Readmission Prediction Flags At Risk Patients
By Katie Wike, contributing writer
Automated prediction alerts are helping providers identify patients who are most likely to be readmitted within a month
The University of Pennsylvania Health System has integrated software into the system’s EHR that can identify at risk patients and predict readmissions. According to an announcement by Penn, the “team found that having been admitted to the hospital two or more times in the 12 months prior to admission is the best way to predict which patients are at risk for being readmitted in the 30 days after discharge. As a result of this finding, the automated tool is now able to identify patients as being ‘high risk’ for readmission and creates a ‘flag’ in their electronic health record.
“Upon admission of a high-risk patient, the flag appears next to the patient's name in a column titled ‘readmission risk.’ The flag can be double-clicked to display detailed information relevant to discharge planning including inpatient and emergency department visits over the previous 12 months, as well as information about the care teams, lengths of stay, and problem(s) associated with those prior admissions.”
"The results we've seen with this tool show that we can predict, with a good deal of accuracy, patients who are at risk of being readmitted within 30 days of discharge," said lead author Charles A. Baillie, MD, an internal medicine specialist and fellow in the Center for Clinical Epidemiology and Biostatistics at Penn Medicine. "With this knowledge, care teams have the ability to target these patients, making sure they receive the most intensive interventions necessary to prevent their readmission."
The results are published in the December issue of the Journal of Hospital Medicine., which notes an automated model of the software was successfully integrated into Penn’s EHR and was effective in predicting patients who were at risk for readmission. This allows for interventions which were proven to help reduce 30-day readmissions. They include enhanced patient education and medication reconciliation on the day of discharge, increased home services to provide a safe landing, follow up appointments soon after discharge, and follow-up phone calls to ensure an extra level of protection.
"By automating the process of readmission risk prediction, we were able to provide risk assessment quickly and efficiently in real time, enabling all members of the inpatient team to carry out a coordinated approach to discharge planning, with special attention paid to those identified as being at the highest risk for readmission," said Craig A Umscheid, MD, MSCE, assistant professor of Medicine and Epidemiology, director of the Penn Medicine Center for Evidence-based Practice, and senior author on the study.
"Readmission rates should improve over time as the risk flag is used more routinely and the interventions necessary to reduce readmission rates for those identified as high risk are implemented," said Baillie.
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