Project Summary

Hospital discharge is a decision based on several data points including diagnostic, physiological, demographic and caretaker information. Readmissions days after discharge are costly in addition to negative impact on capacity and service quality of hospitals. 30-days readmission (30DRA) literature remains focused on above variables and medical conditions paying little attention to the role of alternate-care-facilities (such as skilled nursing facilities and hospices) on reduction of 30DRA rates. To the best of our knowledge, there is negligible research considering alternate care variables for predicting readmissions even when physicians have actively started considering discharge-to-alternate-care during discharge planning. This paper develops a classification model for predicting patients who are likely to be readmitted within 30 days of discharge-to-alternate-care. Several machine-learning approaches, such as multi-logistic regression, Naïve Bayes, random forest, and neural networks were tested on the model to find the one with highest predictive power. The model was trained and tested on MIMIC-III, a large anonymized electronic health records (EHRs) database from US hospitals. Results suggest discharge-to-alternate-care reduces 30DRA. Moreover, neural networks and logistic regression techniques show better precision and accuracy in identifying the patients likely to be readmitted in 30 days.

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