Decision support through risk cost estimation in 30-day hospital unplanned readmission

Abstract

Unplanned hospital readmissions mean a significant burden for health systems. Accurately estimating the patient’s readmission risk could help to optimise the discharge decision-making process by smartly ordering patients based on a severity score, thus helping to improve the usage of clinical resources. A great number of heterogeneous factors can influence the readmission risk, which makes it highly difficult to be estimated by a human agent. However, this score could be achieved with the help of AI models, acting as aiding tools for decision support systems. In this paper, we propose a machine learning classification and risk stratification approach to assess the readmission problem and provide a decision support system based on estimated patient risk scores.

Prediction of 30-day unplanned hospital readmission through survival analysis

“…Even when using regression models, the performance at 30 days since discharge cutoff is slightly above what had been obtained using classifier models for the same time period [12] , 0.69 ROC-AUC. The risk of readmission at 29 days is probably not that different from that at 31 days.…”

“…In this paper, the dataset is based on the one presented in [12] , but enriched with new variables that are detailed below. This dataset consists of approximately 33000 episodes of 21800 patients.…”

“…Compared to [12] , this study includes new information: Route of administration of each prescripted treatment. This includes 27 categories such as oral, subcutaneous or nasal.…”

Estimating and planning hospital costs of public hospitals in Brazil

“…Recent contributions to the state-of-the-art are usually department-specific instead of estimating hospitals' global costs. Studies have shown the costs' variation in maternity care [14,15], materials and supply expenses [16], operations [17], and readmission [18]. In general, the traditional economies of scale link the volume increase in a hospital service with reduced cost per patient in that service [19], or even on multiple services [20].…”