The readmission risk flag: using the electronic health record to automatically identify patients at risk for 30-day readmission.
BACKGROUND
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.


OBJECTIVE
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.


METHODS
Retrospective and prospective cohort.


METHODS
Healthcare system consisting of 3 hospitals.


METHODS
All adult patients admitted from August 2009 to September 2012.


METHODS
An automated readmission risk flag integrated into the EHR.


METHODS
Thirty-day all-cause and 7-day unplanned healthcare system readmissions.


RESULTS
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.


CONCLUSIONS
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.
Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
Charles A
CA
Baillie
Christine
C
VanZandbergen
Gordon
G
Tait
Asaf
A
Hanish
Brian
B
Leas
Benjamin
B
French
C William
CW
Hanson
Maryam
M
Behta
Craig A
CA
Umscheid
D000328:Adult; D015331:Cohort Studies; D057286:Electronic Health Records / Q000592:standards / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D010359:Patient Readmission / Q000592:standards*; D011446:Prospective Studies; D012189:Retrospective Studies; D012307:Risk Factors; D013997:Time Factors