Effect of a Real-Time Risk Score on 30-day Readmission Reduction in Singapore.

ABSTRACT:

Objective

To develop a risk score for the real-time prediction of readmissions for patients using patient specific information captured in electronic medical records (EMR) in Singapore to enable the prospective identification of high-risk patients for enrolment in timely interventions.

Methods

Machine-learning models were built to estimate the probability of a patient being readmitted within 30 days of discharge. EMR of 25,472 patients discharged from the medicine department at Ng Teng Fong General Hospital between January 2016 and December 2016 were extracted retrospectively for training and internal validation of the models. We developed and implemented a real-time 30-day readmission risk score generation in the EMR system, which enabled the flagging of high-risk patients

SUBMITTER: Wu CX 

PROVIDER: S-EPMC8154510 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature