Predictive risk modelling for early hospital readmission of patients with diabetes in India

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.