By Natosha McNeal – Agentic orchestration is what makes scale achievable. It sets the boundaries an agent operates within and records every step it takes, with a clinician or compliance check built in wherever the stakes call for it. This allows AI to take on real work while the people accountable for it remain in control.
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This month’s AI report includes news and updates from Apella, Causaly, Definitive Healthcare, Luma Health, MDaudit, ModMed, MUSC Health, Nabla, PerfectServe, Relatient, Sage, Sheba Medical Center, and ThinkSono.
By Theo Sakalidis – When I worked on the buy side of healthcare private equity, we underwrote a lot of physician practices. And I can tell you that the clinical work, the thing the doctors trained a decade to do, was almost never the thesis. The thesis was the back office.
By Pat Williams – When healthcare leaders talk about ambient AI scribing, they almost always picture the same setting: a primary care physician, managing a panel of 15 to 20 patients, dictating notes between appointments. That’s the benchmark the industry has built its expectations around.
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 Abhishek Gupta – Despite the strong investments in data and analytics, VBC’s promise remains largely unfulfilled. Most organizations have invested in analytics dashboards that surface insights but don’t drive action. A closer look reveals why.
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?