Background: Hospitals are highly resource-dependent settings, which spend a large proportion of healthcare financial resources. The analysis of hospital efficiency can provide insight into how scarce resources are used to create health values. This study examines the Technical Efficiency (TE) of 12 teaching hospitals affiliated with Tehran University of Medical Sciences (TUMS) between 1999 and 2011. Methods: The Stochastic Frontier Analysis (SFA) method was applied to estimate the efficiency of TUMS hospitals. A best function, referred to as output and input parameters, was calculated for the hospitals. Number of medical doctors, nurses, and other personnel, active beds, and outpatient admissions were considered as the input variables and number of inpatient admissions as an output variable. Results: The mean level of TE was 59% (ranging from 22 to 81%). During the study period the efficiency increased from 61 to 71%. Outpatient admission, other personnel and medical doctors significantly and positively affected the production (P< 0.05). Concerning the Constant Return to Scale (CRS), an optimal production scale was found, implying that the productions of the hospitals were approximately constant. Conclusion: Findings of this study show a remarkable waste of resources in the TUMS hospital during the decade considered. This warrants policy-makers and top management in TUMS to consider steps to improve the financial management of the university hospitals.

Purpose As hospitals are the most costly service providers in every healthcare systems, special attention should be given to their performance in terms of resource allocation and consumption. The purpose of this paper is to evaluate technical, allocative and economic efficiency in intensive care units (ICUs) of hospitals affiliated by Yazd University of Medical Sciences (YUMS) in 2015. Design/methodology/approach This was a descriptive, analytical study conducted in ICUs of seven training hospitals affiliated by YUMS using data envelopment analysis (DEA) in 2015. The number of physicians, nurses, active beds and equipment were regarded as input variables and bed occupancy rate, the number of discharged patients, economic information such as bed price and physicians' fees were mentioned as output variables of the study. Available data from study variables were retrospectively gathered and analyzed through the Deap 2.1 software using the variable returns to scale methodology. Findings The study findings revealed the average scores of allocative, economic, technical, managerial and scale efficiency to be relatively 0.956, 0.866, 0.883, 0.89 and 0.913. Regarding to latter three types of efficiency, five hospitals had desirable performance. Practical implications Given that additional costs due to an extra number of manpower or unnecessary capital resources impose economic pressure on hospitals also the fact that reduction of surplus production plays a major role in reducing such expenditures in hospitals, it is suggested that departments with low efficiency reduce their input surpluses to achieve the optimal level of performance. Originality/value The authors applied a DEA approach to measure allocative, economic, technical, managerial and scale efficiency of under-study hospitals. This is a helpful linear programming method which acts as a powerful and understandable approach for comparative performance assessment in healthcare settings and a guidance for healthcare managers to improve their departments' performance.

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In Khuzestan, the mean of medication per patient was fewer than national average. Approximately, pattern of prescribed drug by family physicians (including dosage form and type of drugs) was similar to other provinces of Iran.

Background: Given the rapid pace of changes in community health needs and the mission of healthcare organizations to provide and promote the community's health, the growing need to increase health system responsiveness to people as a key element of observance and fulfillment of justice is felt more than ever. Objectives: This study was aimed at designing the native model of responsiveness for Iran and to validate the aspects of the proposed model. Materials and Methods: Our study had a cross-sectional design and was a validation study performed in 2014. In order to define and identify responsiveness model aspects, the first phase recorded the views of 200 key informants from 19 provinces of Iran. Snowball sampling was used to select experts (based on WHO guideline). Then, the opinions of 18 comments were received from service recipients in the form of three focus group discussions and were analyzed by the frame framework analysis (interviewed recipients were selected using the purposive sampling method). Finally, in order to confirm the model's efficacy, a responsiveness questionnaire with 7 aspects (domains) and 52 indicators (items) obtained from the initial proposed model was answered by 600 members of the selected families in the two provinces of Fars and Yazd. A multi-stage cluster sampling approach was used for the household survey. The results were analyzed by the Confirmatory Factor Analysis (CFA) test and through the use of Lisrel software. Results: Confirmatory Factor Analysis, based on the results of the key informant survey and group discussions, showed that according to quantities of GFI = 0.91, CFI = 0.93, NFI = 0.91, RMSEA = 0.074, SRMR = 0.061 and Hoelter (CN) = 178.54 in outpatient services and where GFI = 0.89, CFI = 0.91, NFI = 0.86, RMSEA = 0.064, SRMR = 0.053 and Hoelter (CN) = 158.93 for inpatient services, seven factors (F) (dignity, informed choice, confidentiality, patient training and informing, access to services, quality of basic amenities, and access to social support) are the main determinants of the responsiveness model and proposed model validity. Conclusions: Given the comprehensiveness of presented aspects and indicators in this proposed model and its validity test, the aforementioned responsiveness model can be considered a suitable model to use when assessing the levels of health system responsiveness in Iran.