us that the affiliation model yields a positive result since these hospitals are predicted to have similar efficiency scores.
In this study, at first Turkish Health System and Affiliations System which was implemented as new system in Turkey was examined in the concept of Health Reform Program. Also, it is mentioned that the condition of the affiliation system and why we need to use this system in Turkey. Then, literature review of measurement of affiliated hospital efficiency studies conducted in various countries is examined. DEA was used as a methodology in the study. All training and research hospitals in Turkey which affiliated and non-affiliated have been analysed and special evaluations have been made for these hospitals between 2013 and 2017. So that, presenting more realistic results of the analysis overall training and research hospitals included in analysis.
While the previous system has more regular and organized, the affiliation model is very new in Turkey. So that, we have to need a time for understand of the effect of the public. Nevertheless, with this system all patients are able to consult the academic staff of the university and owing to this reason the academician expertise more area with these all types of patient patterns. For all that, health service suppliers under pressure and the instruction cost of new hospital building are another invisible cost of this system. However, this situation needs more time and there is lack of any student education processes in these time period and also the opportunity cost become another important phenomenon.
The main limitation of the study is that before and after period of affiliation was not examined separately. If such an examination is made in future studies, the benefits of affiliation system for university hospitals will be revealed in more detail. In addition, the two-stage Network DEA method is planned to use for analysing the “education and training” function and “service delivery” stages separately with their own parameters and to present a different perspective on the affiliation system.
Affiliation model which began in 2009 as a pilot model in Turkey, expands to the opening of new medical schools with parallel policies has reached 25 in 2019. Thus, without any capital investments, the newly established medical faculties and public hospitals carry out health services and training and research activities together. According to a comprehensively analysis, the efficiency scores of the affiliated hospitals were higher than those of non-affiliated training and research hospitals which are included all Turkish training and research hospitals. Herewith, status of the affiliated hospitals will be more clearly revealed. The desired results could be achieved from the gradual augmentation application.
Continuous changes in the affiliation regulations, the legal infrastructure of the model is fully established, also academic and administrative problems are proof of this system difficulties both for the university and the MoH hospitals. The efficiency analysis conducted within this framework confirms these findings and results. All the results of this application positively affected. In general, it is possible to evaluate the effectiveness of the model in terms of efficiency scores.
Recently, there are some news about conjoint to several university hospitals to MoH hospitals in Turkish Government agenda. In addition, this model is expected to continue towards the merger of university medical faculties, which faced especially financial problems, and MoH hospitals. For this reason, policy makers are advised to take precaution for regarding the function of model and structural problems. For example, the fact that hospitals remain between the “education and training” function and the “service delivery” function may cause conflict between university and hospital staff. Also, in order to eliminate this problem, the process of signing contracts with lecturers in hospitals and university staff is required to be initiated.
It is suggested to compare the affiliation system with longer time series, and to make impact analysis studies that enable before and after period in which model is realized in two separate planes. And this study is the first study that have been performing all affiliated and non-affiliated hospital efficiency measures in Turkey with an original contribution to the literature with this direction.
Ethical Approval and Consent to participate
This study don’t need any ethical approval because no use of human and animal participant.
Consent for publication
Not applicable.
Availability of supporting data
Datasets that are restricted and not publicly available
Competing interests
The author declare that they have no competing interests.
Funding
This study was not funded by any private firm or public institution.
Authors' contributions
There is only one author in this paper. So that all part is designed by this author.
Acknowledgements
The author acknowledge the Ministry of Health Statistic Department for their support to understand the affiliation system of the hospitals.
Authors' information
University of Turkish Aeronautical Association, Industrial Engineering Department, Ankara, Turkey, email:[email protected], mobile: +90 532 266 55 50, address: Bahcekapi Quarter Okul St. No:11 06790 Etimesgut, Ankara, Turkey
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