Vasileios Bouras
Cipher Health, USA
Title: Predicting readmission of heart failure patients using automated follow-up calls
Biography
Biography: Vasileios Bouras
Abstract
Cipher Health brings care providers and patients together, but that's not all. Over the years, we've brought together technologists, clinicians, engineers, consultants, students, coders, researchers, communicators, politicos, and a host of others. We're as passionate about our team as they are about healthcare, and it shows.
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. Our conclusion was that 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.