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

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

Author information

1
Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.

Abstract

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.

PMID:
24227707
PMCID:
PMC4407637
DOI:
10.1002/jhm.2106
[Indexed for MEDLINE]
Free PMC Article

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