An automated electronic tool to assess the risk of 30-day readmission: Validation of predictive performance

Nancy L. Dawson, Razvan M. Chirila, Vandana Y. Bhide, Colleen S. Thomas, Keith J. Cannon, M. Caroline Burton

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Objective: To validate an electronic tool created to identify inpatients who are at risk of readmission within 30 days and quantify the predictive performance of the readmission risk score (RRS). Methods: Retrospective cohort study including inpatients who were discharged between 1 Nov 2012 and 31 Dec 2012. The ability of the RRS to discriminate between those who did and did not have a 30-day urgent readmission was quantified by the c statistic. Calibration was assessed by plotting the observed and predicted probability of 30-day urgent readmission. Predicted probabilities were obtained from generalized estimating equations, clustering on patient. Results: Of 1689 hospital inpatient discharges (1515 patients), 159 (9.4%) had a 30-day urgent readmission. The RRS had some discriminative ability (c statistic: 0.612; 95% confidence interval: 0.570-0.655) and good calibration. Conclusions: Our study shows that the RRS has some discriminative ability. The automated tool can be used to estimate the probability of a 30-day urgent readmission.

Original languageEnglish (US)
Pages (from-to)449-454
Number of pages6
JournalJournal of Clinical Outcomes Management
Volume23
Issue number10
StatePublished - Oct 1 2016

ASJC Scopus subject areas

  • Health Policy

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