Mahdi Mahdavi

Technical Efficiency of Teaching Hospitals in Iran: The Use of Stochastic Frontier Analysis, 1999–2011

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.

Comparison of Predictive Models for the Early Diagnosis of Diabetes

Objectives This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts. Methods We used memetic algorithms to update weights and to improve prediction accuracy of models. In the first step, the optimum amount for neural network parameters such as momentum rate, transfer function, and error function were obtained through trial and error and based on the results of previous studies. In the second step, optimum parameters were applied to memetic algorithms in order to improve the accuracy of prediction. This preliminary analysis showed that the accuracy of neural networks is 88%. In the third step, the accuracy of neural network models was improved using a memetic algorithm and resulted model was compared with a logistic regression model using a confusion matrix and receiver operating characteristic curve (ROC). Results The memetic algorithm improved the accuracy from 88.0% to 93.2%. We also found that memetic algorithm had a higher accuracy than the model from the genetic algorithm and a regression model. Among models, the regression model has the least accuracy. For the memetic algorithm model the amount of sensitivity, specificity, positive predictive value, negative predictive value, and ROC are 96.2, 95.3, 93.8, 92.4, and 0.958 respectively. Conclusions The results of this study provide a basis to design a Decision Support System for risk management and planning of care for individuals at risk of diabetes.

Recent Iranian Health System Reform: An Operational Perspective to Improve Health Services Quality

The operational management of healthcare services is expected to directly touch patient experiences. Iranian Ministry of Health and Medical Education (MoHME) for the first time, as such, has sought to improve the operational management of healthcare delivery within a reform agenda by setting benchmarks for ‘number of visit per hour’ and waiting time in outpatient clinics of about 700 affiliated hospitals. As a new initiative, it has faced with mixed reactions and various doubts have been cast on its successful implementation. This manuscript aims to shed some light on the operational challenges of the initiative and the requirements of its successful implementation.

The relationship between context, structure, and processes with outcomes of 6 regional diabetes networks in Europe

Background While health service provisioning for the chronic condition Type 2 Diabetes (T2D) often involves a network of organisations and professionals, most evidence on the relationships between the structures and processes of service provisioning and the outcomes considers single organisations or solo practitioners. Extending Donabedian’s Structure-Process-Outcome (SPO) model, we investigate how differences in quality of life, effective coverage of diabetes, and service satisfaction are associated with differences in the structures, processes, and context of T2D services in six regions in Finland, Germany, Greece, Netherlands, Spain, and UK. Methods Data collection consisted of: a) systematic modelling of provider network’s structures and processes, and b) a cross-sectional survey of patient reported outcomes and other information. The survey resulted in data from 1459 T2D patients, during 2011–2012. Stepwise linear regression models were used to identify how independent cumulative proportion of variance in quality of life and service satisfaction are related to differences in context, structure and process. The selected context, structure and process variables are based on Donabedian’s SPO model, a service quality research instrument (SERVQUAL), and previous organization and professional level evidence. Additional analysis deepens the possible bidirectional relation between outcomes and processes. Results The regression models explain 44% of variance in service satisfaction, mostly by structure and process variables (such as human resource use and the SERVQUAL dimensions). The models explained 23% of variance in quality of life between the networks, much of which is related to contextual variables. Our results suggest that effectiveness of A1c control is negatively correlated with process variables such as total hours of care provided per year and cost of services per year. Conclusions While the selected structure and process variables explain much of the variance in service satisfaction, this is less the case for quality of life. Moreover, it appears that the effect of the clinical outcome A1c control on processes is stronger than the other way around, as poorer control seems to relate to more service use, and higher cost. The standardized operational models used in this research prove to form a basis for expanding the network level evidence base for effective T2D service provisioning.

The impact of travel distance, travel time and waiting time on health-related quality of life of diabetes patients: An investigation in six European countries

AIMS The effects of travel distance and travel time to the primary diabetes care provider and waiting time in the practice on health-related quality of life (HRQoL) of patients with type 2 diabetes are investigated. RESEARCH DESIGN AND METHODS Survey data of 1313 persons with type 2 diabetes from six regions in England (274), Finland (163), Germany (254), Greece (165), the Netherlands (354), and Spain (103) were analyzed. Various multiple linear regression analyses with four different EQ-5D-3L indices (English, German, Dutch and Spanish index) as target variables, with travel distance, travel time, and waiting time in the practice as focal predictors and with control for study region, patients gender, patients age, patients education, time since diagnosis, thoroughness of provider-patient communication were computed. Interactions of regions with the remaining five control variables and the three focal predictors were also tested. RESULTS There are no interactions of regions with control variables or focal predictors. The indices decrease with increasing travel time to the provider and increasing waiting time in the providers practice. CONCLUSIONS HRQoL of patients with type 2 diabetes might be improved by decreasing travel time to the provider and waiting time in the providers practice.