EHR tool accurately predicts patients at risk for readmission

An automated prediction tool embedded in EHRs could assist organizations with implementing strategies designed to improve care and reduce readmissions, according to a study published in the Journal of Hospital Medicine.

In the study conducted at University of Pennsylvania’s Perelman School of Medicine, researchers successfully incorporated an automated prediction tool that identifies newly admitted patients who are at risk of readmission within 30 days of discharge.

Specifically, the research 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 of being readmitted within 30 days post discharge.

The tool enabled the flagging of high-risk patients in EHRs; clinicians were able to double click the flag icon to learn about previous emergency department (ED) visits and discharge planning, issues that arose and identification of the care team involved. The impact of the tool was studied for one year. During that time, patients who triggered the readmission alert were subsequently readmitted 31 percent of the time. When an alert was not triggered, patients were readmitted only 11 percent of the time.

"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."

Access the study here.