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. DESIGN: Retrospective and prospective cohort. SETTING: Healthcare system consisting of 3 hospitals. PATIENTS: All adult patients admitted from August 2009 to September 2012. INTERVENTIONS: An automated readmission risk flag integrated into the EHR. MEASURES: 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.