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Physician payment schemes and physician productivity: Analysis of Turkish healthcare reforms
Accepted Manuscript Title: Physician Payment Schemes and Physician Productivity: Analysis of Turkish Healthcare Reforms Author: Burcay Erus Ozan Hatipoglu PII: DOI: Reference:
S0168-8510(17)30055-6 http://dx.doi.org/doi:10.1016/j.healthpol.2017.02.012 HEAP 3699
To appear in:
Health Policy
Received date: Revised date: Accepted date:
9-3-2016 13-2-2017 21-2-2017
Please cite this article as: Burcay Erus, Ozan Hatipoglu, Physician Payment Schemes and Physician Productivity: Analysis of Turkish Healthcare Reforms, (2017), http://dx.doi.org/10.1016/j.healthpol.2017.02.012 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
*Title Page (showing Author Details)
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Burcay Erus †and Ozan Hatipoglu‡
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Physician Payment Schemes and Physician Productivity: Analysis of Turkish Healthcare Reforms∗
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We would like to thank the editor and two anonymous referees for very helpful comments and Ertunc Aydogdu for research assistance. We acknowledge financial support from Scientific and Technical Research Council of Turkey, Project Nr 108K455 † Department of Economics, Bogazici University, Bebek, Istanbul 34342 Turkey. [email protected]. Phone:+902123597638 ‡ Corresponding Author. Department of Economics, Bogazici [email protected].
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*Highlights (for review)
Highlights - This paper attempts to uncover the causes of the increase in public hospital services after the implementation of a major Turkish Health reform program. - It employs data envelopment analysis to decompose the increase in efficiency into technological and scale components.
- The role of technological change and scale economies are found to be limited
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- We find that the shift from dual-practice to full-time employment at public hospitals by the physicians is the main cause behind the dramatic increase in outpatient and inpatient services provided by the public hospitals.
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*Manuscript (without Author Details) Click here to view linked References
Abstract
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Physician Payment Schemes and Physician Productivity: Analysis of Turkish Healthcare Reforms
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Following healthcare reforms in Turkey, inpatient and outpatient care provided in public hospitals more than doubled from 2003 to 2006. An important component of the reforms has been a shift from a salary based physician compensation scheme
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to one where fee-for-service component is dominant. The change did not only incentivize physicians to provide a higher volume of services but also encouraged them
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to practice full-time, rather than dual-time, in public hospitals. Lacking figures on
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full-time equivalent figures at hospital level, earlier research used head-counts for physician workforce and found technological change and scale economies to be important determinants. We employ data envelopment analysis and find that, under
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plausible scenarios regarding the number of dual vs full-time physician numbers, most of the change in hospital services may be explained only by the shift to fulltime practice. Our estimations find the change in technology and scale economies to play a relatively minor role. JEL classification: I10, I11, I18, C33, C52
Keywords: Efficiency of public hospitals, health transformation program, data envelopment analysis, structural change, model evaluation, part-time work, data envelopment analysis, linear programming
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Introduction
Starting in 2003, Turkey initiated reforms titled Health Transformation Programme (HTP) aiming to increase access to healthcare services and to improve efficiency of health-
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care providers1 . Among other things, reforms resulted in significant increase in the use of inpatient and outpatient services in public hospitals. From 2003 to 2006, number of
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patients cared in public hospitals in Turkey more than doubled from 114 thousand to 254
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thousand (Ministry of Health (MOH) 2012). In this paper we analyze determinants of the change in hospital output using data envelopment analysis (DEA). We pay attention to a particular aspect of the reform, the change in the payment schemes to specialists in
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public hospitals and resulting shift from dual-time to full-time employment. According to the MoH statistics, 89 percent of specialists were employed part-time in public hospitals
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in 2002 and this figure dropped to 44 percent by 2006 as a result of a new payment scheme which favored full-time practice at public hospitals.
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Dual practice is a common practice in medical profession but research is rather limited
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(Moghri et al., 2016). Gonzales and Macho-Stadler (2013) analyze options available to the policy maker and show with a theoretical model that the switch may be harmful for healthcare services in certain instances.While our study is far from making a complete
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evaluation of the change it sheds light to the impact of the reform on the volume of healthcare services in the short run. It is of importance to better understand Turkish healthcare reforms since it is an example of health system reforms conducted with consultancy by World Bank. World Bank has been an active player in redesigning health policy in developing countries since 1980s (Ruger 2005). A number of countries adopted similar reforms and Turkish case has been lauded in some publications, such as Atun et al. (2013) for its success in 1
Okem and Cakar (2015) provide a review of empirical on Turkish healthcare reforms. See OECD/World Bank (2008), for an early description of the reforms and Agartan (2007), and Yenimahalleli-Yasar (2011), for critics. Atun et al. (2013) and responses to them provide a more recent discussion. On specific aspects of the reforms see Erus and Aktakke (2012), for impact on out-of pocket expenditures, Aran and Hentschel (2012), and Erus et al. (2015), for health insurance coverage.
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increasing access to healthcare. What has been missing in these analyses, however, are the mechanisms leading to observed outcomes, mostly because of data limitations. Here, we find that payment schemes to specialists and resulting shift from dual-time to full time
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practice plays an important role in hospital output. This finding is in contrast from previous research on Turkish healthcare reform which
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generally relate increased output to technological change. We argue that this results from the omission of full-time and part time distinction due to data limitations and we address
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the problem using different scenarios. Specifically we show that the results are sensitive to the way physician input is accounted for and that under certain plausible assumptions
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regarding number of part-time specialists in 2003, most of the gain in productivity is explained not by technical efficiency but by the change in the full-time equivalent number
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of physicians.
The article is organized as follows. Next section discusses reforms in public hospital
Background
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system in Turkey briefly. This is followed by the methodology and results.
Turkey went through a rigorous reform process in health care to improve access and
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efficiency starting in 2003. One of the main pillars of the reform was the unification of previously segregated public health insurance schemes with the objective of providing universal coverage. With the new scheme a larger portion of the population was covered for care at a significantly larger network of hospitals which started to include private ones. Number of healthcare services provided soon increased sharply and healthcare spending on patient treatment grew by 5.1 percent per year in real terms from 2003 to 2006 (Sulku 2011b). On the supply side, an important component of the reform process was the new physician payment scheme at public hospitals. The earlier system was largely salary based and a significant proportion of specialists worked dual-time, also operating their
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own private offices. With the reforms a new payment scheme, called Performance Based Supplementary Payment System (PBSPS), was instituted. In PBSPS each and every procedure performed by specialists are assigned points (see Table 1 below for an example
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of the points assigned to different procedures in 2006). The amount of supplementary payment is based on the points collected during the month by the specialist. Although
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there is a component in the system which adjusts the payment according to quality of the hospital, this mostly includes input measures, such as the number of physician offices or
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presence of certain diagnostic devices, but few externally verified outcome measure other than the result of an annual general patient survey about the hospital2 . These payments
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led to an increase of about 200% in specialist pay from 2002 to 2006 in real terms and in 2006 supplementary payment made up about two thirds of the average specialist income
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(World Bank/OECD 2008).
In addition to providing an incentive to increase productivity of physicians, new system also targeted the widespread practice of dual work in public hospitals. Dual-time
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specialists receive only 40% of the points for services that they provide and hence are
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entitled to significantly lower pay. As the new public health insurance scheme covered care at private hospitals but not at private offices, demand for these offices went down as
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well, and a large number of specialists chose to close their offices and work full-time at public hospitals. While 89 percent of public hospital physicians worked part-time in 2002, this figure dropped to 44 percent by 2006 (Ministry of Health, 2007). The rate continued to go down in following years and the government passed a new law which effectively banned dual-time practice in public hospitals in 2010. Other significant changes in this period in public hospitals were consolidation of some public hospitals as the ownership was transferred from different public authorities to the MoH , upgraded health information systems and investments on infrastructure. We expect the new payment scheme and its impact on the shift to full-time employ2
See MoH (2008) for details.
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Points 21 10 21 21 5 30 0 0 1280 2500 500 420 143 243 4
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Inpatient visits(two visits per day) Consultation Emergency outpatient exam Outpatient exam Referred outpatient exam Psychiatry exam(21 points after 10 patients) Electrocardiogram IM injection Valvotomy, mitral valve, closed Coronary by-pass, carotid endarterectomy, Splenectomy Appendectomy Natural Birth Cesacan Birth X-ray(two lungs, two direction)
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Procedure
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Table 1: Performance Evaluation
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Notes: MoH data. Minimum amount of operations required to earn points in parenthesis
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ment to be the main determinant of the increase in services provided in public hospitals. Incentivized to work full-time in public hospitals and paid by the number of services that they provide, we hypothesize that the shift in the employment status of the specialists is
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the main driver of the large increase in hospital outputs. Furthermore, we hypothesize that omitting this change from the analysis will lead to attribute the change in the vol-
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ume to other components of the reform, such as the technological innovations. Indeed, a previous study by Sulku (2011a) makes use of number of specialists without a full-time-
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equivalent adjustment and finds technology to be among the major determinants of the
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increase in hospital outputs.
Methodology
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To evaluate the change in hospital efficiency and its components we use data envelopment analysis (DEA). DEA is a commonly used method in health economics. It can be roughly
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described as finding an efficient production frontier given the inputs and outputs of pro-
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duction units and then assessing each unit’s performance relative to the frontier. Charnes, Cooper and Rhodes (1978) and Banker, Charnes and Cooper (1984) provide two early and distinct methods of calculating the efficiency frontier. The former assumes constant
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returns to scale, where output multiples at the same rate with the inputs and is more suited for perfectly competitive markets with no constraints on financing whereas in the latter the returns are variable and is preferred when competition is imperfect and there are financial constraints. As in Färe et al. (1994) and Sulku(2011a) we use a variable returns to scale method that is more appropriate for the health sector in Turkey. The method allows us to decompose the productivity change into global and local factors3 . A global change shifts the frontier for all units and changes the optimal size for each unit whereas local factors affect similarly sized units relative to their benchmarks4 . 3
Output oriented DEA specifies units that produce greater amounts of output compared to other units using the same amount of inputs as efficient. 4 See Cooper et al (2001) for a detailed presentation.
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When calculating hospital efficiency, variables such as number of specialists, nonspecialists, practitioners, nurses, beds or equipment can be considered as inputs whereas outpatient or inpatient visits and number of surgeries can be considered as outputs.
Data
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2006 into pure-efficiency change and a residual scale component5 .
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We use a Malmquist index to decompose the efficiency change over the period 2002-
For the study we make use of hospital surveys by Ministry of Health from 2003 and 2006. The surveys provide information on number of outpatient visits, inpatient days as
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well as surgeries. Following Sulku (2011a) we set provinces as the unit of observation. There are 81 provinces in Turkey. Those hospitals operated by universities and private
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hospitals are excluded. We make an adjustment for case-mix using a Roemer case-mix index. Hospitals are classified into groups by calculating the average length of stay per
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in-patient multiplied by the occupancy rate and divided by the sample average. Since
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the average length reflects the severity of cases it will be higher for those hospitals whose occupancy rate is higher than the average or vice versa. The analysis is, however, subject to some of the shortcomings of the index. For example, the length of stay may be a
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misleading indicator of case severity for a small percentage of public hospitals in Turkey which also act as research hospitals. The length of stay in these hospitals might be longer for education and research purposes. In Table 2, we provide summary statistics for the input and output variables in 2003 and 2006. A look at the output variables reveals the magnitude of the change from 2003 to 2006. Both inpatient and outpatient visits per province increased roughly by about 100% over a period of three years and the number of surgeries tripled. We also observe that the full-time equivalent specialists has increased 59% and practitioners have more
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See Appendix for the calculation we employ in this paper.
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8 Table 2: Data Summary
min
2003 average
Inpatient visits
1134408 9162793 116493 2340010 (1194639) (3094331) 1197 35856 243823 3092 65528 (34946) (85893) 302.6946 42135 438681 792.9346 79994 (56622) (142147) 241 11932 139822 1259 35696 (17745) (67828) 100 873 6264 110 1477 (826) (2074) 6 173 2072 24 291 (260) (607) 3 128 (262) 4 79 954 5 192 (111) (604)
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Inpatient visits(case-mix adjusted)
min
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75498
Outpatient visits
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2006 average
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Years
Surgeries
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Beds Specialists
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Part-time specialists
22757824 582988 1052960 385164 15824 4327 2125 4463
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Practitioners
max
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(Notes: Standard errors in parenthesis)
than doubled in the same period. The number of part-time specialists in 2003 is missing6 .
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Lacking exact figures, we only know that the ratio of full-time specialists in Turkey was 11 percent in early 2003. To estimate full-time equivalent number of specialists in provinces in 2003 we make use of this information and the available distribution of dualtime specialists across hospitals in 2006. We also consider an extreme case scenario in which all physicians are assumed to be dual-time in 2003 for comparison purposes. In all calculations dual-time specialists are considered to be equivalent to half-time specialist. A major cause for concern in estimation using DEA is sensitivity of the results to measurement error in inputs and outputs (Ramanathan 2003). That may be a concern the measurement of health workforce input. Physicians or nurses, for example, may 6
The Ministry of Health does not have full-time equivalent figures for the year 2003 at the hospital or provincial level but only for Turkey as a whole.
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work half-time or overtime and accordingly full-time equivalent figures should be used7 . Physicians or nurses, for example, may work half-time or overtime. Hence, the literature emphasizes use of full-time equivalent figures for workforce in order to avoid measurement
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error. Using full-time equivalent physician figures is even more important in Turkish case since a crucial component of the healthcare reforms in Turkey has been financial
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incentives for the specialists to induce a change from part-time to full-time service in
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public hospitals8 .
Results
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Table 3 presents the results according to the Malmquist decomposition. While columns display decomposition of the change in its components, rows show the assumptions re-
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garding full-time equivalent figure calculation. First set of rows use number of specialists as is, with no correction for dual-time employment, second set of rows provides another
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extreme where all specialists are assumed to be working dual-time in 2003. The last set
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of rows adopts official figure of 11 percent for full-time specialists and assumes that they are distributed across provinces according to the distribution of full-time specialists in 2006. Figures displayed in the Table are geometric mean, minimum and maximum over
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81 provinces. When full-time equivalence is disregarded we find a significant change in total factor productivity, which is caused partially by technological change and partially by efficiency change. About half of the efficiency change appears to be caused by scale effects and the other half by the pure efficiency change. Results are, to a large extent, in line with Sulku (2011a) who relates the component related to technological change to increased spending in infrastructure and scale effects to reorganization in hospital market. 7
The literature on DEA sensitivity to input and output measurement proposes a variety of methods to address the problem (see e.g. Charnes et al., 1985, Cooper et al., 2001). Also see Gajewski et al. (2012) for an example involving unobserved measurement error in nursing input 8 There have been other evaluations of hospital efficiency in Turkey such as Sahin and Ozcan (2000) and Bilsel and Davutyan (2014) but these did not perform an evaluation of the reforms. Ozcan et al. (2009) analyzed the changes in efficiency but for a later period, from 2005 to 2008.
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10 Table 3: Efficiency of Public Hospitals
TC
EC
PEC
SC
No distinction Geometric mean Minimum Maximum
1.29 0.33 2.29
1.12 1.16 0.29 0.66 1.73 1.46
1.08 0.31 1.80
1.07 0.65 1.46
2003 all part-time, 2006 as reported Geometric mean Minimum Maximum
1.07 0.26 1.82
0.96 1.12 0.28 0.65 1.73 1.42
1.06 0.30 1.72
1.06 0.60 1.48
2003 11% full time, 2006 as reported Geometric mean Minimum Maximum
1.18 0.35 2.26
1.02 1.16 0.31 0.64 1.90 1.42
1.13 0.32 1.99
1.02 0.70 1.35
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TFPC
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(TFPC: Total factor productivity change, TC: Technological change, EC:Efficiency change, PEC: Pure efficiency change, SC: Scale Change)
When we consider the two scenarios taking into account full-time equivalence, however,
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a different picture emerges. To begin with, the change in TFP is reduced from 1.29 to 1.18. This is mostly due to elimination of the increase in technological efficiency
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indicated by a drop from 1.12 to 1.02, and it suggests that the driving force behind the improvement is the pure efficiency change, which reflects the switch from part-time to full-time employment. Technological change plays no role in the overall improvement in efficiency. Furthermore, the effects of scale change are also largely reduced. Resulting change in TFP is almost totally due to an increase in pure efficiency, which is a measure of input performance devoid of any scale effects.
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Conclusion
Although Turkey has implemented a major reform program in its healthcare system, its evaluations are rather rare. Simultaneous changes that happened in various aspects of
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healthcare system and lack of adequate data make the assessment difficult. Our work attempts to uncover how public hospitals were able to face sharp increase in demand following more generous insurance schemes. Our results confirm the dramatic increase
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in outpatient and inpatient services provided by the public hospitals, but, unlike earlier literature, we find that the change from dual-practice to full-time employment as the
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main cause behind the change. Other than that scale economies and some increase in the productivity appear to be important.
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Our results show importance of the incentive schemes on physician behavior. There has been a switch from a salary-based system to a fee-for service system where full-time
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practice is favored over dual-time practice. This resulted in a significant increase in the number of services by getting specialists to work full-time in public hospitals. This being
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said, it is likely that the new system is subject to some of the negative consequences of fee-for-service systems reported in the literature. Some aspects of quality such as visit times may have worsened. For example, in a survey among physicians conducted by
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Turkish Medical Association (2009) physicians state reduced visit times (67 percent) and
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reduced quality of care (60 percent) but increased prevalence of unethical practices (70 percent). Aktan et al. (2014) report an increase in unnecessary invasive practices with
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higher rewards and a decrease in the time spent on medical education. In primary care setting, Ocek et al. (2014) finds services not targeted by the incentive schemes, such as chronic disease management, to be neglected. Other aspects such as length of waiting times may have improved. Unfortunately data is lacking in this respect and it is not possible to evaluate accurately the impact of reform on quality. In fact this appears to be one of the major shortcomings in Turkish healthcare reforms, as comprehensive evaluations are lacking when it comes to health outcome due to lack of quality measures. This highlights the importance of generating data on all relevant aspects of health outcomes and incorporating a well-defined impact evaluation procedure into the reform process.
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Our work also points to the importance of measuring inputs correctly by using fulltime equivalent figures in efficiency analysis using DEA. When there is a change in the composition of the workforce omitting equivalency may lead to wrong conclusions.
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The excess demand for health care services resulting from healthcare reforms that ease access may require an increase in physician workforce which is achieved in Turkish case
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by an induced switch to full-time practice. Changing incentive schemes may also be a contributing factor in increasing the productivity of physicians but its impact on quality
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should be closely monitored.
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Agartan, T.I. (2007). Turkish health system in transition: Historical background and reform experience (Unpublished doctoral dissertation). State University of New York, New York.
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Aktan O., Pala K., Ilhan B. (2014). Health-care reform in Turkey: far from perfect (Correspondence). The Lancet, 383(9911):25?26.
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Aran, M., Hentschel, J. (2012). Protection in good and bad times? The Turkish green card health program. Policy Research Working Paper, no. 6178. Washington D.C.: World Bank.
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Atun, R., Aydın S., Chakraborty S., Sumer, S., Aran, M., Gurol, I., Akdağ, R. (2013). Universal health coverage in Turkey: enhancement of equity. The Lancet, 382(9886), 65-99.
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Banker R.D., Charnes R.F., Cooper W.W(1984) . Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30,1078?1092.
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Baris E, Mollahaliloglu S, Aydin S. 2011. Healthcare in Turkey: from laggard to leader. BMJ 342: 579-582
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Bilsel, M. and Davutyan, N. (2014). Hospital Efficiency with Mortality as Undesirable Output: the Turkish Case, Annals of Operations Research, 221(1): 73-88. Charnes A, Cooper WW, Rhodes E(1978) . Measuring the efficiency of decision making units. European Journal of Operation Research, 2(1):429?444.
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Charnes, A., W.W. Cooper, A.Y. Lewin, R.C. Morey, J.J. Rousseau (1985). Sensitivity and Stability Analysis in DEA. Annals of Operations Research 2 139-150. Cooper, W. W., Shanling Li, L. M. Seifo, Kaoru Tone, R. M. Thrall, J. Zhu. (2001). Sensitivity and Stability Analysis in DEA: Some Recent Developments. Journal of Productivity Analysis, 15(3): 217-246. Erus, B. and Aktakke, N. (2012) Impact of healthcare reforms on out-of-pocket health expenditures in Turkey for public insurees, European Journal of Health Economics, 13(3):337-346 Erus, B., Yakut-Cakar, Calı, S., Adaman, F. (2015). Health Policy for the Poor: An Exploration on the Take-Up of Means-Tested Health Benefits in Turkey, Social Science & Medicine, 130:99-106
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Gajewski, B.J., Lee, R., Dunton, N. (2012) Data Envelopment Analysis in the Presence of Measurement Error: Case Study from the National Database of Nursing Quality Indicators, Journal of Applied Statistics, 39(12):2639-53.
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González, P., Macho-Stadler, I. (2013). A theoretical approach to dual practice regulations in the health sector. Journal of Health Economics, 32(1): 66-87.
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Ministry of Health (2008). Performance Management in Health: Performance Based Supplementary Payment System, Ministry of Health, Ankara, Turkey. Ministry of Health (2012). Health Statistics Yearbook. Ministry of Health, Ankara, Turkey.
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Moghri, J., Arab, M., Rashidian, A., Akbari Sari, A. (2016). Physician dual practice: A descriptive mapping review of literature. Iranian Journal of Public Health, 45(3):278-288.
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Ocek, Z.A., Ciceklioglu, M., Yucel, U., and Ozdemir, R. (2014). Family medicine model in Turkey: a qualitative assessment from the perspectives of primary care workers. BMC Family Practice, 15, 38. doi:10.1186/1471-2296-15-38.
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Ökem Z.G., Çakar M. (2015). What have health care reforms achieved in Turkey? An appraisal of the "Health Transformation Programme", Health Policy, 119(9):1153-63. OECD/World Bank. (2008). OECD Reviews of Health Systems: Turkey. Paris: OECD Publishing.
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Ramanathan, R. (2003) An Introduction to data envelopment analysis: a tool for performance measurement. Sage Publications, London. Ruger, J.P.(2005). The changing role of the World Bank in global health. American Journal of Public Health, 95(1):60-70. Sahin, I., and Ozcan, Y.A. (2000). Public sector hospital efficiency for provincial markets in Turkey. Journal of Medical Systems, 24(6):307-320. Sahin, I., Ozcan, Y. A., and Ozgen, H. (2011). Assessment of hospital efficiency under health transformation program in Turkey. Central European Journal of Operations Research, 19(1):19-37. Sulku, S.N. (2011a) The health sector reforms and the efficiency of public hospitals in Turkey: provincial markets, The European Journal of Public Health, 22(5) 634-8.
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Sulku, S.N. (2011b) Turkiye’de Sağlıkta Donuşum Programı Öncesi ve Sonrasında Sağlık Hizmetlerinin Sunumu, Finansmanı ve Sağlık Harcamaları. Maliye Bakanlığı Strateji Geliştirme Başkanlığı, Ankara.
Appendix
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8
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Yenimahalleli-Yaşar, G. (2011). ‘Health transformation programme’ in Turkey: an assessment. International Journal of Health Planning and Management, 26(2), 110-133.
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Let y t denote the outputs and xt denote the inputs at time t. Then an efficiency matrix can be defined as Dt (xt+1 , y t+1 ) = inf(θ : (xt+1 , y t+1 /θ) ∈ S t )
(1)
d
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where inf denotes the largest quantity that is less than or equal to each of a given set of quantities. D denotes the efficiency metric which assigns a distance between the output vector y t and the inputs xt at time t. S t is the production technology set or the set of all feasible input-output vectors and θ is a scalar.Dt can be thought of a distance function that measures the maximal proportional change in outputs such that (xt+1 , y t + 1) is feasible in relation to the technology at t. S t can be specified as constant or variable returns to scale 9 . For the decomposition we utilize a Malmquist index given by #1
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Dt (xt+1 , y t+1 ) Dt (xt , y t ) Dt+1 (xt+1 , y t+1 ) × ( )( ) M (xt+1 , y t+1 , xt , y t ) = Dt (xt , y t ) Dt+1 (xt+1 , y t+1 ) Dt+1 (xt , y t ) "
2
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(2)
In (2) the ratio outside the brackets computes the change of relative efficiency from t until t+1. The geometric mean of the term inside the brackets captures two components of t+1 t+1 ,y t+1 ) the technological shift: D D(x captures the efficiency change and t (xt ,y t ) Dt (xt+1 ,y t+1 )
Dt (xt ,y t )
[( Dt+1 (xt+1 ,yt+1 ) )( Dt+1 (xt ,yt ) )
#1 2
captures technical change. Note that if xt = xt+1 and y t = y t+1
the index gives 1 which indicates no change. Improvements in productivity yields numbers greater than unity and deteriorations results in index which is lower than unity.
9
See Färe et al. (1994) for details
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S0168-8510(17)30055-6 http://dx.doi.org/doi:10.1016/j.healthpol.2017.02.012 HEAP 3699
To appear in:
Health Policy
Received date: Revised date: Accepted date:
9-3-2016 13-2-2017 21-2-2017
Please cite this article as: Burcay Erus, Ozan Hatipoglu, Physician Payment Schemes and Physician Productivity: Analysis of Turkish Healthcare Reforms, (2017), http://dx.doi.org/10.1016/j.healthpol.2017.02.012 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
*Title Page (showing Author Details)
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Burcay Erus †and Ozan Hatipoglu‡
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Physician Payment Schemes and Physician Productivity: Analysis of Turkish Healthcare Reforms∗
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We would like to thank the editor and two anonymous referees for very helpful comments and Ertunc Aydogdu for research assistance. We acknowledge financial support from Scientific and Technical Research Council of Turkey, Project Nr 108K455 † Department of Economics, Bogazici University, Bebek, Istanbul 34342 Turkey. [email protected]. Phone:+902123597638 ‡ Corresponding Author. Department of Economics, Bogazici [email protected].
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*Highlights (for review)
Highlights - This paper attempts to uncover the causes of the increase in public hospital services after the implementation of a major Turkish Health reform program. - It employs data envelopment analysis to decompose the increase in efficiency into technological and scale components.
- The role of technological change and scale economies are found to be limited
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- We find that the shift from dual-practice to full-time employment at public hospitals by the physicians is the main cause behind the dramatic increase in outpatient and inpatient services provided by the public hospitals.
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*Manuscript (without Author Details) Click here to view linked References
Abstract
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Physician Payment Schemes and Physician Productivity: Analysis of Turkish Healthcare Reforms
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Following healthcare reforms in Turkey, inpatient and outpatient care provided in public hospitals more than doubled from 2003 to 2006. An important component of the reforms has been a shift from a salary based physician compensation scheme
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to one where fee-for-service component is dominant. The change did not only incentivize physicians to provide a higher volume of services but also encouraged them
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to practice full-time, rather than dual-time, in public hospitals. Lacking figures on
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full-time equivalent figures at hospital level, earlier research used head-counts for physician workforce and found technological change and scale economies to be important determinants. We employ data envelopment analysis and find that, under
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plausible scenarios regarding the number of dual vs full-time physician numbers, most of the change in hospital services may be explained only by the shift to fulltime practice. Our estimations find the change in technology and scale economies to play a relatively minor role. JEL classification: I10, I11, I18, C33, C52
Keywords: Efficiency of public hospitals, health transformation program, data envelopment analysis, structural change, model evaluation, part-time work, data envelopment analysis, linear programming
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Introduction
Starting in 2003, Turkey initiated reforms titled Health Transformation Programme (HTP) aiming to increase access to healthcare services and to improve efficiency of health-
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care providers1 . Among other things, reforms resulted in significant increase in the use of inpatient and outpatient services in public hospitals. From 2003 to 2006, number of
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patients cared in public hospitals in Turkey more than doubled from 114 thousand to 254
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thousand (Ministry of Health (MOH) 2012). In this paper we analyze determinants of the change in hospital output using data envelopment analysis (DEA). We pay attention to a particular aspect of the reform, the change in the payment schemes to specialists in
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public hospitals and resulting shift from dual-time to full-time employment. According to the MoH statistics, 89 percent of specialists were employed part-time in public hospitals
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in 2002 and this figure dropped to 44 percent by 2006 as a result of a new payment scheme which favored full-time practice at public hospitals.
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Dual practice is a common practice in medical profession but research is rather limited
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(Moghri et al., 2016). Gonzales and Macho-Stadler (2013) analyze options available to the policy maker and show with a theoretical model that the switch may be harmful for healthcare services in certain instances.While our study is far from making a complete
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evaluation of the change it sheds light to the impact of the reform on the volume of healthcare services in the short run. It is of importance to better understand Turkish healthcare reforms since it is an example of health system reforms conducted with consultancy by World Bank. World Bank has been an active player in redesigning health policy in developing countries since 1980s (Ruger 2005). A number of countries adopted similar reforms and Turkish case has been lauded in some publications, such as Atun et al. (2013) for its success in 1
Okem and Cakar (2015) provide a review of empirical on Turkish healthcare reforms. See OECD/World Bank (2008), for an early description of the reforms and Agartan (2007), and Yenimahalleli-Yasar (2011), for critics. Atun et al. (2013) and responses to them provide a more recent discussion. On specific aspects of the reforms see Erus and Aktakke (2012), for impact on out-of pocket expenditures, Aran and Hentschel (2012), and Erus et al. (2015), for health insurance coverage.
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increasing access to healthcare. What has been missing in these analyses, however, are the mechanisms leading to observed outcomes, mostly because of data limitations. Here, we find that payment schemes to specialists and resulting shift from dual-time to full time
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practice plays an important role in hospital output. This finding is in contrast from previous research on Turkish healthcare reform which
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generally relate increased output to technological change. We argue that this results from the omission of full-time and part time distinction due to data limitations and we address
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the problem using different scenarios. Specifically we show that the results are sensitive to the way physician input is accounted for and that under certain plausible assumptions
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regarding number of part-time specialists in 2003, most of the gain in productivity is explained not by technical efficiency but by the change in the full-time equivalent number
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of physicians.
The article is organized as follows. Next section discusses reforms in public hospital
Background
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system in Turkey briefly. This is followed by the methodology and results.
Turkey went through a rigorous reform process in health care to improve access and
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efficiency starting in 2003. One of the main pillars of the reform was the unification of previously segregated public health insurance schemes with the objective of providing universal coverage. With the new scheme a larger portion of the population was covered for care at a significantly larger network of hospitals which started to include private ones. Number of healthcare services provided soon increased sharply and healthcare spending on patient treatment grew by 5.1 percent per year in real terms from 2003 to 2006 (Sulku 2011b). On the supply side, an important component of the reform process was the new physician payment scheme at public hospitals. The earlier system was largely salary based and a significant proportion of specialists worked dual-time, also operating their
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own private offices. With the reforms a new payment scheme, called Performance Based Supplementary Payment System (PBSPS), was instituted. In PBSPS each and every procedure performed by specialists are assigned points (see Table 1 below for an example
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of the points assigned to different procedures in 2006). The amount of supplementary payment is based on the points collected during the month by the specialist. Although
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there is a component in the system which adjusts the payment according to quality of the hospital, this mostly includes input measures, such as the number of physician offices or
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presence of certain diagnostic devices, but few externally verified outcome measure other than the result of an annual general patient survey about the hospital2 . These payments
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led to an increase of about 200% in specialist pay from 2002 to 2006 in real terms and in 2006 supplementary payment made up about two thirds of the average specialist income
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(World Bank/OECD 2008).
In addition to providing an incentive to increase productivity of physicians, new system also targeted the widespread practice of dual work in public hospitals. Dual-time
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specialists receive only 40% of the points for services that they provide and hence are
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entitled to significantly lower pay. As the new public health insurance scheme covered care at private hospitals but not at private offices, demand for these offices went down as
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well, and a large number of specialists chose to close their offices and work full-time at public hospitals. While 89 percent of public hospital physicians worked part-time in 2002, this figure dropped to 44 percent by 2006 (Ministry of Health, 2007). The rate continued to go down in following years and the government passed a new law which effectively banned dual-time practice in public hospitals in 2010. Other significant changes in this period in public hospitals were consolidation of some public hospitals as the ownership was transferred from different public authorities to the MoH , upgraded health information systems and investments on infrastructure. We expect the new payment scheme and its impact on the shift to full-time employ2
See MoH (2008) for details.
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Points 21 10 21 21 5 30 0 0 1280 2500 500 420 143 243 4
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Inpatient visits(two visits per day) Consultation Emergency outpatient exam Outpatient exam Referred outpatient exam Psychiatry exam(21 points after 10 patients) Electrocardiogram IM injection Valvotomy, mitral valve, closed Coronary by-pass, carotid endarterectomy, Splenectomy Appendectomy Natural Birth Cesacan Birth X-ray(two lungs, two direction)
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Procedure
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Table 1: Performance Evaluation
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Notes: MoH data. Minimum amount of operations required to earn points in parenthesis
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ment to be the main determinant of the increase in services provided in public hospitals. Incentivized to work full-time in public hospitals and paid by the number of services that they provide, we hypothesize that the shift in the employment status of the specialists is
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the main driver of the large increase in hospital outputs. Furthermore, we hypothesize that omitting this change from the analysis will lead to attribute the change in the vol-
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ume to other components of the reform, such as the technological innovations. Indeed, a previous study by Sulku (2011a) makes use of number of specialists without a full-time-
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equivalent adjustment and finds technology to be among the major determinants of the
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increase in hospital outputs.
Methodology
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To evaluate the change in hospital efficiency and its components we use data envelopment analysis (DEA). DEA is a commonly used method in health economics. It can be roughly
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described as finding an efficient production frontier given the inputs and outputs of pro-
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duction units and then assessing each unit’s performance relative to the frontier. Charnes, Cooper and Rhodes (1978) and Banker, Charnes and Cooper (1984) provide two early and distinct methods of calculating the efficiency frontier. The former assumes constant
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returns to scale, where output multiples at the same rate with the inputs and is more suited for perfectly competitive markets with no constraints on financing whereas in the latter the returns are variable and is preferred when competition is imperfect and there are financial constraints. As in Färe et al. (1994) and Sulku(2011a) we use a variable returns to scale method that is more appropriate for the health sector in Turkey. The method allows us to decompose the productivity change into global and local factors3 . A global change shifts the frontier for all units and changes the optimal size for each unit whereas local factors affect similarly sized units relative to their benchmarks4 . 3
Output oriented DEA specifies units that produce greater amounts of output compared to other units using the same amount of inputs as efficient. 4 See Cooper et al (2001) for a detailed presentation.
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When calculating hospital efficiency, variables such as number of specialists, nonspecialists, practitioners, nurses, beds or equipment can be considered as inputs whereas outpatient or inpatient visits and number of surgeries can be considered as outputs.
Data
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2006 into pure-efficiency change and a residual scale component5 .
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We use a Malmquist index to decompose the efficiency change over the period 2002-
For the study we make use of hospital surveys by Ministry of Health from 2003 and 2006. The surveys provide information on number of outpatient visits, inpatient days as
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well as surgeries. Following Sulku (2011a) we set provinces as the unit of observation. There are 81 provinces in Turkey. Those hospitals operated by universities and private
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hospitals are excluded. We make an adjustment for case-mix using a Roemer case-mix index. Hospitals are classified into groups by calculating the average length of stay per
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in-patient multiplied by the occupancy rate and divided by the sample average. Since
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the average length reflects the severity of cases it will be higher for those hospitals whose occupancy rate is higher than the average or vice versa. The analysis is, however, subject to some of the shortcomings of the index. For example, the length of stay may be a
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misleading indicator of case severity for a small percentage of public hospitals in Turkey which also act as research hospitals. The length of stay in these hospitals might be longer for education and research purposes. In Table 2, we provide summary statistics for the input and output variables in 2003 and 2006. A look at the output variables reveals the magnitude of the change from 2003 to 2006. Both inpatient and outpatient visits per province increased roughly by about 100% over a period of three years and the number of surgeries tripled. We also observe that the full-time equivalent specialists has increased 59% and practitioners have more
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See Appendix for the calculation we employ in this paper.
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8 Table 2: Data Summary
min
2003 average
Inpatient visits
1134408 9162793 116493 2340010 (1194639) (3094331) 1197 35856 243823 3092 65528 (34946) (85893) 302.6946 42135 438681 792.9346 79994 (56622) (142147) 241 11932 139822 1259 35696 (17745) (67828) 100 873 6264 110 1477 (826) (2074) 6 173 2072 24 291 (260) (607) 3 128 (262) 4 79 954 5 192 (111) (604)
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Inpatient visits(case-mix adjusted)
min
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75498
Outpatient visits
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2006 average
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Years
Surgeries
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Beds Specialists
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Part-time specialists
22757824 582988 1052960 385164 15824 4327 2125 4463
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Practitioners
max
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(Notes: Standard errors in parenthesis)
than doubled in the same period. The number of part-time specialists in 2003 is missing6 .
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Lacking exact figures, we only know that the ratio of full-time specialists in Turkey was 11 percent in early 2003. To estimate full-time equivalent number of specialists in provinces in 2003 we make use of this information and the available distribution of dualtime specialists across hospitals in 2006. We also consider an extreme case scenario in which all physicians are assumed to be dual-time in 2003 for comparison purposes. In all calculations dual-time specialists are considered to be equivalent to half-time specialist. A major cause for concern in estimation using DEA is sensitivity of the results to measurement error in inputs and outputs (Ramanathan 2003). That may be a concern the measurement of health workforce input. Physicians or nurses, for example, may 6
The Ministry of Health does not have full-time equivalent figures for the year 2003 at the hospital or provincial level but only for Turkey as a whole.
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work half-time or overtime and accordingly full-time equivalent figures should be used7 . Physicians or nurses, for example, may work half-time or overtime. Hence, the literature emphasizes use of full-time equivalent figures for workforce in order to avoid measurement
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error. Using full-time equivalent physician figures is even more important in Turkish case since a crucial component of the healthcare reforms in Turkey has been financial
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incentives for the specialists to induce a change from part-time to full-time service in
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public hospitals8 .
Results
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Table 3 presents the results according to the Malmquist decomposition. While columns display decomposition of the change in its components, rows show the assumptions re-
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garding full-time equivalent figure calculation. First set of rows use number of specialists as is, with no correction for dual-time employment, second set of rows provides another
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extreme where all specialists are assumed to be working dual-time in 2003. The last set
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of rows adopts official figure of 11 percent for full-time specialists and assumes that they are distributed across provinces according to the distribution of full-time specialists in 2006. Figures displayed in the Table are geometric mean, minimum and maximum over
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81 provinces. When full-time equivalence is disregarded we find a significant change in total factor productivity, which is caused partially by technological change and partially by efficiency change. About half of the efficiency change appears to be caused by scale effects and the other half by the pure efficiency change. Results are, to a large extent, in line with Sulku (2011a) who relates the component related to technological change to increased spending in infrastructure and scale effects to reorganization in hospital market. 7
The literature on DEA sensitivity to input and output measurement proposes a variety of methods to address the problem (see e.g. Charnes et al., 1985, Cooper et al., 2001). Also see Gajewski et al. (2012) for an example involving unobserved measurement error in nursing input 8 There have been other evaluations of hospital efficiency in Turkey such as Sahin and Ozcan (2000) and Bilsel and Davutyan (2014) but these did not perform an evaluation of the reforms. Ozcan et al. (2009) analyzed the changes in efficiency but for a later period, from 2005 to 2008.
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10 Table 3: Efficiency of Public Hospitals
TC
EC
PEC
SC
No distinction Geometric mean Minimum Maximum
1.29 0.33 2.29
1.12 1.16 0.29 0.66 1.73 1.46
1.08 0.31 1.80
1.07 0.65 1.46
2003 all part-time, 2006 as reported Geometric mean Minimum Maximum
1.07 0.26 1.82
0.96 1.12 0.28 0.65 1.73 1.42
1.06 0.30 1.72
1.06 0.60 1.48
2003 11% full time, 2006 as reported Geometric mean Minimum Maximum
1.18 0.35 2.26
1.02 1.16 0.31 0.64 1.90 1.42
1.13 0.32 1.99
1.02 0.70 1.35
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TFPC
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(TFPC: Total factor productivity change, TC: Technological change, EC:Efficiency change, PEC: Pure efficiency change, SC: Scale Change)
When we consider the two scenarios taking into account full-time equivalence, however,
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a different picture emerges. To begin with, the change in TFP is reduced from 1.29 to 1.18. This is mostly due to elimination of the increase in technological efficiency
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indicated by a drop from 1.12 to 1.02, and it suggests that the driving force behind the improvement is the pure efficiency change, which reflects the switch from part-time to full-time employment. Technological change plays no role in the overall improvement in efficiency. Furthermore, the effects of scale change are also largely reduced. Resulting change in TFP is almost totally due to an increase in pure efficiency, which is a measure of input performance devoid of any scale effects.
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Conclusion
Although Turkey has implemented a major reform program in its healthcare system, its evaluations are rather rare. Simultaneous changes that happened in various aspects of
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healthcare system and lack of adequate data make the assessment difficult. Our work attempts to uncover how public hospitals were able to face sharp increase in demand following more generous insurance schemes. Our results confirm the dramatic increase
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in outpatient and inpatient services provided by the public hospitals, but, unlike earlier literature, we find that the change from dual-practice to full-time employment as the
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main cause behind the change. Other than that scale economies and some increase in the productivity appear to be important.
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Our results show importance of the incentive schemes on physician behavior. There has been a switch from a salary-based system to a fee-for service system where full-time
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practice is favored over dual-time practice. This resulted in a significant increase in the number of services by getting specialists to work full-time in public hospitals. This being
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said, it is likely that the new system is subject to some of the negative consequences of fee-for-service systems reported in the literature. Some aspects of quality such as visit times may have worsened. For example, in a survey among physicians conducted by
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Turkish Medical Association (2009) physicians state reduced visit times (67 percent) and
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reduced quality of care (60 percent) but increased prevalence of unethical practices (70 percent). Aktan et al. (2014) report an increase in unnecessary invasive practices with
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higher rewards and a decrease in the time spent on medical education. In primary care setting, Ocek et al. (2014) finds services not targeted by the incentive schemes, such as chronic disease management, to be neglected. Other aspects such as length of waiting times may have improved. Unfortunately data is lacking in this respect and it is not possible to evaluate accurately the impact of reform on quality. In fact this appears to be one of the major shortcomings in Turkish healthcare reforms, as comprehensive evaluations are lacking when it comes to health outcome due to lack of quality measures. This highlights the importance of generating data on all relevant aspects of health outcomes and incorporating a well-defined impact evaluation procedure into the reform process.
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Our work also points to the importance of measuring inputs correctly by using fulltime equivalent figures in efficiency analysis using DEA. When there is a change in the composition of the workforce omitting equivalency may lead to wrong conclusions.
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The excess demand for health care services resulting from healthcare reforms that ease access may require an increase in physician workforce which is achieved in Turkish case
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by an induced switch to full-time practice. Changing incentive schemes may also be a contributing factor in increasing the productivity of physicians but its impact on quality
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should be closely monitored.
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Agartan, T.I. (2007). Turkish health system in transition: Historical background and reform experience (Unpublished doctoral dissertation). State University of New York, New York.
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Aktan O., Pala K., Ilhan B. (2014). Health-care reform in Turkey: far from perfect (Correspondence). The Lancet, 383(9911):25?26.
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Aran, M., Hentschel, J. (2012). Protection in good and bad times? The Turkish green card health program. Policy Research Working Paper, no. 6178. Washington D.C.: World Bank.
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Atun, R., Aydın S., Chakraborty S., Sumer, S., Aran, M., Gurol, I., Akdağ, R. (2013). Universal health coverage in Turkey: enhancement of equity. The Lancet, 382(9886), 65-99.
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Banker R.D., Charnes R.F., Cooper W.W(1984) . Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30,1078?1092.
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Bilsel, M. and Davutyan, N. (2014). Hospital Efficiency with Mortality as Undesirable Output: the Turkish Case, Annals of Operations Research, 221(1): 73-88. Charnes A, Cooper WW, Rhodes E(1978) . Measuring the efficiency of decision making units. European Journal of Operation Research, 2(1):429?444.
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Charnes, A., W.W. Cooper, A.Y. Lewin, R.C. Morey, J.J. Rousseau (1985). Sensitivity and Stability Analysis in DEA. Annals of Operations Research 2 139-150. Cooper, W. W., Shanling Li, L. M. Seifo, Kaoru Tone, R. M. Thrall, J. Zhu. (2001). Sensitivity and Stability Analysis in DEA: Some Recent Developments. Journal of Productivity Analysis, 15(3): 217-246. Erus, B. and Aktakke, N. (2012) Impact of healthcare reforms on out-of-pocket health expenditures in Turkey for public insurees, European Journal of Health Economics, 13(3):337-346 Erus, B., Yakut-Cakar, Calı, S., Adaman, F. (2015). Health Policy for the Poor: An Exploration on the Take-Up of Means-Tested Health Benefits in Turkey, Social Science & Medicine, 130:99-106
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Gajewski, B.J., Lee, R., Dunton, N. (2012) Data Envelopment Analysis in the Presence of Measurement Error: Case Study from the National Database of Nursing Quality Indicators, Journal of Applied Statistics, 39(12):2639-53.
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González, P., Macho-Stadler, I. (2013). A theoretical approach to dual practice regulations in the health sector. Journal of Health Economics, 32(1): 66-87.
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Ministry of Health (2008). Performance Management in Health: Performance Based Supplementary Payment System, Ministry of Health, Ankara, Turkey. Ministry of Health (2012). Health Statistics Yearbook. Ministry of Health, Ankara, Turkey.
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Moghri, J., Arab, M., Rashidian, A., Akbari Sari, A. (2016). Physician dual practice: A descriptive mapping review of literature. Iranian Journal of Public Health, 45(3):278-288.
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Ökem Z.G., Çakar M. (2015). What have health care reforms achieved in Turkey? An appraisal of the "Health Transformation Programme", Health Policy, 119(9):1153-63. OECD/World Bank. (2008). OECD Reviews of Health Systems: Turkey. Paris: OECD Publishing.
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Ramanathan, R. (2003) An Introduction to data envelopment analysis: a tool for performance measurement. Sage Publications, London. Ruger, J.P.(2005). The changing role of the World Bank in global health. American Journal of Public Health, 95(1):60-70. Sahin, I., and Ozcan, Y.A. (2000). Public sector hospital efficiency for provincial markets in Turkey. Journal of Medical Systems, 24(6):307-320. Sahin, I., Ozcan, Y. A., and Ozgen, H. (2011). Assessment of hospital efficiency under health transformation program in Turkey. Central European Journal of Operations Research, 19(1):19-37. Sulku, S.N. (2011a) The health sector reforms and the efficiency of public hospitals in Turkey: provincial markets, The European Journal of Public Health, 22(5) 634-8.
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Sulku, S.N. (2011b) Turkiye’de Sağlıkta Donuşum Programı Öncesi ve Sonrasında Sağlık Hizmetlerinin Sunumu, Finansmanı ve Sağlık Harcamaları. Maliye Bakanlığı Strateji Geliştirme Başkanlığı, Ankara.
Appendix
cr
8
ip t
Yenimahalleli-Yaşar, G. (2011). ‘Health transformation programme’ in Turkey: an assessment. International Journal of Health Planning and Management, 26(2), 110-133.
us
Let y t denote the outputs and xt denote the inputs at time t. Then an efficiency matrix can be defined as Dt (xt+1 , y t+1 ) = inf(θ : (xt+1 , y t+1 /θ) ∈ S t )
(1)
d
M
an
where inf denotes the largest quantity that is less than or equal to each of a given set of quantities. D denotes the efficiency metric which assigns a distance between the output vector y t and the inputs xt at time t. S t is the production technology set or the set of all feasible input-output vectors and θ is a scalar.Dt can be thought of a distance function that measures the maximal proportional change in outputs such that (xt+1 , y t + 1) is feasible in relation to the technology at t. S t can be specified as constant or variable returns to scale 9 . For the decomposition we utilize a Malmquist index given by #1
te
Dt (xt+1 , y t+1 ) Dt (xt , y t ) Dt+1 (xt+1 , y t+1 ) × ( )( ) M (xt+1 , y t+1 , xt , y t ) = Dt (xt , y t ) Dt+1 (xt+1 , y t+1 ) Dt+1 (xt , y t ) "
2
Ac ce p
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65
(2)
In (2) the ratio outside the brackets computes the change of relative efficiency from t until t+1. The geometric mean of the term inside the brackets captures two components of t+1 t+1 ,y t+1 ) the technological shift: D D(x captures the efficiency change and t (xt ,y t ) Dt (xt+1 ,y t+1 )
Dt (xt ,y t )
[( Dt+1 (xt+1 ,yt+1 ) )( Dt+1 (xt ,yt ) )
#1 2
captures technical change. Note that if xt = xt+1 and y t = y t+1
the index gives 1 which indicates no change. Improvements in productivity yields numbers greater than unity and deteriorations results in index which is lower than unity.
9
See Färe et al. (1994) for details
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