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The readmission risk flag: Using the electronic health record to automatically identify patients at risk for 30‐day readmission
Author(s) -
Baillie Charles A.,
VanZandbergen Christine,
Tait Gordon,
Hanish Asaf,
Leas Brian,
French Benjamin,
William Hanson C.,
Behta Maryam,
Umscheid Craig A.
Publication year - 2013
Publication title -
journal of hospital medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.128
H-Index - 65
eISSN - 1553-5606
pISSN - 1553-5592
DOI - 10.1002/jhm.2106
Subject(s) - medicine , flag (linear algebra) , electronic health record , hospital medicine , emergency medicine , medical emergency , intensive care medicine , health care , mathematics , economics , economic growth , pure mathematics , algebra over a field
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. Journal of Hospital Medicine 2013;8:689–695. © 2013 Society of Hospital Medicine