biostudies-literatureUnknown12(1)Wang LT.T & W.F. Chao FoundationJohn S. Dunn Research FoundationHealthcare regulatory agencies have mandated a reduction in 30-day hospital readmission rates and have targeted COPD as a major contributor to 30-day readmissions. We aimed to develop and validate a simple tool deploying an artificial neural network (ANN) for early identification of COPD patients with high readmission risk. Using COPD patient data from eight hospitals within a large urban hospital system, four variables were identified, weighted and validated. These included the number of in-patient admissions in the previous 6 months, the number of medications administered on the first day, insurance status, and the Rothman Index on hospital day one. An ANN model was trained to provide a predictive algorithm and validated on an additional dataset from a separate time period. The model wasScientific reports20633https://www.ebi.ac.uk/biostudies/studies/S-EPMC9712389biostudies-literatureAn AI-driven clinical care pathway to reduce 30-day readmission for chronic obstructive pulmonary disease (COPD) patients.PMC9712389Li GWong SSYJackson REXu PEzeana CFFrost AWang LYin ZPuppala MNezamabadi AOgunti RHe TWong STCYu XRoberts AWfalseAn AI-driven clinical care pathway to reduce 30-day readmission for chronic obstructive pulmonary disease (COPD) patients.Healthcare regulatory agencies have mandated a reduction in 30-day hospital readmission rates and have targeted COPD as a major contributor to 30-day readmissions. We aimed to develop and validate a simple tool deploying an artificial neural network (ANN) for early identification of COPD patients with high readmission risk. Using COPD patient data from eight hospitals within a large urban hospital system, four variables were identified, weighted and validated. These included the number of in-patient admissions in the previous 6 months, the number of medications administered on the first day, insurance status, and the Rothman Index on hospital day one. An ANN model was trained to provide a predictive algorithm and validated on an additional dataset from a separate time period. The model was2022-01-01T00:00:00Z2022 Nov2025-04-19T03:33:03.03Z2025-04-07T13:42:14.256ZS-EPMC97123893645079510.1038/s41598-022-22434-3