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

J Hosp Med. 2013 Dec;8(12):689-95. doi: 10.1002/jhm.2106. Epub 2013 Nov 13.


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.

Publication types

  • Multicenter Study

MeSH terms

  • Adult
  • Cohort Studies
  • Electronic Health Records / standards
  • Electronic Health Records / statistics & numerical data*
  • Female
  • Humans
  • Male
  • Patient Readmission / standards*
  • Prospective Studies
  • Retrospective Studies
  • Risk Factors
  • Time Factors