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

[EN] 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. L.A., P.P.S, J.R.N.C, P.R.V. and J.C.P.C. were founded by Generalitat Valenciana through IVACE (Valencian Institute of Business Competitiveness, https://www.ivace.es/index.php/es/) distributed nominatively to Valencian technological innovation centres under project IMDEEA-2021-100. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Arnal-Benedicto, L.; Pons-Suñer, P.; Navarro Cerdan, JR.; Ruiz Valls, P.; Caballero Mateos, MJ.; Valdivieso Martínez, B.; Perez-Cortes, J. (2022). Decision support through risk cost estimation in 30-day hospital unplanned readmission. PLoS ONE. 17(7):1-16. https://doi.org/10.1371/journal.pone.0271331