Performance Evaluation of Private Hospitals under the Health Transformation Initiative in Türkiye

Performance Evaluation of Private Hospitals under the Health Transformation Initiative in Türkiye | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Performance Evaluation of Private Hospitals under the Health Transformation Initiative in Türkiye Seher Nur Sülkü, Yağmur Tokatlıoğlu, Aziz Küçük, Alper Mortaş This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8376761/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aim This study examines the efficiency of Türkiye’s private healthcare sector, which has expanded significantly recent decades in parallel with global trends. Between 2002 and 2022, the number of private hospitals more than doubled, largely driven by the Health Transformation Program. Data and Methodology: We assess the technical efficiency of outpatient and inpatient care services provided by private hospitals in Türkiye through the application of stochastic frontier analysis (SFA). The unbalanced panel dataset includes 541 hospitals and covers the period from 2019 to 2023. The SFA models incorporate inputs such as the number of medical and non-medical personnel, the number of beds, and their interactions. Additionally, external factors—including the COVID-19 pandemic and the 2023 earthquake in southern Türkiye—are integrated into the models to evaluate their effects on the outputs of outpatient visits and inpatient discharges. Findings: The SFA results indicate that the average technical efficiencies of private hospitals were 46% for inpatient services and 60% for outpatient services. These results suggest a potential for improvement of 54% and 40%, respectively, through more efficient utilization of existing input bundles under current technological conditions. The efficiency averages show no significant variation across years, regions, hospital types, or sizes. The analysis further reveals that healthcare personnel play a critical role in the efficient delivery of healthcare services. Nurses, along with other medical and non-medical staff, appear to function as potential substitutes within private hospitals. Outpatient efficiency declined sharply in 2020, the adverse impact of the pandemic lessened in 2021. In contrast, inpatient services remained largely unaffected by COVID-19. The 2023 earthquake, however, substantially decreased efficiency across both service types, with outpatient care being most severely impacted. Finally, the production functions for both inpatient and outpatient services exhibit diminishing returns to scale, indicating that hospital expansion may outpace managerial capacity. Conclusion The findings offer valuable insights to both private sector managers and healthcare policy makers, supporting more informed decision-making and strategic planning in shaping the hospital sector. Health policy private hospital efficiency Türkiye stochastic frontier analysis Figures Figure 1 Introduction Private healthcare has become a pivotal actor in shaping healthcare delivery in many low- and middle-income countries over recent decades [ 1 ]. In Türkiye, the Health Transformation Programme (HTP), launched in 2003, facilitated significant expansion of the private healthcare sector. HTP reforms aimed to improve service quality, increase access, strengthen infrastructure, and enhance efficiency and sustainability. The introduction of Universal Health Insurance in 2008 eliminated the distinction between public and private treatment costs, allowing private hospital services to be reimbursed through agreements with the Social Security Institution (SSI). Between 2002 and 2008, the number of private hospitals increased by 47.6% to 400, and bed capacity grew by 69% to nearly 21,000 [ 2 ]. By 2022, private hospitals represented 37% of all hospitals in Türkiye, with 21% of total hospital beds [ 2 ]. Admissions to private hospitals increased over twelvefold in the last two decades, driven by private insurance coverage and SSI agreements [ 2 ]. To ensure financial viability, private hospitals contracted with SSI were initially permitted to charge up to 30% above official tariffs in 2008, later increasing to 200% maximum differential fees (SSI circular 2012/25). Costs exceeding statutory insurance limits can now be covered through supplementary or complementary insurance, supporting both hospital profitability and patient access. The Turkish private healthcare sector has demonstrated significant growth and plays a critical role in national healthcare delivery. While expansion has improved access and alleviated some public hospital burdens, workforce shortages, financial sustainability, and efficiency challenges remain [ 3 ]. Most studies on Turkish hospital efficiency focus on public facilities [ 4 – 8 ], with a very limited recent analyses of private hospitals [ 9 – 10 ]. Although Özgen Narcı et al. [ 9 ] and Yildiz et al. [ 10 ] consider both the public and private sectors, their studies are based on data from 2010 and 2012 respectively. Understanding private hospital efficiency is essential for informing policy, optimizing resources, and guiding regulators in shaping a comprehensive and effective healthcare system. At this point, we aim to contribute to the literature by analyzing the efficiency of private hospitals using the most recent data from the Turkish Ministry of Health for the period [2019–2023] through stochastic frontier analysis (SFA), which accounts for uncontrollable external factors such as the COVID-19 pandemic and the 2023 devastating earthquake in southern Türkiye. SFA is more suitable compared to Data Envelopment Approach for assessing the impact of external shocks on hospital performance [ 11 – 12 ]. This study is organized as follows: The next section outlines the methodology and describes the dataset. Thereafter, we present the empirical findings, and finally, followed by a discussion and concluding remarks in the final section. Methodology This study employs a stochastic frontier production model with a translog functional form, which generalizes the Cobb-Douglas specification [ 13 – 14 ] as shown in Eq. ( 1 ): $$\:{\text{l}\text{n}\text{y}}_{\text{i}\text{t}}={{\beta\:}}_{0}+\sum\:_{\text{j}=1}^{\text{h}}{{\beta\:}}_{\text{j}}\text{l}\text{n}{\text{x}}_{\text{j}\text{i}\text{t}}+\frac{1}{2}\sum\:_{\text{j}=1}^{\text{h}}\sum\:_{\text{k}=1}^{\text{h}}{{\beta\:}}_{\text{j}\text{k}}\text{l}\text{n}{\text{x}}_{\text{j}\text{i}\text{t}}\text{l}\text{n}{\text{x}}_{\text{k}\text{i}\text{t}}+({\text{v}}_{\text{i}\text{t}}-\:{\text{u}}_{\text{i}\text{t}})$$ 1 here \(\:{\text{y}}_{\text{i}\text{t}}\) is i th hospital’s output production in time period t, \(\:{\text{x}}_{\text{j}\text{i}\text{t}}\) is j th type input amount of the i th hospital in time period t, \(\:{\text{u}}_{\text{i}\text{t}}\) is an inefficiency component, \(\:{\text{u}}_{\text{i}\text{t}}\sim\:{\text{N}}^{+}(0,{{\sigma\:}}_{\text{u}}^{2})\) , that shows the quantity that is less than the maximum feasible production frontier and finally \(\:{\text{v}}_{\text{i}\text{t}}\) is an idiosyncratic error term, \(\:{\text{v}}_{\text{i}\text{t}}\sim\:\text{N}\:(0,{{\sigma\:}}_{\text{v}}^{2})\) . The model separates output deviations into inefficiency ( \(\:{\text{u}}_{\text{i}\text{t}}\) ) and random error ( \(\:{\text{v}}_{\text{i}\text{t}}\) ) components, allowing technical efficiency to be assessed relative to the maximum feasible production frontier. To address potential heteroscedasticity in the error terms, inefficiency is modeled as a function of observable external factors ( \(\:{\:\text{z}}_{\text{u},\text{i}\text{t}}\:\) ) beyond hospital management control, such as Ministry interventions or workforce age distribution [ 15 – 17 ]. In this study, panel data are treated as cross-sectional, enabling time-varying inefficiency estimation using time trends or dummies [ 18 – 19 ]. In SFA, the production frontier is estimated by the maximum likelihood estimation methodology and technical efficiency is then calculated as the ratio of actual to potential output, ranging from 0 to 1, with higher values indicating greater efficiency, \(\:{\text{T}\text{E}}_{\text{i}\text{t}}=\text{exp}\left(-{\text{u}}_{\text{i}\text{t}}\right)\) . Data Set Our study analyzes 541 private hospitals in Türkiye from 2019 to 2023, including 23 university-affiliated and 518 general private hospitals, using data from the Basic Health Statistics Module (T.S.I.M.) with Ministry of Health permission. Hospitals with fewer than 20 beds or 10 staff and branch hospitals were excluded. In Table 1 input, output and (in)efficiency variables, that are chosen according to the literature [ 20 – 21 ] and availability of data, are presented. Outputs include total outpatient visits and inpatient numbers adjusted by the case-mix index (C.M.I.) to account for service complexity, while inputs comprise physicians, nurses, other medical and non-medical staff, and bed numbers [ 22 – 23 ]. Inefficiency variables include provincial per capita income and the share of population over 65 are gathered from Turkish Statistical Institute [ 24 – 25 ] reflecting demand-driven effects on hospital efficiency [ 26 – 27 ]. Year dummies capture temporal effects with 2019 serving as the reference year, including the COVID-19 period (2020–2021), and a dummy variable represents the six provinces of southern Türkiye most affected by the 2023 earthquake. Table 1 Variables and descriptive statistics, unbalanced pooled data, 2018–2023 Variable Definition Mean Std. Dev. Min Max Output outpatient The number of outpatient visits within a year. 7853.6 5420.6 128 38004 inpatient The annual number of hospitalized cases (including both discharges and deaths) adjusted by CMI 26801.9 24889.4 4.1 192853.6 Case Mix Index (CMI) CMI Romer’s CMI 3.2 2.2 0.04 22.7 ALS Average length of stay 3.1 1.7 1 24.9 OR Occupancy Rate (%) 56.9 21.5 1.3 100 Input bed All type of hospital beds 106.1 68.6 20 810 physician Physicians including specialists and general practitioners 49.4 33.6 10 365 nurse Nurse 65.2 61.9 10 546 other medical Other medical staff such as health officer, health technician and pharmacist 80.2 66.2 10 1385 nonmedical Non-medical staff working outside health services 154.5 151.6 10 1553 Inefficiency Variables income Per capita income ( $ 1000) 10.5 4.0 2.9 17.1 65age Population rate over 65 years (%) 12.7 3.8 5.5 29.2 d2020 dummy variable of 2020 0.2 0.4 0 1 d2021 dummy variable of 2021 0.2 0.4 0 1 d2022 dummy variable of 2022 0.2 0.4 0 1 d2023 dummy variable of 2023 0.2 0.4 0 1 dearthquake dummy variable for earthquake 0.02 0.12 0 1 Note: Total Number of Observations: 2226, Total Number of Hospital: 541 (23 University Hospital, 518 General Hospital) Empirical Findings First, in order to select the appropriate model that best fits our pooled data we utilized the Likelihood Ratio (LR) test. As shown in Table 2 , the LR test indicated that the stochastic frontier estimation procedure is preferred to ordinary least square method. Thus, SFA model described in the methodology was applied separately to outpatient and inpatient service outputs. The estimation was conducted using STATA 17.0, implementing source codes provided by Kumbhakar et al. [ 19 ]. Robust standard errors were used to mitigate the impact of outliers and ensure the reliability of parameter estimates. Table 2 SFA: Estimated parameters Outpatient Inpatient constant 7.08 (0.00***) -4.48 (0.009***) Inputs ( \(\:{\widehat{{\beta\:}}}_{j}\) ): Direct Effect bed 2.79 (0.00***) physician 0.57 (0.02**) 0.95 (0.03**) nurse 0.18 (0.27) 0.86 (0.04**) othermedical 0.79 (0.00***) 1.55 (0.00***) nonmedical 0.23 (0.07*) -0.20 (0.27) Quadratic terms ( \(\:{\widehat{{\beta\:}}}_{jk}\:when\:k\ne\:j)\) bedxbed -0.17 (0.56) physicianxphysician -0.36 (0.00***) -0.34 (0.028**) nursexnurse 0.00 (0.94) -0.15 (0.33) othermedicalxothermedical -0.09 (0.048**) -0.16 (0.00***) nonmedicalxnonmedical 0.05 (0.09*) 0.08 (0.011**) Cross products ( \(\:{\widehat{{\beta\:}}}_{jk}\:when\:k\ne\:j)\) bedxphysician -0.20 (0.14) bedxnurse 0.12 (0.462) bedxothermedical -0.25 (0.000**) bedxnonmedical 0.00 (0.176) physicianxnurse 0.23 (0.00***) 0.14 (0.15) physicianxothermedical 0.02 (0.7) 0.05 (0.40) physicianxnonmedical 0.005 (0.882) 0.09 (0.057*) nursexothermedical -0.08 (0.049**) -0.04 (0.48) nursexnonmedical -0.11 (0.00**) -0.19 (0.00***) othermedicalxnonmedical 0.00 (0.956) 0.08 (0.03**) Inefficiency effects \(\:\left({\widehat{{\delta\:}}}_{m}\right)\) constant -1.65 (0.00***) -1.27 (0.00***) income 0.09 (0.00***) 0.03 (0.00***) 65age -0.004 (0.78) 0.02 (0.04**) d2020 0.47 (0.00***) 0.22 (0.11) d2021 0.16 (0.23) 0.04 (0.80) d2022 -0.001 (0.99) -0.007 (0.95) d2023 0.30 (0.02***) 0.24 (0.09*) dearthquake 1.42 (0.00***) 0.25 (0.00***) Log Likelihood -1511 -2586 LR Test n + 613*** 2226 768*** 2226 Note: *** significant at the 0.01 level, ** significant at the 0.05 level, * significant at the 0.10 level , + unbalanced pooled data for the total number of 541 private hospitals over the [2019, 2023] period. The estimated coefficients of the input variables ( \(\:{\widehat{{\beta\:}}}_{j}\) ) represent their direct influence on service production. For outpatient services, all labor inputs—including physicians, other medical staff, and non-medical staff—had positive and statistically significant effects, except for nurses ( \(\:{\widehat{{\beta\:}}}_{nurse}=0.18,\:p-val=0.27)\) . In inpatient services, all health personnel, except non-medical staff, had significant positive effects, with hospital bed capacity demonstrating the largest contribution ( \(\:{\widehat{{\beta\:}}}_{Bed}=2.79,\:p-val=0.00)\) . Quadratic terms for physician and other medical staff were negative for both outpatient and inpatient services, indicating decreasing marginal productivity, while the squared term for non-medical staff was positive, suggesting increasing productivity for this group. Cross-product coefficients provide insight into input interactions. In outpatient services, a 1% increase in physicians increased the need for nurses by 0.23% (p < 0.01), indicating complementarity, whereas nurses acted as substitutes for other medical and non-medical staff. Similarly, in inpatient services, nurses were substitutes for non-medical staff, while physicians and non-medical staff were complementary. A 1% increase in hospital beds reduced the requirement for other medical staff by 0.25%. These findings underscore the complex interplay between workforce composition and hospital output. We also examined the impact of inefficiency factors on hospital service production. Positive associations with the inefficiency term ( \(\:{\text{u}}_{\text{i}\text{t}}\) ) indicate a reduction in hospital efficiency. Higher provincial income significantly increased inefficiency in both outpatient ( \(\:{\widehat{{\delta\:}}}_{income}^{outpatient}=0.09\) , p < 0.01) and inpatient services ( \(\:{\widehat{{\delta\:}}}_{income}^{inpatient}=0.03\) , \(\:p\) < 0.01). The proportion of the population over 65 significantly increased inefficiency in inpatient services, reflecting the additional demand for complex care. Year dummies captured temporal shocks, including the COVID-19 pandemic and the 2023 earthquake. Outpatient efficiency declined significantly in 2020 ( \(\:{\widehat{{\delta\:}}}_{d2020}^{outpatient}=0.47\) , \(\:p\) < 0.01), but pandemic effects mitigated in 2021 ( \(\:{\widehat{{\delta\:}}}_{d2021}^{outpatient}=0.16\) , \(\:p=0.23\) ). In contrast, inpatient services were largely unaffected by COVID-19. The 2023 earthquake significantly reduced efficiency for both service types, with outpatient services experiencing the most pronounced impact. Following the SFA estimation, technical efficiency scores were calculated for each hospital. Average efficiency was 60% for outpatient and 46% for inpatient services, indicating potential improvements of 40% and 54%, respectively, through better use of existing inputs and technology (Please see Fig. 1 ). Efficiency averages were largely consistent across years, hospital types, regions, and sizes. 1 In our study, after we derived the estimation for Eq. ( 1 ) via SFA, the output elasticities of each of the input variables, \(\:{\text{x}}_{\text{j}}\) , at their mean are calculated, \(\:{\text{e}}_{\text{j}}=\frac{\partial\:\text{l}\text{n}\text{y}}{\partial\:{\text{l}\text{n}\text{x}}_{\text{j}}}={\widehat{{\beta\:}}}_{\text{j}}+\sum\:_{\text{j}}{\widehat{{\beta\:}}}_{\text{j}\text{k}}\text{l}\text{n}{\text{x}}_{\text{j}}\) , then, we estimated the returns to scales ( RTS ) for both inpatient and outpatient production by summing up these input elasticities. Depending on whether this estimate is greater than, equal to, or less than one, the RTS will be increasing, constant, or decreasing, respectively. According to our findings, the sum of elasticities indicates decreasing returns to scale for both outpatient (0.75) and inpatient services (0.91), meaning a 1% increase in all inputs raises outpatient services by only 0.75% and inpatient services by 0.91%. Decreasing RTS occurs when a private hospital grows beyond its capacity to be effectively managed, resulting in less efficient production as its size increases. Discussion and Conclusion In this study, we evaluate the technical efficiency of private hospitals in Türkiye for the period 2019–2023 using stochastic frontier analysis (SFA), explicitly accounting for the impacts of the COVID-19 pandemic and the 2023 earthquake. The SFA estimation results provide important insights into the production dynamics, factor substitution patterns, and inefficiency determinants of private hospitals in Türkiye. The estimated output elasticities indicate that labor inputs remain central to hospital service production, consistent with the labor-intensive nature of healthcare. In our analysis it is found out that for outpatient services, nurses have a positive but statistically insignificant effect, while non-medical staff plays a significant role. This situation suggests that outpatient care services in private hospitals are more physician-focused and procedure-oriented, and that while nursing roles are important, they do not directly lead to a measurable increase in output volume. Marginal productivity declines when the number of physicians or other medical staff becomes very high. An increase in doctors raises the demand for nurses, whereas more nurses reduce the need for other medical and non-medical staff, showing that nurses can substitute for some personnel roles. Similarly, increasing the number of beds may reduce the need for other medical staff but increase the demand for nurses. These observed patterns highlight the strategic importance of nurses, whose versatile skills—from patient registration to direct medical care—make them central to private hospital operations [ 28 ]. Our SFA results indicate that doctors, nurses, and other medical staff positively contribute to inpatient service production as like Goudarzi et al., [ 29 ]; Sülkü et al., [ 30 ]. All medical personnel, primarily physicians and other medical staff (excluding non-medical personnel), contribute significantly to inpatient care. This is consistent with the multidisciplinary nature of inpatient care. When these relationships are evaluated as a whole, it becomes clear that the workforce composition in private hospitals is critically important in terms of efficiency and that personnel distribution must be strategically planned. We found out that external factors also critically influence the technical efficiency of private hospitals. Surprisingly, higher provincial income is associated with increased inefficiency, likely because private hospitals are concentrated in major cities—Ankara, Istanbul, and Izmir—with uneven distribution of beds and longer waiting times in Istanbul [ 31 – 34 ]. It is seen that inpatient efficiency improves when a larger proportion of the population is aged 65 or above, reflecting higher demand for complex care, consistent with previous research highlighting the positive association between aging populations and hospital utilization [ 35 ]. Moreover, outpatient service efficiency declined sharply in 2020; however, the negative effects of the pandemic eased in 2021. In contrast, inpatient services showed only minimal disruption from COVID-19. The 2023 earthquake, however, caused a substantial drop in efficiency across both service types, with outpatient care being the most severely affected. This finding reflects the widespread disruption of healthcare delivery systems, damage to infrastructure, population displacement, and shifts in service priorities following the disaster. Furthermore, our study also identifies diminishing returns to scale: increasing physician numbers does not proportionally increase patient numbers, suggesting inefficiencies arise when hospitals grow beyond an optimal size. Technical efficiency levels—60% for outpatient care and 46% for inpatient care—indicate a potential for improvement of 40% and 54%, respectively, with current inputs and technology. The fact that efficiency does not show significant differences across years, regions, hospital types, and scales suggests that inefficiency has a systematic structure and stems from managerial or organizational constraints. In this study, we focus on the technical efficiency of the private hospital sector, while recognizing that these institutions operate with a profit motive. Consequently, a limitation of our analysis is the inability to assess cost-effectiveness or profitability due to data constraints. Nevertheless, given the private sector’s critical role in healthcare provision, our study evaluating its efficiency from multiple perspectives provides valuable insights for policymakers to design informed strategies and improve the overall healthcare system. Inefficiencies in private hospitals in Türkiye are considered to stem from misallocation of resources, external shocks, demographic pressures, and limitations in management capacity. Hence, to increase efficiency, it is recommended to optimize the workforce composition, strengthen management capacity, increase institutional resilience against shocks such as disasters and pandemics, and reorganize the service structure according to the changing needs of the population. Declarations Competing interests The authors declare no competing interests. Ethics declaration Not applicable. Funding No funding was received for this study. Author Contribution All authors contributed equally, read, and approved the final version of the manuscript. Acknowledgement The extended version of this publication is included as a chapter in the book “Transforming Health in Turkey: An Evaluation of Two Decades of Reform” (edited by Dilek Başar and Selcen Öztürk), published by Routledge. This book was a closed-access publication, however we, the chapter authors are granted with open-access journal publication from Routledge for the shortened version in the journal BMC Health Services Research. Data Availability This study utilizes data from the Basic Health Statistics Module (T.S.I.M.), obtained with permission from the Ministry of Health. The dataset covers 541 private hospitals in Türkiye between 2019 and 2023, comprising 23 university-affiliated private hospitals and 518 general private hospitals. References Sriram V, Yilmaz V, Kaur S, Andres C, Cheng M, Meessen B. The role of private healthcare sector actors in health service delivery and financing policy processes in low- and middle-income countries: A scoping review. BMJ Global Health. 2024;8(Suppl 5):e013408. https://doi.org/10.1136/bmjgh-2023-013408 . Ministry of Health of Türkiye. Health Statistics Yearbook-2022. Ankara: Republic of Türkiye Ministry of Health; 2024. Sülkü SN, Tokatlıoğlu Y. Out of pocket healthcare expenditures in Türkiye under universal health coverage. In: Sülkü SN, Tokatlıoğlu Y, editors. The future of universal health insurance. Springer; 2024. pp. 99–130. https://doi.org/10.52305/HFBG4622 . İlgün G, Konca M. Assessment of efficiency levels of training and research hospitals in Turkey and the factors affecting their efficiencies. Health Policy Technol. 2019;8(4):343–8. https://doi.org/10.1016/j.hlpt.2019.08.008 . Şahin B, İlgün G. Assessment of the impact of public hospital associations (PHAs) on the efficiency of hospitals under the ministry of health in Turkey with data envelopment analysis. Health Care Manag Sci. 2019;22:437–46. https://doi.org/10.1007/s10729-018-9463-5 . Manavgat G, Demirci A. Decentralization matter of healthcare and effect on regional healthcare efficiency: Evidence from Turkey. Sosyoekonomi. 2020;28(44):261–81. https://doi.org/10.17233/sosyoekonomi.2020.02.12 . Bağcı H, Çil Koçyiğit S. Evaluating the decentralization of public hospitals in Turkey in terms of technical efficiency: Data envelopment analysis and Malmquist index. Benchmarking: Int J. 2023;30(10):4425–60. https://doi.org/10.1108/BIJ-03-2021-0140 . Sülkü SN, Mortaş A, Küçük A. Measuring efficiency of public hospitals under the impact of COVID-19: The case of Türkiye. Cost Eff Resource Allocation. 2023;21:70. https://doi.org/10.1186/s12962-023-00480-6 . Özgen Narcı H, Ozcan Y, Şahin İ, Tarcan M, Narcı M. An examination of competition and efficiency for hospital industry in Turkey. Health Care Manag Sci. 2015;18:407–18. https://doi.org/10.1007/s10729-014-9315-x . Yildiz MS, Heboyan V, Khan MM. Estimating technical efficiency of Turkish hospitals: implications for hospital reform initiatives. BMC Health Serv Res. 2018;18:401. https://doi.org/10.1186/s12913-018-3239-y . Hollingsworth B, Wildman J. The efficiency of health production: re-estimating the WHO panel data using parametric and non-parametric approaches to provide additional information. Health Econ. 2003;12(6):493–504. https://doi.org/10.1002/hec.751 . Yeşilyurt ME, Şahin E, Elbi MD, Kızılkaya A, Koyuncuoğlu MU, Akbaş-Yeşilyurt F. A novel method for computing single output for DEA with application in hospital efficiency. Socio-Economic Plann Sci. 2021;76:100995. https://doi.org/10.1016/j.seps.2020.100995 . Aigner D, Lovell CK, Schmidt P. Formulation and estimation of stochastic frontier production function models. J Econ. 1977;6(1):21–37. https://doi.org/10.1016/0304-4076(77)90052-5 . Meeusen W, Van den Broeck J. Efficiency estimation from Cobb-Douglas production functions with composed error. Int Econ Rev. 1977;18(2):435–44. https://doi.org/10.2307/2525757 . Caudill SB, Ford JM. Biases in frontier estimation due to heteroscedasticity. Econ Lett. 1993;41:17–20. Hadri K. Estimation of a doubly heteroscedastic stochastic frontier cost function. J Bus Economic Stat. 1999;17:359–63. Coelli TJ, Rao DSP, O’Donnell CJ, Battese GE. An introduction to efficiency and productivity analysis. 2nd ed. Springer; 2005. Zhang M. The comparison of stochastic frontier analysis with panel data models [master's thesis]. Loughborough University School of Business and Economics; 2012. Kumbhakar SC, Wang H, Horncastle AP. A practitioner’s guide to stochastic frontier analysis using Stata. Cambridge University Press; 2015. Asbu EZ, Masri MD, Al Naboulsi M. Determinants of hospital efficiency: A literature review. Int J Healthc. 2020;6(2). https://doi.org/10.5430/ijh.v6n2p44 . Mbau R, Musiega A, Nyawira L, Tsofa B, Mulwa A, et al. Analysing the efficiency of health systems: A systematic review of the literature. Appl Health Econ Health Policy. 2023;21(2):205–24. https://doi.org/10.1007/s40258-022-00785-2 . Roemer MI, Moustafa AT, Hopkins CE. A proposed hospital quality index: Hospital death rates adjusted for case severity. Health Serv Res. 1968;3(2):96–118. Sulku SN. The health sector reforms and the efficiency of public hospitals in Turkey: Provincial markets. Eur J Pub Health. 2012;22(5):634–8. https://doi.org/10.1093/eurpub/ckr163 . TURKSTAT. İl Bazında Gayrisafi Yurt İçi Hasıla. 2022. Turkish Statistical Institute; 2023. TURKSTAT. The Statistic Tables of Address Based Population Registration System. Turkish Statistical Institute; 2024. Folland S, Goodman AC, Stano M. The economics of health and health care. 5th ed. Pearson Prentice Hall; 2007. Santerre RE, Neun SP. Health economics: Theory, insights, and industry studies. 5th ed. South-Western Cengage Learning; 2010. Butler M, Schultz TJ, Drennan J. Substitution of nurses for physicians in the hospital setting for patient, process of care, and economic outcomes. Cochrane Database Syst Reviews. 2020;2020(5):CD013616. https://doi.org/10.1002/14651858.CD013616 . Goudarzi R, Pourreza A, Shokoohi M, Askari R, Mahdavi M, Moghri J. Technical efficiency of teaching hospitals in Iran: The use of stochastic frontier analysis, 1999–2011. Int J Health Policy Manage. 2014;3:91–7. https://doi.org/10.15171/ijhpm.2014.66 . Sulku SN, Tokatlioglu Y, Cosar K. Receiving or not deemed necessary healthcare services. BMC Public Health. 2023;23:208. https://doi.org/10.1186/s12889-023-15135-7 . Andersen R, Newman JF. Societal and individual determinants of medical care utilization in the United States. Milbank Q. 2005;83(4):1–28. https://doi.org/10.1111/j.1468-0009.2005.00428.x . Deloitte. Türkiye sağlık sektörü raporu: Sürdürülebilir ve kaliteli bir sağlık sektörü için genel bakış ve potansiyel iyileştirme alanları. YASED; 2012. Yıldırım HH, Konca M. Türkiye’de özel sağlık kurumları sektörü: Mevcut durum, sorunlar ve çözüm önerileri. TÜSPE Yayınları; 2018. Tokatlioglu Y, Sulku SN. The impact of the covid-19 outbreak on unmet health care needs in Istanbul. Prev Med Rep. 2023;36:102400. https://doi.org/10.1016/j.pmedr.2023.102400 . Rosenberg M, Tomioka S, Barber SL. Research to inform health systems’ responses to rapid population ageing. Health Res Policy Syst. 2022;20(Suppl 1):128. https://doi.org/10.1186/s12961-022-00917-z . Footnotes The distribution of the efficiency scores with respect to years, hospital types, region and sizes can be shared upon request from the corresponding author. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8376761","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":566161382,"identity":"fa094c32-7019-43d0-a224-b64166dbe0a1","order_by":0,"name":"Seher Nur Sülkü","email":"","orcid":"","institution":"Ankara Hacı Bayram Veli University","correspondingAuthor":false,"prefix":"","firstName":"Seher","middleName":"Nur","lastName":"Sülkü","suffix":""},{"id":566161383,"identity":"86049a98-bfcc-463d-a0a7-d2409b34bae7","order_by":1,"name":"Yağmur Tokatlıoğlu","email":"data:image/png;base64,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","orcid":"","institution":"Ankara Hacı Bayram Veli University","correspondingAuthor":true,"prefix":"","firstName":"Yağmur","middleName":"","lastName":"Tokatlıoğlu","suffix":""},{"id":566161384,"identity":"7ff6f939-c12e-43e2-97aa-22041d4cbbff","order_by":2,"name":"Aziz Küçük","email":"","orcid":"","institution":"Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Aziz","middleName":"","lastName":"Küçük","suffix":""},{"id":566161385,"identity":"c584f656-5403-4d17-abab-f030c5b6895f","order_by":3,"name":"Alper Mortaş","email":"","orcid":"","institution":"Ankara Hacı Bayram Veli University","correspondingAuthor":false,"prefix":"","firstName":"Alper","middleName":"","lastName":"Mortaş","suffix":""}],"badges":[],"createdAt":"2025-12-16 13:38:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8376761/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8376761/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99315732,"identity":"5b56df05-ce3e-4bee-a359-451c9f376a48","added_by":"auto","created_at":"2025-12-31 16:27:18","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":63918,"visible":true,"origin":"","legend":"","description":"","filename":"PrivateHospitals.docx","url":"https://assets-eu.researchsquare.com/files/rs-8376761/v1/d21977c913e100d6b66b60c9.docx"},{"id":99315119,"identity":"457d1924-29a8-4e37-b01d-b361ae771e9c","added_by":"auto","created_at":"2025-12-31 16:26:24","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6691,"visible":true,"origin":"","legend":"","description":"","filename":"f23636889fc34a1da204a4af9c9fd1e6.json","url":"https://assets-eu.researchsquare.com/files/rs-8376761/v1/eba1e77b23e4314d4ec0cfb1.json"},{"id":99144886,"identity":"0791d182-da7a-4f35-9411-f713fbf798f8","added_by":"auto","created_at":"2025-12-29 08:52:04","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":96840,"visible":true,"origin":"","legend":"","description":"","filename":"f23636889fc34a1da204a4af9c9fd1e61enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8376761/v1/a863d8839e5046a4ec5a3b68.xml"},{"id":99144883,"identity":"54d5fa7e-0bc8-490f-8510-a4a902a2b2c0","added_by":"auto","created_at":"2025-12-29 08:52:04","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6408,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8376761/v1/f0e35afdb87ad5b442eb9266.png"},{"id":99144888,"identity":"beb34e26-d9e2-441d-8ecc-4fc65f4e4107","added_by":"auto","created_at":"2025-12-29 08:52:05","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":95497,"visible":true,"origin":"","legend":"","description":"","filename":"f23636889fc34a1da204a4af9c9fd1e61structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8376761/v1/22e9479cf225286ae5de5e10.xml"},{"id":99144887,"identity":"421a98b4-d288-42f7-8203-fe611b274374","added_by":"auto","created_at":"2025-12-29 08:52:04","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":105481,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8376761/v1/aa07955d69e481a2afeed896.html"},{"id":99144889,"identity":"1514bf7f-db73-465d-89b0-162dd2f2d58a","added_by":"auto","created_at":"2025-12-29 08:52:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6653,"visible":true,"origin":"","legend":"\u003cp\u003eAverage technical efficiency\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8376761/v1/4ebbc52c522e04a19bb37104.png"},{"id":101754466,"identity":"568b47ab-66e5-40c7-8835-111c87f26969","added_by":"auto","created_at":"2026-02-03 10:42:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":617240,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8376761/v1/fb5a07ff-ce74-4c23-a58f-c47af2c590e8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Performance Evaluation of Private Hospitals under the Health Transformation Initiative in Türkiye","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePrivate healthcare has become a pivotal actor in shaping healthcare delivery in many low- and middle-income countries over recent decades [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In T\u0026uuml;rkiye, the Health Transformation Programme (HTP), launched in 2003, facilitated significant expansion of the private healthcare sector. HTP reforms aimed to improve service quality, increase access, strengthen infrastructure, and enhance efficiency and sustainability. The introduction of Universal Health Insurance in 2008 eliminated the distinction between public and private treatment costs, allowing private hospital services to be reimbursed through agreements with the Social Security Institution (SSI). Between 2002 and 2008, the number of private hospitals increased by 47.6% to 400, and bed capacity grew by 69% to nearly 21,000 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. By 2022, private hospitals represented 37% of all hospitals in T\u0026uuml;rkiye, with 21% of total hospital beds [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Admissions to private hospitals increased over twelvefold in the last two decades, driven by private insurance coverage and SSI agreements [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. To ensure financial viability, private hospitals contracted with SSI were initially permitted to charge up to 30% above official tariffs in 2008, later increasing to 200% maximum differential fees (SSI circular 2012/25). Costs exceeding statutory insurance limits can now be covered through supplementary or complementary insurance, supporting both hospital profitability and patient access.\u003c/p\u003e \u003cp\u003eThe Turkish private healthcare sector has demonstrated significant growth and plays a critical role in national healthcare delivery. While expansion has improved access and alleviated some public hospital burdens, workforce shortages, financial sustainability, and efficiency challenges remain [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Most studies on Turkish hospital efficiency focus on public facilities [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], with a very limited recent analyses of private hospitals [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Although \u0026Ouml;zgen Narcı et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and Yildiz et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] consider both the public and private sectors, their studies are based on data from 2010 and 2012 respectively.\u003c/p\u003e \u003cp\u003eUnderstanding private hospital efficiency is essential for informing policy, optimizing resources, and guiding regulators in shaping a comprehensive and effective healthcare system. At this point, we aim to contribute to the literature by analyzing the efficiency of private hospitals using the most recent data from the Turkish Ministry of Health for the period [2019\u0026ndash;2023] through stochastic frontier analysis (SFA), which accounts for uncontrollable external factors such as the COVID-19 pandemic and the 2023 devastating earthquake in southern T\u0026uuml;rkiye. SFA is more suitable compared to Data Envelopment Approach for assessing the impact of external shocks on hospital performance [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study is organized as follows: The next section outlines the methodology and describes the dataset. Thereafter, we present the empirical findings, and finally, followed by a discussion and concluding remarks in the final section.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis study employs a stochastic frontier production model with a translog functional form, which generalizes the Cobb-Douglas specification [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] as shown in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{\\text{l}\\text{n}\\text{y}}_{\\text{i}\\text{t}}={{\\beta\\:}}_{0}+\\sum\\:_{\\text{j}=1}^{\\text{h}}{{\\beta\\:}}_{\\text{j}}\\text{l}\\text{n}{\\text{x}}_{\\text{j}\\text{i}\\text{t}}+\\frac{1}{2}\\sum\\:_{\\text{j}=1}^{\\text{h}}\\sum\\:_{\\text{k}=1}^{\\text{h}}{{\\beta\\:}}_{\\text{j}\\text{k}}\\text{l}\\text{n}{\\text{x}}_{\\text{j}\\text{i}\\text{t}}\\text{l}\\text{n}{\\text{x}}_{\\text{k}\\text{i}\\text{t}}+({\\text{v}}_{\\text{i}\\text{t}}-\\:{\\text{u}}_{\\text{i}\\text{t}})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ehere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{y}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is i\u003csup\u003eth\u003c/sup\u003e hospital\u0026rsquo;s output production in time period t, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{x}}_{\\text{j}\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is j\u003csup\u003eth\u003c/sup\u003e type input amount of the i\u003csup\u003eth\u003c/sup\u003e hospital in time period t, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{u}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is an inefficiency component, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{u}}_{\\text{i}\\text{t}}\\sim\\:{\\text{N}}^{+}(0,{{\\sigma\\:}}_{\\text{u}}^{2})\\)\u003c/span\u003e\u003c/span\u003e, that shows the quantity that is less than the maximum feasible production frontier and finally \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{v}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is an idiosyncratic error term, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{v}}_{\\text{i}\\text{t}}\\sim\\:\\text{N}\\:(0,{{\\sigma\\:}}_{\\text{v}}^{2})\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe model separates output deviations into inefficiency (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{u}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e) and random error (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{v}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e) components, allowing technical efficiency to be assessed relative to the maximum feasible production frontier. To address potential heteroscedasticity in the error terms, inefficiency is modeled as a function of observable external factors (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:\\text{z}}_{\\text{u},\\text{i}\\text{t}}\\:\\)\u003c/span\u003e\u003c/span\u003e) beyond hospital management control, such as Ministry interventions or workforce age distribution [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, panel data are treated as cross-sectional, enabling time-varying inefficiency estimation using time trends or dummies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In SFA, the production frontier is estimated by the maximum likelihood estimation methodology and technical efficiency is then calculated as the ratio of actual to potential output, ranging from 0 to 1, with higher values indicating greater efficiency, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{T}\\text{E}}_{\\text{i}\\text{t}}=\\text{exp}\\left(-{\\text{u}}_{\\text{i}\\text{t}}\\right)\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Set\u003c/h2\u003e \u003cp\u003eOur study analyzes 541 private hospitals in T\u0026uuml;rkiye from 2019 to 2023, including 23 university-affiliated and 518 general private hospitals, using data from the Basic Health Statistics Module (T.S.I.M.) with Ministry of Health permission. Hospitals with fewer than 20 beds or 10 staff and branch hospitals were excluded. In Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e input, output and (in)efficiency variables, that are chosen according to the literature [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and availability of data, are presented. Outputs include total outpatient visits and inpatient numbers adjusted by the case-mix index (C.M.I.) to account for service complexity, while inputs comprise physicians, nurses, other medical and non-medical staff, and bed numbers [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Inefficiency variables include provincial per capita income and the share of population over 65 are gathered from Turkish Statistical Institute [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] reflecting demand-driven effects on hospital efficiency [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Year dummies capture temporal effects with 2019 serving as the reference year, including the COVID-19 period (2020\u0026ndash;2021), and a dummy variable represents the six provinces of southern T\u0026uuml;rkiye most affected by the 2023 earthquake.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables and descriptive statistics, unbalanced pooled data, 2018\u0026ndash;2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eOutput\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eoutpatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe number of outpatient visits within a year.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7853.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5420.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003einpatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe annual number of hospitalized cases (including both discharges and deaths) adjusted by CMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26801.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24889.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e192853.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eCase Mix Index (CMI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRomer\u0026rsquo;s CMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage length of stay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccupancy Rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eInput\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll type of hospital beds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e810\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ephysician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysicians including specialists and general practitioners\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e546\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother medical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther medical staff such as health officer, health technician and pharmacist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enonmedical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-medical staff working outside health services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eInefficiency Variables\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eincome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer capita income (\u003cspan\u003e$\u003c/span\u003e1000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation rate over 65 years (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ed2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edummy variable of 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ed2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edummy variable of 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ed2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edummy variable of 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ed2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edummy variable of 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edearthquake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edummy variable for earthquake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Total Number of Observations: 2226, Total Number of Hospital: 541 (23 University Hospital, 518 General Hospital)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Empirical Findings","content":"\u003cp\u003eFirst, in order to select the appropriate model that best fits our pooled data we utilized the Likelihood Ratio (LR) test. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the LR test indicated that the stochastic frontier estimation procedure is preferred to ordinary least square method. Thus, SFA model described in the methodology was applied separately to outpatient and inpatient service outputs. The estimation was conducted using STATA 17.0, implementing source codes provided by Kumbhakar et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Robust standard errors were used to mitigate the impact of outliers and ensure the reliability of parameter estimates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSFA: Estimated parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOutpatient\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eInpatient\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003econstant\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.08 (0.00***)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.48 (0.009***)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eInputs (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{{\\beta\\:}}}_{j}\\)\u003c/span\u003e\u003c/span\u003e): Direct Effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.79 (0.00***)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ephysician\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57 (0.02**)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95 (0.03**)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enurse\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18 (0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86 (0.04**)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eothermedical\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79 (0.00***)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55 (0.00***)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enonmedical\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23 (0.07*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.20 (0.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eQuadratic terms (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{{\\beta\\:}}}_{jk}\\:when\\:k\\ne\\:j)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebedxbed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.17 (0.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ephysicianxphysician\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.36 (0.00***)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.34 (0.028**)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enursexnurse\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00 (0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.15 (0.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eothermedicalxothermedical\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.09 (0.048**)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.16 (0.00***)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enonmedicalxnonmedical\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05 (0.09*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08 (0.011**)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCross products (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{{\\beta\\:}}}_{jk}\\:when\\:k\\ne\\:j)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebedxphysician\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.20 (0.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebedxnurse\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12 (0.462)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebedxothermedical\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.25 (0.000**)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebedxnonmedical\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00 (0.176)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ephysicianxnurse\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23 (0.00***)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14 (0.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ephysicianxothermedical\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05 (0.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ephysicianxnonmedical\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005 (0.882)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09 (0.057*)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enursexothermedical\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.08 (0.049**)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.04 (0.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enursexnonmedical\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.11 (0.00**)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.19 (0.00***)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eothermedicalxnonmedical\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00 (0.956)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08 (0.03**)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eInefficiency effects \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left({\\widehat{{\\delta\\:}}}_{m}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003econstant\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.65 (0.00***)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.27 (0.00***)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eincome\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09 (0.00***)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03 (0.00***)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e65age\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.004 (0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02 (0.04**)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ed2020\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47 (0.00***)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22 (0.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ed2021\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16 (0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04 (0.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ed2022\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001 (0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.007 (0.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ed2023\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.30 (0.02***)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24 (0.09*)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003edearthquake\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.42 (0.00***)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25 (0.00***)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLog Likelihood\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLR Test\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e613***\u003c/p\u003e \u003cp\u003e2226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e768***\u003c/p\u003e \u003cp\u003e2226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: \u003cem\u003e*** significant at the 0.01 level, ** significant at the 0.05 level, * significant at the 0.10 level\u003c/em\u003e, \u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eunbalanced pooled data for the total number of 541 private hospitals over the [2019, 2023] period.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe estimated coefficients of the input variables (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{{\\beta\\:}}}_{j}\\)\u003c/span\u003e\u003c/span\u003e) represent their direct influence on service production. For outpatient services, all labor inputs\u0026mdash;including physicians, other medical staff, and non-medical staff\u0026mdash;had positive and statistically significant effects, except for nurses (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{{\\beta\\:}}}_{nurse}=0.18,\\:p-val=0.27)\\)\u003c/span\u003e\u003c/span\u003e. In inpatient services, all health personnel, except non-medical staff, had significant positive effects, with hospital bed capacity demonstrating the largest contribution (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{{\\beta\\:}}}_{Bed}=2.79,\\:p-val=0.00)\\)\u003c/span\u003e\u003c/span\u003e. Quadratic terms for physician and other medical staff were negative for both outpatient and inpatient services, indicating decreasing marginal productivity, while the squared term for non-medical staff was positive, suggesting increasing productivity for this group.\u003c/p\u003e \u003cp\u003eCross-product coefficients provide insight into input interactions. In outpatient services, a 1% increase in physicians increased the need for nurses by 0.23% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating complementarity, whereas nurses acted as substitutes for other medical and non-medical staff. Similarly, in inpatient services, nurses were substitutes for non-medical staff, while physicians and non-medical staff were complementary. A 1% increase in hospital beds reduced the requirement for other medical staff by 0.25%. These findings underscore the complex interplay between workforce composition and hospital output.\u003c/p\u003e \u003cp\u003eWe also examined the impact of inefficiency factors on hospital service production. Positive associations with the inefficiency term (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{u}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e) indicate a reduction in hospital efficiency. Higher provincial income significantly increased inefficiency in both outpatient (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{{\\delta\\:}}}_{income}^{outpatient}=0.09\\)\u003c/span\u003e\u003c/span\u003e, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and inpatient services (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{{\\delta\\:}}}_{income}^{inpatient}=0.03\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003e \u0026lt; 0.01). The proportion of the population over 65 significantly increased inefficiency in inpatient services, reflecting the additional demand for complex care. Year dummies captured temporal shocks, including the COVID-19 pandemic and the 2023 earthquake. Outpatient efficiency declined significantly in 2020 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{{\\delta\\:}}}_{d2020}^{outpatient}=0.47\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003e \u0026lt; 0.01), but pandemic effects mitigated in 2021 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{{\\delta\\:}}}_{d2021}^{outpatient}=0.16\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p=0.23\\)\u003c/span\u003e\u003c/span\u003e). In contrast, inpatient services were largely unaffected by COVID-19. The 2023 earthquake significantly reduced efficiency for both service types, with outpatient services experiencing the most pronounced impact.\u003c/p\u003e \u003cp\u003eFollowing the SFA estimation, technical efficiency scores were calculated for each hospital. Average efficiency was 60% for outpatient and 46% for inpatient services, indicating potential improvements of 40% and 54%, respectively, through better use of existing inputs and technology (Please see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Efficiency averages were largely consistent across years, hospital types, regions, and sizes.\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn our study, after we derived the estimation for Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) via SFA, the output elasticities of each of the input variables, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{x}}_{\\text{j}}\\)\u003c/span\u003e\u003c/span\u003e, at their mean are calculated, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{e}}_{\\text{j}}=\\frac{\\partial\\:\\text{l}\\text{n}\\text{y}}{\\partial\\:{\\text{l}\\text{n}\\text{x}}_{\\text{j}}}={\\widehat{{\\beta\\:}}}_{\\text{j}}+\\sum\\:_{\\text{j}}{\\widehat{{\\beta\\:}}}_{\\text{j}\\text{k}}\\text{l}\\text{n}{\\text{x}}_{\\text{j}}\\)\u003c/span\u003e\u003c/span\u003e, then, we estimated the returns to scales (\u003cem\u003eRTS\u003c/em\u003e) for both inpatient and outpatient production by summing up these input elasticities. Depending on whether this estimate is greater than, equal to, or less than one, the \u003cem\u003eRTS\u003c/em\u003e will be increasing, constant, or decreasing, respectively. According to our findings, the sum of elasticities indicates decreasing returns to scale for both outpatient (0.75) and inpatient services (0.91), meaning a 1% increase in all inputs raises outpatient services by only 0.75% and inpatient services by 0.91%. Decreasing RTS occurs when a private hospital grows beyond its capacity to be effectively managed, resulting in less efficient production as its size increases.\u003c/p\u003e"},{"header":"Discussion and Conclusion","content":"\u003cp\u003eIn this study, we evaluate the technical efficiency of private hospitals in T\u0026uuml;rkiye for the period 2019\u0026ndash;2023 using stochastic frontier analysis (SFA), explicitly accounting for the impacts of the COVID-19 pandemic and the 2023 earthquake. The SFA estimation results provide important insights into the production dynamics, factor substitution patterns, and inefficiency determinants of private hospitals in T\u0026uuml;rkiye.\u003c/p\u003e \u003cp\u003eThe estimated output elasticities indicate that labor inputs remain central to hospital service production, consistent with the labor-intensive nature of healthcare. In our analysis it is found out that for outpatient services, nurses have a positive but statistically insignificant effect, while non-medical staff plays a significant role. This situation suggests that outpatient care services in private hospitals are more physician-focused and procedure-oriented, and that while nursing roles are important, they do not directly lead to a measurable increase in output volume. Marginal productivity declines when the number of physicians or other medical staff becomes very high. An increase in doctors raises the demand for nurses, whereas more nurses reduce the need for other medical and non-medical staff, showing that nurses can substitute for some personnel roles. Similarly, increasing the number of beds may reduce the need for other medical staff but increase the demand for nurses. These observed patterns highlight the strategic importance of nurses, whose versatile skills\u0026mdash;from patient registration to direct medical care\u0026mdash;make them central to private hospital operations [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur SFA results indicate that doctors, nurses, and other medical staff positively contribute to inpatient service production as like Goudarzi et al., [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]; S\u0026uuml;lk\u0026uuml; et al., [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. All medical personnel, primarily physicians and other medical staff (excluding non-medical personnel), contribute significantly to inpatient care. This is consistent with the multidisciplinary nature of inpatient care. When these relationships are evaluated as a whole, it becomes clear that the workforce composition in private hospitals is critically important in terms of efficiency and that personnel distribution must be strategically planned.\u003c/p\u003e \u003cp\u003eWe found out that external factors also critically influence the technical efficiency of private hospitals. Surprisingly, higher provincial income is associated with increased inefficiency, likely because private hospitals are concentrated in major cities\u0026mdash;Ankara, Istanbul, and Izmir\u0026mdash;with uneven distribution of beds and longer waiting times in Istanbul [\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. It is seen that inpatient efficiency improves when a larger proportion of the population is aged 65 or above, reflecting higher demand for complex care, consistent with previous research highlighting the positive association between aging populations and hospital utilization [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, outpatient service efficiency declined sharply in 2020; however, the negative effects of the pandemic eased in 2021. In contrast, inpatient services showed only minimal disruption from COVID-19. The 2023 earthquake, however, caused a substantial drop in efficiency across both service types, with outpatient care being the most severely affected. This finding reflects the widespread disruption of healthcare delivery systems, damage to infrastructure, population displacement, and shifts in service priorities following the disaster. Furthermore, our study also identifies diminishing returns to scale: increasing physician numbers does not proportionally increase patient numbers, suggesting inefficiencies arise when hospitals grow beyond an optimal size.\u003c/p\u003e \u003cp\u003eTechnical efficiency levels\u0026mdash;60% for outpatient care and 46% for inpatient care\u0026mdash;indicate a potential for improvement of 40% and 54%, respectively, with current inputs and technology. The fact that efficiency does not show significant differences across years, regions, hospital types, and scales suggests that inefficiency has a systematic structure and stems from managerial or organizational constraints.\u003c/p\u003e \u003cp\u003eIn this study, we focus on the technical efficiency of the private hospital sector, while recognizing that these institutions operate with a profit motive. Consequently, a limitation of our analysis is the inability to assess cost-effectiveness or profitability due to data constraints. Nevertheless, given the private sector\u0026rsquo;s critical role in healthcare provision, our study evaluating its efficiency from multiple perspectives provides valuable insights for policymakers to design informed strategies and improve the overall healthcare system. Inefficiencies in private hospitals in T\u0026uuml;rkiye are considered to stem from misallocation of resources, external shocks, demographic pressures, and limitations in management capacity. Hence, to increase efficiency, it is recommended to optimize the workforce composition, strengthen management capacity, increase institutional resilience against shocks such as disasters and pandemics, and reorganize the service structure according to the changing needs of the population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics declaration\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding was received for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed equally, read, and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eThe extended version of this publication is included as a chapter in the book \u0026ldquo;Transforming Health in Turkey: An Evaluation of Two Decades of Reform\u0026rdquo; (edited by Dilek Başar and Selcen \u0026Ouml;zt\u0026uuml;rk), published by Routledge.\u003c/p\u003e \u003cp\u003eThis book was a closed-access publication, however we, the chapter authors are granted with open-access journal publication from Routledge for the shortened version in the journal BMC Health Services Research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThis study utilizes data from the Basic Health Statistics Module (T.S.I.M.), obtained with permission from the Ministry of Health. The dataset covers 541 private hospitals in T\u0026uuml;rkiye between 2019 and 2023, comprising 23 university-affiliated private hospitals and 518 general private hospitals.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSriram V, Yilmaz V, Kaur S, Andres C, Cheng M, Meessen B. The role of private healthcare sector actors in health service delivery and financing policy processes in low- and middle-income countries: A scoping review. BMJ Global Health. 2024;8(Suppl 5):e013408. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmjgh-2023-013408\u003c/span\u003e\u003cspan address=\"10.1136/bmjgh-2023-013408\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Health of T\u0026uuml;rkiye. Health Statistics Yearbook-2022. Ankara: Republic of T\u0026uuml;rkiye Ministry of Health; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026uuml;lk\u0026uuml; SN, Tokatlıoğlu Y. Out of pocket healthcare expenditures in T\u0026uuml;rkiye under universal health coverage. In: S\u0026uuml;lk\u0026uuml; SN, Tokatlıoğlu Y, editors. The future of universal health insurance. Springer; 2024. pp. 99\u0026ndash;130. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.52305/HFBG4622\u003c/span\u003e\u003cspan address=\"10.52305/HFBG4622\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eİlg\u0026uuml;n G, Konca M. Assessment of efficiency levels of training and research hospitals in Turkey and the factors affecting their efficiencies. Health Policy Technol. 2019;8(4):343\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.hlpt.2019.08.008\u003c/span\u003e\u003cspan address=\"10.1016/j.hlpt.2019.08.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eŞahin B, İlg\u0026uuml;n G. Assessment of the impact of public hospital associations (PHAs) on the efficiency of hospitals under the ministry of health in Turkey with data envelopment analysis. Health Care Manag Sci. 2019;22:437\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10729-018-9463-5\u003c/span\u003e\u003cspan address=\"10.1007/s10729-018-9463-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManavgat G, Demirci A. Decentralization matter of healthcare and effect on regional healthcare efficiency: Evidence from Turkey. Sosyoekonomi. 2020;28(44):261\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17233/sosyoekonomi.2020.02.12\u003c/span\u003e\u003cspan address=\"10.17233/sosyoekonomi.2020.02.12\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBağcı H, \u0026Ccedil;il Ko\u0026ccedil;yiğit S. Evaluating the decentralization of public hospitals in Turkey in terms of technical efficiency: Data envelopment analysis and Malmquist index. Benchmarking: Int J. 2023;30(10):4425\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/BIJ-03-2021-0140\u003c/span\u003e\u003cspan address=\"10.1108/BIJ-03-2021-0140\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026uuml;lk\u0026uuml; SN, Mortaş A, K\u0026uuml;\u0026ccedil;\u0026uuml;k A. Measuring efficiency of public hospitals under the impact of COVID-19: The case of T\u0026uuml;rkiye. Cost Eff Resource Allocation. 2023;21:70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12962-023-00480-6\u003c/span\u003e\u003cspan address=\"10.1186/s12962-023-00480-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Ouml;zgen Narcı H, Ozcan Y, Şahin İ, Tarcan M, Narcı M. An examination of competition and efficiency for hospital industry in Turkey. Health Care Manag Sci. 2015;18:407\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10729-014-9315-x\u003c/span\u003e\u003cspan address=\"10.1007/s10729-014-9315-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYildiz MS, Heboyan V, Khan MM. Estimating technical efficiency of Turkish hospitals: implications for hospital reform initiatives. BMC Health Serv Res. 2018;18:401. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12913-018-3239-y\u003c/span\u003e\u003cspan address=\"10.1186/s12913-018-3239-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHollingsworth B, Wildman J. The efficiency of health production: re-estimating the WHO panel data using parametric and non-parametric approaches to provide additional information. Health Econ. 2003;12(6):493\u0026ndash;504. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/hec.751\u003c/span\u003e\u003cspan address=\"10.1002/hec.751\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeşilyurt ME, Şahin E, Elbi MD, Kızılkaya A, Koyuncuoğlu MU, Akbaş-Yeşilyurt F. A novel method for computing single output for DEA with application in hospital efficiency. Socio-Economic Plann Sci. 2021;76:100995. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.seps.2020.100995\u003c/span\u003e\u003cspan address=\"10.1016/j.seps.2020.100995\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAigner D, Lovell CK, Schmidt P. Formulation and estimation of stochastic frontier production function models. J Econ. 1977;6(1):21\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0304-4076(77)90052-5\u003c/span\u003e\u003cspan address=\"10.1016/0304-4076(77)90052-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeeusen W, Van den Broeck J. Efficiency estimation from Cobb-Douglas production functions with composed error. Int Econ Rev. 1977;18(2):435\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2525757\u003c/span\u003e\u003cspan address=\"10.2307/2525757\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaudill SB, Ford JM. Biases in frontier estimation due to heteroscedasticity. Econ Lett. 1993;41:17\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHadri K. Estimation of a doubly heteroscedastic stochastic frontier cost function. J Bus Economic Stat. 1999;17:359\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoelli TJ, Rao DSP, O\u0026rsquo;Donnell CJ, Battese GE. An introduction to efficiency and productivity analysis. 2nd ed. Springer; 2005.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang M. The comparison of stochastic frontier analysis with panel data models [master's thesis]. Loughborough University School of Business and Economics; 2012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumbhakar SC, Wang H, Horncastle AP. A practitioner\u0026rsquo;s guide to stochastic frontier analysis using Stata. Cambridge University Press; 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsbu EZ, Masri MD, Al Naboulsi M. Determinants of hospital efficiency: A literature review. Int J Healthc. 2020;6(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5430/ijh.v6n2p44\u003c/span\u003e\u003cspan address=\"10.5430/ijh.v6n2p44\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMbau R, Musiega A, Nyawira L, Tsofa B, Mulwa A, et al. Analysing the efficiency of health systems: A systematic review of the literature. Appl Health Econ Health Policy. 2023;21(2):205\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40258-022-00785-2\u003c/span\u003e\u003cspan address=\"10.1007/s40258-022-00785-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoemer MI, Moustafa AT, Hopkins CE. A proposed hospital quality index: Hospital death rates adjusted for case severity. Health Serv Res. 1968;3(2):96\u0026ndash;118.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSulku SN. The health sector reforms and the efficiency of public hospitals in Turkey: Provincial markets. Eur J Pub Health. 2012;22(5):634\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/eurpub/ckr163\u003c/span\u003e\u003cspan address=\"10.1093/eurpub/ckr163\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTURKSTAT. İl Bazında Gayrisafi Yurt İ\u0026ccedil;i Hasıla. 2022. Turkish Statistical Institute; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTURKSTAT. The Statistic Tables of Address Based Population Registration System. Turkish Statistical Institute; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFolland S, Goodman AC, Stano M. The economics of health and health care. 5th ed. Pearson Prentice Hall; 2007.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanterre RE, Neun SP. Health economics: Theory, insights, and industry studies. 5th ed. South-Western Cengage Learning; 2010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eButler M, Schultz TJ, Drennan J. Substitution of nurses for physicians in the hospital setting for patient, process of care, and economic outcomes. Cochrane Database Syst Reviews. 2020;2020(5):CD013616. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/14651858.CD013616\u003c/span\u003e\u003cspan address=\"10.1002/14651858.CD013616\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoudarzi R, Pourreza A, Shokoohi M, Askari R, Mahdavi M, Moghri J. Technical efficiency of teaching hospitals in Iran: The use of stochastic frontier analysis, 1999\u0026ndash;2011. Int J Health Policy Manage. 2014;3:91\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15171/ijhpm.2014.66\u003c/span\u003e\u003cspan address=\"10.15171/ijhpm.2014.66\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSulku SN, Tokatlioglu Y, Cosar K. Receiving or not deemed necessary healthcare services. BMC Public Health. 2023;23:208. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-023-15135-7\u003c/span\u003e\u003cspan address=\"10.1186/s12889-023-15135-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersen R, Newman JF. Societal and individual determinants of medical care utilization in the United States. Milbank Q. 2005;83(4):1\u0026ndash;28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1468-0009.2005.00428.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1468-0009.2005.00428.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeloitte. T\u0026uuml;rkiye sağlık sekt\u0026ouml;r\u0026uuml; raporu: S\u0026uuml;rd\u0026uuml;r\u0026uuml;lebilir ve kaliteli bir sağlık sekt\u0026ouml;r\u0026uuml; i\u0026ccedil;in genel bakış ve potansiyel iyileştirme alanları. YASED; 2012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYıldırım HH, Konca M. T\u0026uuml;rkiye\u0026rsquo;de \u0026ouml;zel sağlık kurumları sekt\u0026ouml;r\u0026uuml;: Mevcut durum, sorunlar ve \u0026ccedil;\u0026ouml;z\u0026uuml;m \u0026ouml;nerileri. T\u0026Uuml;SPE Yayınları; 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTokatlioglu Y, Sulku SN. The impact of the covid-19 outbreak on unmet health care needs in Istanbul. Prev Med Rep. 2023;36:102400. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pmedr.2023.102400\u003c/span\u003e\u003cspan address=\"10.1016/j.pmedr.2023.102400\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosenberg M, Tomioka S, Barber SL. Research to inform health systems\u0026rsquo; responses to rapid population ageing. Health Res Policy Syst. 2022;20(Suppl 1):128. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12961-022-00917-z\u003c/span\u003e\u003cspan address=\"10.1186/s12961-022-00917-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e The distribution of the efficiency scores with respect to years, hospital types, region and sizes can be shared upon request from the corresponding author.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Health policy, private hospital efficiency, Türkiye, stochastic frontier analysis","lastPublishedDoi":"10.21203/rs.3.rs-8376761/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8376761/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eThis study examines the efficiency of T\u0026uuml;rkiye\u0026rsquo;s private healthcare sector, which has expanded significantly recent decades in parallel with global trends. Between 2002 and 2022, the number of private hospitals more than doubled, largely driven by the Health Transformation Program.\u003c/p\u003e\u003ch2\u003eData and Methodology:\u003c/h2\u003e \u003cp\u003eWe assess the technical efficiency of outpatient and inpatient care services provided by private hospitals in T\u0026uuml;rkiye through the application of stochastic frontier analysis (SFA). The unbalanced panel dataset includes 541 hospitals and covers the period from 2019 to 2023. The SFA models incorporate inputs such as the number of medical and non-medical personnel, the number of beds, and their interactions. Additionally, external factors\u0026mdash;including the COVID-19 pandemic and the 2023 earthquake in southern T\u0026uuml;rkiye\u0026mdash;are integrated into the models to evaluate their effects on the outputs of outpatient visits and inpatient discharges.\u003c/p\u003e\u003ch2\u003eFindings:\u003c/h2\u003e \u003cp\u003eThe SFA results indicate that the average technical efficiencies of private hospitals were 46% for inpatient services and 60% for outpatient services. These results suggest a potential for improvement of 54% and 40%, respectively, through more efficient utilization of existing input bundles under current technological conditions. The efficiency averages show no significant variation across years, regions, hospital types, or sizes. The analysis further reveals that healthcare personnel play a critical role in the efficient delivery of healthcare services. Nurses, along with other medical and non-medical staff, appear to function as potential substitutes within private hospitals. Outpatient efficiency declined sharply in 2020, the adverse impact of the pandemic lessened in 2021. In contrast, inpatient services remained largely unaffected by COVID-19. The 2023 earthquake, however, substantially decreased efficiency across both service types, with outpatient care being most severely impacted. Finally, the production functions for both inpatient and outpatient services exhibit diminishing returns to scale, indicating that hospital expansion may outpace managerial capacity.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe findings offer valuable insights to both private sector managers and healthcare policy makers, supporting more informed decision-making and strategic planning in shaping the hospital sector.\u003c/p\u003e","manuscriptTitle":"Performance Evaluation of Private Hospitals under the Health Transformation Initiative in Türkiye","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-29 08:51:54","doi":"10.21203/rs.3.rs-8376761/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"415e6c17-1030-49f0-a560-5e110ee0a4ce","owner":[],"postedDate":"December 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-03T09:57:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-29 08:51:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8376761","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8376761","identity":"rs-8376761","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}