Predicting readmission of heart failure patients using automated follow-up calls

Shelby Inouye, Vasileios Bouras, Eric Shouldis, Adam Johnstone, Zachary Silverzweig, Pallav Kosuri

Readmission rates for patients with heart failure (HF) remain high. Many efforts to identify patients at high risk for readmission focus on patient demographics or on measures taken in the hospital. We evaluated a method for risk assessment that depends on patient self-report following discharge from the hospital. In this study, we investigated whether automated calls could be used to identify patients who are at a higher risk of readmission within 30 days. An automated multi-call follow-up program was deployed with 1095 discharged HF patients. During each call, the patient reported his or her general health status. Patients were grouped by the trend of their responses over the two calls, and their unadjusted 30-day readmission rates were compared. Pearson's chi-square test was used to evaluate whether readmission risk was independent of response trend. Of the 1095 patients participating in the program, 837 (76%) responded to the general status question in at least one of the calls and 515 (47%) patients responded to the general status question in both calls. Out of the 89 patients exhibiting a negative response trend, 37% were readmitted. By contrast, the 97 patients showing a positive trend and the 329 patients showing a neutral trend were readmitted at rates of 16% and 14% respectively. The dependence of readmission on trend group was statistically significant (P < 0.0001). Patients at an elevated risk of readmission can be identified based on the trend of their responses to automated follow-up calls. This presents a simple method for risk stratification based on patient self-assessment.