title: Development and implementation of a real-time 30-day readmission predictive model.
abstract: Hospitals are under great pressure to reduce readmissions of patients. Being able to reliably predict patients at increased risk for rehospitalization would allow for tailored interventions to be offered to them. This requires the creation of a functional predictive model specifically designed to support real-time clinical operations. A predictive model for readmissions within 30 days of discharge was developed using retrospective data from 45,924 MGH admissions between 2/1/2012 and 1/31/2013 only including factors that would be available by the day after admission. It was then validated prospectively in a real-time implementation for 3,074 MGH admissions between 10/1/2013 and 10/31/2013. The model developed retrospectively had an AUC of 0.705 with good calibration. The real-time implementation had an AUC of 0.671 although the model was overestimating readmission risk. A moderately discriminative real-time 30-day readmission predictive model can be developed and implemented in a large academic hospital.
authors.affiliation: Laboratory of Computer Science, Massachusetts General Hospital, Boston, MA.
authors.forename: Patrick R
authors.initials: PR
authors.lastname: Cronin
authors.affiliation: Department of Medicine, Massachusetts General Hospital, Boston, MA.
authors.forename: Jeffrey L
authors.initials: JL
authors.lastname: Greenwald
authors.affiliation: Department of Medicine, Massachusetts General Hospital, Boston, MA.
authors.forename: Gwen C
authors.initials: GC
authors.lastname: Crevensten
authors.affiliation: Laboratory of Computer Science, Massachusetts General Hospital, Boston, MA.
authors.forename: Henry C
authors.initials: HC
authors.lastname: Chueh
authors.affiliation: Laboratory of Computer Science, Massachusetts General Hospital, Boston, MA.
authors.forename: Adrian H
authors.initials: AH
authors.lastname: Zai
mesh_terms: D000046:Academic Medical Centers; D019540:Area Under Curve; D006769:Hospitals, General; D006801:Humans; D008404:Massachusetts; D008962:Models, Theoretical; D016017:Odds Ratio; D010359:Patient Readmission; D012189:Retrospective Studies; D012307:Risk Factors
keywords: 30-Day Readmissions; Predictive Modeling; Readmission Risk; Real-Time