Predicting Hospital Readmission among Patients with Sepsis using Clinical and Wearable Data

Abstract

Sepsis is a life-threatening condition that occurs due to a dysregulated host response to infection. Recent data demonstrate that patients with sepsis have a significantly higher readmission risk than other common conditions, such as heart failure, pneumonia and myocardial infarction and associated economic burden. Prior studies have demonstrated an association between a patient's physical activity levels and readmission risk. In this study, we show that distribution of activity level prior and post-discharge among patients with sepsis are predictive of unplanned rehospitalization in 90 days (P-value<1e-3). Our preliminary results indicate that integrating Fitbit data with clinical measurements may improve model performance on predicting 90 days readmission.

Voice-Enabled Response Analysis Agent (VERAA): Leveraging Large Language Models to Map Voice Responses in SDoH Survey

“…While the causal mechanism is unknown, LEP is known to be associated with greater difficulties in accessing medical care, and language barriers can impede providers' ability to take an appropriate clinical history that may lead to clinical errors or delays in care. In our previous work, we have demonstrated that SDoH (8) and wearable (9,10) data can dramatically improve the accuracy of sepsis readmission scores. However, SDoH factors are often poorly captured in electronic health records (EHRs) and are not available at the time of hospital discharge.…”