Machine-learning approach using step counts predicts hospitalization during radiotherapy

An artificial intelligence model appeared to predict the likelihood of unplanned hospitalizations during chemoradiation therapy among a cohort of patients with various cancer types. The results, presented at American Society for Radiation Oncology Annual Meeting, showed the model, which used daily step counts measured through wearable devices as a proxy to monitor patients' health, provided physicians with a real-time method to provide personalized care. About 10% to 20% of patients who undergo outpatient radiation or chemoradiation require acute care via an ED visit or hospital admission during their course of treatment. These unplanned hospitalizations can cause treatment delays and stress that may affect clinical outcomes, according to a press release. "Wearable devices allow for continuous, objective capture of patient-generated health data outside of the clinical setting, which minimizes travel and has the potential to have a more realistic and equitable assessment of a person's health status," Isabel Friesner, clinical data researcher at University of California, San Francisco, said during the presentation.