Estimating Technical Efficiency of Turkish Hospitals

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Estimation of Turkish Hospitals technical efficiency with stochastic frontier model.

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Estimating Technical Efficiency of Turkish Hospitals

  1. 1. Estimating Technical Efficiency of Turkish Hospitals: An Application of Stochastic Frontier Analysis M. Said Yildiza, MS | Vahé Heboyanb, PhD | M. Mahmud Khan, PhDc Problem Statement The trifecta of rising medical expenditures, low quality, and poor access are of great concern to healthcare administrators and policymakers worldwide. Improving healthcare delivery efficiency might make it possible to provide more and better healthcare services with fewer resources. Compared to OECD countries, Turkey is behind in major healthcare indicators, such as life expectancy, mortality rates, hospital beds per population, and physician density (Table 1). Therefore, it becomes essential to develop a better understanding of the healthcare resource utilization and hospital efficiency in the country. Objective To analyze the technical efficiency of the Turkish hospitals utilizing comprehensive input and output data of public and private hospitals in Turkey. Policy Importance Understand state-of-healthcare resource use and existing (in)efficiencies among hospitals of various ownership (e.g. public, private) and type (e.g. teaching, general). Design and deploy data-driven policies and programs aimed at improving and optimizing the efficiency of healthcare system and delivery in Turkey. Data Cross-sectional data obtained from the statistical reports of MoH Health Services General Directorate of Turkey for the year 2011. Complete sample consisting of 1,078 hospitals with different ownerships (397 private, 56 university, and 625 MoH hospitals) and types (98 teaching and 981 general). Specialty hospitals, small size integrated hospitals (<20 inpatients beds), and hospitals with considerable missing data were removed. Table 1. Select Health Indicators, Turkey and OECD, 2011 (or nearest) Contacts a. Internal Auditor, Ministry of Health, Turkey and Visiting Scholar, Dept. of Health Services Policy and Management, University of South Carolina, Columbia, SC; mustafasaid25@gmail.com. b. Assistant Professor, Dept. Health Management and Informatics, Georgia Regents University, Augusta, GA; Vheboyan@gru.edu (corresponding author). c. Professor and Head, Dept. of Health Services Policy and Management, University of South Carolina, Columbia, SC; MKhan@mailbox.sc.edu. Methods Analytic method: Stochastic Frontier Analysis (Kumbhakar & Lovell, 2000) Functional form: modified Cobb-Douglas production technology per Battese et al. (1996) to account for zero-value inputs. Dependent variable is an output score for each hospital derived as a weighted average of multiple output variables. Weights were average prices of hospital services set by the Turkish Social Insurance Institute (SSI). icubeds*=ln[max(icubeds, D1)] per Battese et al. (1996) u and v are assumed to be independent. The term v captures the effect of measurement error. The component u captures the effect of inefficiency. Independent variables control for hospital infrastructure, technology, and human resources as described in Table 2. Results & Discussion Hospitals with TE score of 0.6 or more: MoH general (41%); MoH integrated county (46%); MoH teaching (40%); private general (38%); private university (54%); public university (34%). MoH general hospitals are more efficient (mean=0.57, sd=0.007) than the private general hospitals (mean=0.49, sd=0.013) in Turkey; contrary to the assumption that private hospitals have higher incentives to be more efficient As expected, public (mean=0.41, sd=0.03) and private (mean=0.39, sd=0.04) university hospitals are the least efficient since they spend significant resources for medical school student and resident training rather than production services. Integrated county hospitals have lower efficiency scores than other public sector hospitals because of low utilization rates of the facilities (lower demand for services). Regional and provincial differences are affected by hospital type and size composition of hospitals in the regions. Future research is needed to (1) identify factors influencing technical efficiency of hospitals and (2) assess policy effectiveness of affiliation protocols between MoH hospitals and university hospitals to increase the overall efficiency of hospitals at the country level. Select References 1. Battese, GE, SJ Malik, AG Manzoor. An Investigation of Technical Inefficiencies of Production of Wheat Farmers in Four Districts of Pakistan. Journal of Agricultural Economics, 47(1): 37-49, 1996. 2. OECD (2013), Health at a Glance 2013: OECD Indicators, OECD Publishing. 3. Kumbhakar, SC and CAK Lovell. 2000. Stochastic Frontier Analysis. Cambridge: Cambridge University Press. Figure 2. Technical Efficiency Scores by Hospital Type Figure 3. Technical Efficiency Scores by Province§ § In the GIS shape file, Ardahan and Igdir were combined with Kars; Bartin and Karabuk with Zonguldak; Duzce with Bolu; Osmaniye with Adana; and Yalova with Bursa. Indicator OECD average Turkey Rank† (out of 34 OECD countries) Life expectancy at birth 80.1 74.6 33 Infant mortality (per 1000 live births) 4.1 7.7 33 Total expenditure on health, % GDP 9.3 6.1 33 Total expenditure on health, per capita, US$ PPP 906 3,322 34 Physicians, per 1,000 population 1.7 3.2 33 Nurses, per 1,000 population 1.7 8.7 33 Hospital beds, per 1,000 population 2.5 4.8 32 MRI units per 1 million population 10.5 13.2 18‡ CT scanners per million population 14.7 23.2 21‡ † lower number indicates higher ranking; ‡ out of 32 OECD countries Source: OECD (2013) 0 1 2 3 4 * 5 6 1 7 8 ln( ) ln( ) ln( ) ln( ) ln(devices) ln( ) ln( ) ( ) ( )k kk score bed doctors clinicians admin D icubeds gdp_pc type v u variable description mean st. dev. Coef. St. err. p > |z| score Hospital output score (Turkish lira) 14 mil. 29 mil. bed Number of inpatient hospital beds 143.68 214.89 0.1039 0.0562 0.0650 doctors Number of practicing doctors except residents 54.45 82.83 0.2851 0.0626 0.0000 clinician Number of health professionals other than doctors (nurses, midwives, etc.) 174.34 230.17 0.2842 0.0669 0.0000 devices Total number of xRay, MR, CT, ECG, Doppler 12.21 17.52 0.0632 0.0487 0.1950 admin Number of administrative staff 39.57 53.48 0.0393 0.0281 0.1620 icubeds* Intensive care unit beds 16.88 25.20 1.0988 0.0406 0.0000 Count % Coef. St. err. p > |z| D1 = 0 if icubeds > 0 (reference) = 1 if icubeds = 0 762 316 71 % 29 % 0.3833 0.0902 0.0000 gdp_pc Provincial GDP/capita = 0 if < $15,000 (ref.) = 1 if > $15,000 506 572 47 % 53 % 0.0721 0.0437 0.0990 type MoH general hospitals (reference) 453 42 % MoH integrated county hospitals 129 12 % -0.4245 0.0868 0.0000 MoH teaching hospitals 43 4 % -0.4187 0.1153 0.0000 Private general hospitals 397 37 % 0.1718 0.0912 0.0600 Private university hospitals 13 1 % -0.6258 0.1909 0.0010 Public university hospitals 43 4 % -0.4389 0.1331 0.0010 constant 10.1236 0.2358 0.0000 5 6 1 1 3 5 3 1 1 1 365i ij j iq iq q i inpatient j q im m il l ih h m l h score operations p beds rate p inpatient p delivery p tech p visits p 01020300102030 0 .5 1 0 .5 1 0 .5 1 MoH General MoH Integr. County Hosp. MoH Teaching Private General Private University Public University NumberofHospitals Technical Efficiency Score Table 2. Summary Statistics and Frontier Coefficient Estimates n=1,078 0.3 0.4 0.5 0.6 MoH General MoH Integr. County MoH Teaching Private General Private University Public University TechnicalEfficiencyScore Likelihood-ratio test of no technical inefficiency: H0: =0; = 84.08; p-value = 0.000 Figure 1. Mean Technical Efficiency Scores and 95% Confidence Intervals by Hospital Type

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