The role of technology in prioritizing patient safety is becoming increasingly apparent. Research is emerging around the capability of machine learning (ML) to potentially reduce medical alert fatigue ...

In today’s fast-paced healthcare environment, nurses face constant decisions on what to address first. Using structured prioritization frameworks helps them balance patient safety, acuity, and ...

After COVID-19, most medical centers were faced with the double pressure of increasing number of patients and limited medical human resources, resulting in the crowding and shortage of hospital beds 1 ...

Continued innovations in healthcare require a heightened focus on patient safety. Novel treatments, technologies, and care models promise to improve patient outcomes but can also introduce new patient ...

Cassidy Lockwood exemplifies dedication and expertise as an oncology nurse, consistently practicing evidence-based medicine and prioritizing patient-centered care. Her teamwork, communication skills, ...

The majority of patients with cancer receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework, PANOPLY (Precision Cancer Genomic Report: ...

A team from Cleveland Clinic has developed a new model for prioritizing patients waiting for a lung transplant, aimed at improving outcomes and reducing deaths among those in need of donor lungs. The ...

Ever wonder if the latest and greatest artificial intelligence (AI) tool you read about in the morning paper is going to save your life? A new study published in JAMA led by John W. Ayers, Ph.D., of ...

For trauma patients suffering from massive blood loss, a care approach that emphasizes halting bleeding and restoring circulation first, rather than the traditional approach of restoring the airway ...

Optimizing Outcome Prediction in Diffuse Large B-Cell Lymphoma by Use of Machine Learning and Nationwide Lymphoma Registries: A Nordic Lymphoma Group Study The majority of patients with cancer receive ...