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https://doi.org/10.1055/s-0041-1726422
Title: | Effect of a Real-Time Risk Score on 30-day Readmission Reduction in Singapore | Authors: | Wu, CX Suresh, E Phng, FWL Tai, KP Pakdeethai, J D'souza, JLA Tan, WS Phan, P Lew, KSM Tan, GYH Chua, GSW Hwang, CH |
Keywords: | Aftercare Humans Patient Discharge Patient Readmission Prospective Studies Retrospective Studies Risk Factors Singapore |
Issue Date: | 1-Mar-2021 | Publisher: | Georg Thieme Verlag KG | Citation: | Wu, CX, Suresh, E, Phng, FWL, Tai, KP, Pakdeethai, J, D'souza, JLA, Tan, WS, Phan, P, Lew, KSM, Tan, GYH, Chua, GSW, Hwang, CH (2021-03-01). Effect of a Real-Time Risk Score on 30-day Readmission Reduction in Singapore. Applied Clinical Informatics 12 (2) : 372-382. ScholarBank@NUS Repository. https://doi.org/10.1055/s-0041-1726422 | 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 to care providers in the hospital. Based on the daily high-risk patient list, the various interfaces and flow sheets in the EMR were configured according to the information needs of the various stakeholders such as the inpatient medical, nursing, case management, emergency department, and postdischarge care teams. Results: Overall, the machine-learning models achieved good performance with area under the receiver operating characteristic ranging from 0.77 to 0.81. The models were used to proactively identify and attend to patients who are at risk of readmission before an actual readmission occurs. This approach successfully reduced the 30-day readmission rate for patients admitted to the medicine department from 11.7% in 2017 to 10.1% in 2019 (p < 0.01) after risk adjustment. Conclusion: Machine-learning models can be deployed in the EMR system to provide real-time forecasts for a more comprehensive outlook in the aspects of decision-making and care provision. | Source Title: | Applied Clinical Informatics | URI: | https://scholarbank.nus.edu.sg/handle/10635/206101 | ISSN: | 18690327 | DOI: | 10.1055/s-0041-1726422 |
Appears in Collections: | Elements Staff Publications |
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