An automated tool integrated into an electronic health record to predict patients at risk of hospital readmission can help to reduce such rates, according to a new study published in the Journal of Hospital Medicine.
The researchers, from the University of Pennsylvania School of Medicine, determined that patients admitted to the hospital at least twice in the 12 months before admission are at "high risk" of being readmitted again within 30 days of discharge. They added that an automated tool identified the patients and created a flag in their EHRs.
Almost one-third (31 percent) of the patients who triggered the alert were readmitted. In contrast, when the alert was not triggered, patients were readmitted only 11 percent of the time.
Identifying those at high risk enables the hospital to provide increased interventions, such as arranging for home services and follow up calls.
"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," Craig Umscheid, director of the Penn Medicine Center for Evidence-based Practice and senior author on the study, said in a statement.
EHRs continue to prove their potential worth in helping providers predict patients at risk, such as the likelihood of preterm births. Preventing readmissions is a major focus of health reform; hospitals with too-high readmission rates are subject to penalties.
To learn more:
- here's the study abstract
- read the announcement