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

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.

Automated summary

Unplanned hospital readmissions are burdensome for health systems. This paper proposes a machine learning classification and risk stratification approach to estimate readmission risk and provide a decision support system based on patient risk scores.