The readmission risk flag: Using the electronic health record to automatically identify patients at risk for 30-day readmission
Article first published online: 13 NOV 2013
© 2013 Society of Hospital Medicine
Journal of Hospital Medicine
Volume 8, Issue 12, pages 689–695, December 2013
How to Cite
Baillie, C. A., VanZandbergen, C., Tait, G., Hanish, A., Leas, B., French, B., William Hanson, C., Behta, M. and Umscheid, C. A. (2013), The readmission risk flag: Using the electronic health record to automatically identify patients at risk for 30-day readmission. J. Hosp. Med., 8: 689–695. doi: 10.1002/jhm.2106
- Issue published online: 6 DEC 2013
- Article first published online: 13 NOV 2013
- Manuscript Accepted: 30 SEP 2013
- Manuscript Revised: 27 SEP 2013
- Manuscript Received: 4 JUN 2013
Identification of patients at high risk for readmission is a crucial step toward improving care and reducing readmissions. The adoption of electronic health records (EHR) may prove important to strategies designed to risk stratify patients and introduce targeted interventions.
To develop and implement an automated prediction model integrated into our health system's EHR that identifies on admission patients at high risk for readmission within 30 days of discharge.
Retrospective and prospective cohort.
Healthcare system consisting of 3 hospitals.
All adult patients admitted from August 2009 to September 2012.
An automated readmission risk flag integrated into the EHR.
Thirty-day all-cause and 7-day unplanned healthcare system readmissions.
Using retrospective data, a single risk factor, ≥2 inpatient admissions in the past 12 months, was found to have the best balance of sensitivity (40%), positive predictive value (31%), and proportion of patients flagged (18%), with a C statistic of 0.62. Sensitivity (39%), positive predictive value (30%), proportion of patients flagged (18%), and C statistic (0.61) during the 12-month period after implementation of the risk flag were similar. There was no evidence for an effect of the intervention on 30-day all-cause and 7-day unplanned readmission rates in the 12-month period after implementation.
An automated prediction model was effectively integrated into an existing EHR and identified patients on admission who were at risk for readmission within 30 days of discharge. Journal of Hospital Medicine 2013;8:689–695. © 2013 Society of Hospital Medicine