Research

With the extension of the Acute Hospital Care at Home (AHCaH) waiver through 2030, healthcare systems are scaling home-based recovery programs. To understand the drivers of patient compliance in remote care, we can look at the findings from Vivalink’s 2026 Acute Patient RPM Survey, which polled patients managing severe conditions like cancer and cardiac disease. […]

The acute hospital at home (HaH) program is a healthcare model where patients receive acute care services in their homes rather than in a traditional hospital setting.  The Centers for Medicare & Medicaid Services launched the Acute Hospital Care At Home program in November 2020, to give hospitals expanded flexibility to care for patients in

Recent research highlights the profound impact of remote patient monitoring (RPM) technology on hospital readmission rates for cardiac patients. By examining data from two pivotal studies, it becomes clear how telemedicine and remote monitoring solutions actively work to improve patient outcomes while significantly reducing the burden on our healthcare systems. Tackling the High Hospital Readmission

Continuous physiological monitoring has become a cornerstone in advancing patient care, particularly through wearable technologies that offer real-time insights into vital signs. This article examines research into continuous physiological monitoring. Drawing from a large-scale observational study published in the Journal of Clinical Medicine, we explore how the BioIntelliSense BioButton®, an FDA-cleared device, supports clinical decisions

Inadequate oral feeding in newborns is a clinical condition where infants are unable to consume enough nutrition by mouth to maintain growth and hydration. It is a barrier to hospital discharge in preterm infants. It often keeps otherwise healthy babies in the hospital for extended periods, even after they’re medically stable, solely to support feeding.

Atrial fibrillation (AFib) is a leading cause of stroke and heart failure, yet many cases go undetected until complications arise. Early identification is critical and atrial fibrilationa and artificial intelligence (AI) together open new frontiers in making that possible. Researchers at Cedars-Sinai Health System recently developed an AI algorithm capable of detecting signs of AFib