By Khalid Sattar – Healthcare has spent decades optimizing care delivery. It has invested billions in clinical excellence, advanced diagnostics, modern facilities, digital records, and operational efficiency. Yet one of healthcare’s greatest challenges still occurs before care even begins. Patients struggle to access the system.
Read More
The math was simple and unforgiving: the company couldn’t scale to serve new clients without physically scaling its call center, and physically scaling a call center is slow, expensive, and hard to reverse. The question isn’t whether AI can handle provider calls, it’s how much capacity is sitting on the table until it does.
By Sanjay Subramanian – Some of the most expensive work in healthcare is the work no one counts as care. Physicians and their teams chase authorization status, resubmit records, clarify denial reasons, and explain coverage confusion to patients.
By George Bosnjak – As someone currently building my fourth healthcare technology company, I’ve learned to value practical, hard-working solutions over the flashy hype touted by Silicon Valley. So, when Pope Leo XIV released his encyclical Magnifica Humanitas addressing the rapid rise of artificial intelligence and its potential consequences, I paid attention.
By Jay Anders MD – Behind the polished demos and bold promises, a large language model remains a fast, powerful next-word predictor. It reviews an enormous matrix of possibilities and generates the word it calculates should come next. It does this so well that it can appear to reason. But it does not reason as clinicians do. It predicts.
By Gurkan Camok MSc – Healthcare AI is often introduced through a performance metric: accuracy, sensitivity, specificity, or speed. Those measures matter, but hospital adoption is determined by a different question. Can the system deliver useful, understandable guidance inside a real clinical workflow without creating new delay, confusion, or risk?
By Tharun Kumar Nallamothu – Healthcare organizations are investing heavily in artificial intelligence, but in practice, only a small number of systems make it past the initial rollout and actually get used at scale. The gap isn’t coming from a lack of innovation. If anything, the pace of model improvement has been impressive.
By April Miller – Healthcare organizations face financial pressure as operating costs and administrative demands increase. At the same time, patients expect clear pricing and greater transparency throughout the billing process.
By Sanjeev Menon – Automation was always the floor, not the ceiling. The next phase of patient access depends on two capabilities the first generation was never built to deliver.