HIMSSCast

Founder/CEO Eric Langshur explains how health systems are rethinking traditional venture and commercialization structures. 

Healthcare has accumulated enormous amounts of data, but understanding and analyzing it remains beyond busy clinicians’ capabilities. Technologies like telehealth, wearables, and AI will enable clinicians to spot risk factors earlier and create more personalized, preventive plans that keep people healthier for longer. In the third episode of  our "Future of Virtual Care "  podcast series, hear digital healthcare humanist Philippe Gerwill talk about how virtual care can evolve to a core driver of longevity. 

Advocate Health's nursing informatics leaders discuss how using artificial intelligence technologies in frontline care requires a hands-on, collaborative shift in training that is worth making. 

Lily Liu, divisional director at Western Health, explains why specialised clinical tools demand stricter safety standards, deeper workflow integration, and cross-functional leadership than entry-level AI.

PeriGen's fetal monitoring analysis helps clinicians detect potential complications earlier during labor.

Health systems are under enormous pressure from rising labor costs, workforce shortages, and administrative complexity. However, many of them can realize significant growth by converting existing demand into completed care. In this episode, Assort Health’s Vice President of Health Systems, Imaad Rashied, discusses how AI can automate meaningful portions of operational work to close care gaps and take the burden of care coordination away from patients and staff. 

Chi Mei Medical Center Stroke Center director Dr Hsieh Meng-Tsang says clinical AI should not just flag problems but also suggest meaningful actions.

AI can help clinicians identify patients at risk of sepsis earlier by analyzing patient data and supporting faster, more informed treatment decisions, says Dr. Bobby Reddy Jr., cofounder and CEO of Prenosis.

Redesign Health's Aron Szanto discusses Argus, an AI-powered code review system designed to identify bugs and inconsistencies in AI-generated code.

Oncology is becoming increasingly data-intensive, bringing together imaging, pathology, genomics, clinical history, and patient-generated data. AI presents an opportunity to connect these information sources and surface meaningful insights without adding burden for clinicians. Hear from experts at the intersection of healthcare, AI, and software platforms on what it takes to operationalize these systems in complex care environments.