Purpose – The purpose of this paper is to identify the most significant barriers to successful implementation of information technology (IT) in higher educational institutions (HEIs) of India. Although, educational institutions are investing in IT, they have been not been able to leverage it the same way as other business organizations. The present investigation will assist the management of HEIs to distinguish the key barriers affecting productive IT implementations and further take appropriate measures to deal with it. Design/methodology/approach – For the purpose of the study, focus group and semi-structured interviews were conducted with academicians, administrators, functional heads, and IT staff from various HEIs of India. This research attempts to discover the major barriers to successful implementation of IT in HEIs using an interpretive structural modeling (ISM) methodology. Furthermore, structural analysis and classification of barriers is done using MICMAC analysis. Findings – The results identified the key barriers that if dealt with can help overcome or lower the effect of other barriers preventing successful IT implementation in HEIs. It will provide roadmap to managers and administrators of HEIs to take appropriate measures to overcome the major barrier to effective implementation of IT. Originality/value – Several authors have been studied barriers to implementation of IT in industry and educational institutions, but none have found the most significant barriers that affect successful implementation of IT and may drive other impediments. This research draws inspiration and is being carried out for Indian HEIs.

Hospital readmission is an important contributor to total medical expenditure and is an emerging indicator of quality of care. The goal of this study is to analyze key factors using machine learning methods and patients' medical records of a reputed Indian hospital which impact the all-purpose readmission of a patient with diabetes and compare different classification models that predict readmission and evaluate the best model. This study classified the patients into two different risk groups of readmission (Yes or No) within 30 days of discharge based on patients' characteristics using 2-year clinical and administrative data. It proposed an architecture of this prediction model and identified various risk factors using text mining techniques. Also, groups of consistently occurring factors that inference readmission rates were revealed by associative rule mining. It then evaluated the classification accuracy using five different data mining classifiers and conducted cost analysis. Out of total 9381 records, 1211 (12.9 %) encounters were found as readmissions. This study found that risk factors like hospital department where readmission happens, history of recent prior hospitalization and length of stay are strong predictors of readmission. Random forest was found to be the optimal classifier for this task using the evaluation metric area under precision-recall curve (0.296). From the cost analysis, it is observed that a cost of INR 15.92 million can be saved for 9381 instances of diabetic patient encounters. This work, the first such study done from Indian Healthcare perspective, built a model to predict the risk of readmission within 30 days of discharge for diabetes. This study concludes that the model could be incorporated in healthcare institutions to witness its effectiveness. Cost analysis shows huge savings which is significant for any healthcare system especially in developing countries like India.

The study supports that greater HLA sharing between spouses, associated with lack of an appropriate immune response to them could be responsible for RSA.

Purpose:The current classification of ocular trauma does not incorporate adnexal trauma, injuries that are attributable to a nonmechanical cause and destructive globe injuries. This study proposes a new classification system of ocular trauma which is broader-based to allow for the classification of a wider range of ocular injuries not covered by the current classification.Methods:A clinic-based cross-sectional study to validate the proposed classification. We analyzed 535 cases of ocular injury from January 1, 2012 to February 28, 2012 over a 4-year period in an eye hospital in central India using our proposed classification system and compared it with conventional classification.Results:The new classification system allowed for classification of all 535 cases of ocular injury. The conventional classification was only able to classify 364 of the 535 trauma cases. Injuries involving the adnexa, nonmechanical injuries and destructive globe injuries could not be classified by the conventional classification, thus missing about 33% of cases.Conclusions:Our classification system shows an improvement over existing ocular trauma classification as it allows for the classification of all type of ocular injuries and will allow for better and specific prognostication. This system has the potential to aid communication between physicians and result in better patient care. It can also provide a more authentic, wide spectrum of ocular injuries in correlation with etiology. By including adnexal injuries and nonmechanical injuries, we have been able to classify all 535 cases of trauma. Otherwise, about 30% of cases would have been excluded from the study.

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