Radiologists, why does 95% of the AI you piloted never reach a patient? That number should stop you cold. According to Rad AI, citing the MIT Nanda report, the AI pilot failure rate across healthcare sits at 95%. Not 30%. Not 50%. Ninety-five percent of AI pilots succeed on paper, then stall in practice. That is not a technology problem. That is a healthcare culture problem. And in radiology, it is getting harder to ignore. Here is the data picture Rad AI laid out in a July 2026 piece worth reading: • 95% of AI pilots fail to scale beyond the pilot stage, per the MIT Nanda report • Implementation costs alone can exceed $200,000, per work out of Duke ...

Medicaid just drew a line that science says is wrong. And millions of people in long-term recovery are standing on the wrong side of it. The One Big Beautiful Bill passed about a year ago. It drastically altered Medicaid. Now we are in the rulemaking phase, where CMS explains to the public exactly how those changes get implemented. According to Faces and Voices of Recovery, we are in the midst of a rulemaking process that is highly complex and covers several aspects of the legislation. Two provisions deserve your full attention right now. First: work requirements for Medicaid recipients. There is a Medically Frail exemption, but it requires you to meet one of five specific categories: • Blind or disabled ...

A congressional staffer sits at a desk in 2026, not November, not after the midterms. They are already requesting that companies preserve information. Already cultivating external sources. Already drafting investigative frameworks. This is the reality according to Healthcare Dive, citing attorneys from Holland and Knight's Washington, D.C., office. Congressional Democrats are planning investigations into major healthcare topics if they win majorities in the 120th Congress this November. But the preparation is not waiting for election results. Here is what they are targeting: • Drug pricing strategies and manufacturer price increases • Insurance premium increases and cost-sharing structures • Facility fees and billing practices • Compliance with the No Surprises Act • Medical debt collection and credit reporting practices • Private ...

💥 Everyone calls the ACA enrollment crash a fraud story. The people who left did not commit fraud. They just ran out of money. 📰 According to KFF Health News, ACA plan enrollment continues to drop after Congress allowed enhanced ACA subsidies to expire. Insurers say they are expecting to hike premiums even more next year. And many Americans are publicly saying they can no longer afford coverage. The Trump administration, for its part, attributes the enrollment drop to a crackdown on fraud. 🎯 That framing is a political escape hatch, and I am done watching people accept it. When the price of something doubles and people stop buying it, we do not call that a demand problem. We call ...

😤 Everyone said the Medicaid cuts would hurt adults. They lied. The kids were in the crossfire the whole time. 📰 According to First Focus on Children, one year ago President Trump signed into law the biggest cuts to Medicaid in U.S. history in H.R. 1, the One Big Beautiful Bill Act. Congress insisted the cuts targeted only the childless adult population. New data published this week, on the law's one-year anniversary, says otherwise. Medicaid and CHIP together cover approximately 36 million babies, toddlers, school-aged kids, teens, and young people transitioning into adulthood. These youth comprise nearly half of all Medicaid and CHIP beneficiaries. Congress did not protect them. It miscalculated, or it lied. 🔥 Here is my take. The ...

Investors just bet $250M that AI can fix the doctor-patient conversation. Abridge just closed a $250 million Series D round, bringing its total valuation to over $2.8 billion. For context, Abridge builds ambient AI that listens to clinical conversations and automatically generates structured clinical notes inside the EHR. No manual charting. No after-hours documentation. Just a physician and a patient, talking. This is not a small bet. This is a signal. 💡 Here is what the numbers are actually saying. Physician burnout costs the U.S. healthcare system an estimated $4.6 billion per year, according to a Stanford study. A significant driver of that burnout is documentation burden. Clinicians spend, on average, nearly two hours on EHR tasks for every one ...

Your AI scribe just documented something it never heard. Ambient AI is one of the fastest growing technologies in healthcare right now. Hospitals and clinics across the country are rolling out tools like Nuance DAX, Ambience Healthcare, Abridge, and Nabla to reduce physician burnout and cut documentation time. And the early results are genuinely impressive. Physicians report saving 60 to 90 minutes per day. Satisfaction scores are up. Burnout is down. But here is the question almost no one is asking out loud: What happens when the AI gets it wrong? A growing number of clinicians are quietly flagging instances where ambient AI tools have generated inaccurate summaries, missed critical nuance, or in some cases, documented clinical details that were ...

🚨 FQHC CEO, is your revenue model built for what just happened? Nearly 450,000 New Yorkers just lost their health coverage. On July 1. Not a projection. Done. 📰 According to Time, those New Yorkers were enrolled in New York's Essential Plan, a taxpayer-subsidized program established in 2015 under the Affordable Care Act. It had covered more than a million New Yorkers at extremely low cost. The coverage loss was triggered by HR 1, the "Big Beautiful Bill," signed into law on July 4, 2025. The federal government withdrew the funding that allowed New York to use ACA tax credits for certain legally present immigrants. The state changed eligibility. Hundreds of thousands lost their plans overnight. 🔍 Here is what ...

AI is reading scans faster. But is it reading them better? The radiology world is celebrating speed. AI triage tools now flag critical findings in seconds. Worklists are getting prioritized. Turnaround times are dropping. And on the surface, that sounds like a win. But a growing body of evidence is raising an uncomfortable question: when radiologists trust AI-flagged cases too heavily, do they spend less cognitive energy on the cases the AI calls clean? This is called automation bias, and it is not a new concept in aviation or nuclear safety. It is, however, a newer conversation in diagnostic imaging. Here is what the research is starting to show: - AI models are trained on curated datasets. Real-world scans are ...