Tehran University Medical Journal

Background: One common symptom of diabetes is diabetic retinopathy, if not timely diagnosed and treated, leads to blindness. Retinal image analysis has been currently adopted to diagnose retinopathy. In this study, a model of hierarchical self-organized neural networks has been presented for the detection and classification of retina in diabetic patients.
Methods: This study is a retrospective cross-sectional, conducted from December to February 2015 at the AJA University of Medical Sciences, Tehran. The study has been conducted on the MESSIDOR base, which included 1200 images from the posterior pole of the eye. Retinal images are classified into 3 categories: mild, moderate and severe. A system consisting of a new hybrid classification of SOM has been presented for the detection of retina lesions. The proposed system includes rapid preprocessing, extraction of lesions features, and finally provision of a classification model. In the preprocessing, the system is composed of three processes of primary separation of target lesions, separation of the optical disk, and separation of blood vessels from the retina. The second step is a collection of features based on various descriptions, such as morphology, color, light intensity, and moments. The classification includes a model of hierarchical self-organized networks named HSOM which is proposed to accelerate and increase the accuracy of lesions classification considering the high volume of information in the feature extraction.
Results: The sensitivity, specificity and accuracy of the proposed model for the classification of diabetic retinopathy lesions is 98.9%, 96.77%, 97.87%, respectively.
Conclusion: These days, the cases of diabetes with hypertension are constantly increasing, and one of the main adverse effects of this disease is related to eyes. In this respect, the diagnosis of retinopathy, which is the same as identification of exudates, microanurysm and bleeding, is of particular importance. The results show that the proposed model is able to detect lesions in diabetic retinopathy images and classify them with an acceptable accuracy. In addition, the results suggest that this method has an acceptable performance compared to other methods.

Background: Unnecessary patient admission to a hospital refers to the hospitalization of a patient without clinical indications and criteria. Various factors related to the patient (e.g., age, disease severity, payment method, and admission route and time), the physician and the hospital and its facilities and diagnostic technologies affect a patient unnecessary admission in a hospital. Unnecessary patient hospitalization increases nosocomial infections, morbidity and mortality, and decreases patient satisfaction and hospital productivity. This study aimed to measure unnecessary patient admissions in hospitals in Iran.
Methods: This study was conducted using a systematic review and meta-analysis at Tehran University of Medical Science in August 2019. Seven electronic databases were searched and evaluated for original research papers published between March 2006 and 2018 on patients’ unnecessary admission to a hospital. Finally, 12 articles were selected and analyzed using comprehensive meta-analysis software.
Results: All studies used the appropriateness evaluation protocol (AEP) for assessing patients’ unnecessary hospitalization in the hospitals. Overall, 2.7% of hospital admissions were rated as inappropriate and unnecessary (CI 95%: 1.5-4.9%). The highest unnecessary patients’ admissions were 11.8% in a teaching hospital in Meshginshahr city in 2016, (CI 95%: 8.8%-15.8%) and the lowest unnecessary patients’ admissions was 0.3% in a teaching hospital in Yasuj city in 2016 (CI 95%: 0%-3.6%). Unnecessary patient admission in public hospitals was higher than private hospitals. A significant statistical correlation was observed between unnecessary patient admission, and sample size (P<0.05).
Conclusion: The rate of unnecessary hospital admission in Iran is low. However, hospital resources are wasted due to unnecessary admissions. Expanding the primary health care network, reducing hospital beds, introducing an effective and efficient patient referral system, using a fixed provider payment method, and promoting residential and social services care at macro level, and establishing utilization management committee, using the appropriateness evaluation protocol, establishing short-stay units, and implementing quality management strategies at the hospital level are useful strategies for reducing avoidable hospital admissions.

Background: Medical errors are those errors or mistakes committed by healthcare professionals due to errors of omission, errors in planning, and errors of execution of a planned healthcare action whether or not it is harmful to the patient. Medical error in hospitals increases morbidity and mortality and decreases patient satisfaction and hospital productivity. This study aimed to determine the prevalence of medical errors in Iranian hospitals.
Methods: This study was conducted using systematic review and meta-analysis approaches. All articles written in English and Persian on the prevalence of medical errors in Iranian hospitals up to March 2019 were searched in Web of Science, PubMed, Elsevier, Scopus, Magiran, IranMedex and Scientific Information Database (SID) databases, and Google and Google Scholar search engines. In addition, reference lists of the retrieved papers were hand-searched. A total of 9 studies matching the inclusion criteria were identified, reviewed, and analyzed using comprehensive meta-analysis software.