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We examine how readmission risk may progress over multiple emergency department visits of chronic disease patients, their early stratification into distinct trajectories with related frequencies, and the relationship of these trajectories to patient characteristics. We further extend this analysis to investigate the impact of time-stable and time-varying covariates in predicting future readmission conditional on latent class membership. Results indicate that longitudinal risk stratification can enable early identification of specific patient groups following distinct trajectories based on their presentation for emergency care. Prediction models that incorporate latent classes perform well and demonstrate the promise of trajectory modeling methods combined with advanced prediction models for longitudinal risk assessment in addressing readmission challenges. The methodology and insights from this study are generalizable to other important Information Systems problems.<\/jats:p>","DOI":"10.25300\/misq\/2020\/15101","type":"journal-article","created":{"date-parts":[[2020,3,4]],"date-time":"2020-03-04T16:13:37Z","timestamp":1583338417000},"page":"201-227","source":"Crossref","is-referenced-by-count":43,"title":["Trajectories of Repeated Readmissions of Chronic Disease Patients: Risk Stratification, Profiling, and Prediction"],"prefix":"10.25300","volume":"44","author":[{"given":"Ofir","family":"Ben-Assuli","sequence":"first","affiliation":[{"name":"The Faculty of Business Administration, Ono Academic College,104 Zahal Street, Kiryat Ono 55000, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rema","family":"Padman","sequence":"additional","affiliation":[{"name":"The H. 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