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| | | News and views you ought to know about:- CMS is soon to pilot AI for prior authorization of Medicare claims in six states. The program, announced over the summer, will kick in on New Year’s Day and run through 2031 in Arizona, Ohio, Oklahoma, New Jersey, Texas and Washington. Critics say that, by moving ahead with the plan, the Trump Administration is acting hypocritically. The charge has to sting since CMS Administrator Mehmet Oz, MD, said last June that private payers’ use of prior authorization “frustrates doctors, sometimes results in care that is significantly delayed and erodes public trust in the healthcare system.” Oz’s boss, HHS Secretary Robert F. Kennedy Jr., has made similar comments.
- And now the looming turnabout is—no surprise—inviting barbs from political rivals. Administration officials are “talking out of both sides of their mouth,” Washington Democrat Rep. Suzan DelBene tells NBC News. “It’s hugely concerning.” A physician and policy watcher at Ohio State University adds that the federal government wants to slap private insurers on the wrist for doing the same thing they’re doing.
- For its part, CMS says the program—called WISeR for Wasteful and Inappropriate Service Reduction—will help reduce clinically unsupported care. How? By “working with companies experienced in using enhanced technologies to expedite and improve the review process for a pre-selected set of services that are vulnerable to fraud, waste and abuse.” The WISeR model, CMS adds in a page updated this month, “empowers patients to partner with their healthcare providers on the most clinically appropriate care plan; protects the taxpayer by decreasing fraud, waste and abuse; and focuses providers on care that has the most impact on the well-being of people with Medicare.”
- You’d think someone, somewhere had trialed AI for breast cancer screening on a large, randomized population. Wouldn’t you? Well, if you did, you’d be wrong—until now. UCLA and cousin institution UC-Davis are leading the new research. They’re calling the study the PRISM Trial for Pragmatic Randomized Trial of Artificial Intelligence for Screening Mammography. And they’ve got funding from the Patient-Centered Outcomes Research Institute in Washinton, D.C. Indeed, PCORI is kicking in $16M to support the work. UCLA’s news operation says the study will involve hundreds of thousands of mammograms interpreted at academic medical centers and breast imaging facilities in California, Florida, Massachusetts, Washington and Wisconsin. The team will use ScreenPoint Medical’s Transpara model and, for workflow integration, the Aidoc aiOS platform. Also in on the research are Boston Medical Center, UC-San Diego and the Universities of Miami, Washington and Wisconsin.
- The investigators will look at not only AI’s contribution to cancer detection and recall avoidance but also perceptions of—and trust in—AI-assisted care among radiologists as well as patients.
- “There’s never been a trial of this scope looking at AI in breast cancer screening in the U.S.,” says Hannah Milch, MD, breast imaging specialist at UCLA Health. “The results will help inform not just clinical practice but also insurance coverage, technology adoption and patient communication.”
- Healthcare AI was, is and always will be useless without reams of healthcare data to teach it, test it and keep it humming. Data, one might say, is fuel in AI’s tank. No vehicle is useful if it has no gas in its tank or charge in its battery. Margaret Lozovatsky, MD, the American Medical Association’s CMIO and VP of digital health innovations, suggests as much. “What we haven’t done [yet] is streamline our ability to take [clinical] data—and there’s so much data in there—and truly translate it into actionable information for the clinician,” Lozovatsky says in conversation with a peer. “I want to see us move to a place where we liberate that data and present it to clinicians in real time.” AI, she continues, allows us to turn disparate pieces of data into a story. Once that starts happening, U.S. healthcare can “start to truly trend what is happening to our patients and present that information to the clinician.” Once that happens, “we can focus on making those best clinical decisions and not on sifting through vast amounts of information.”
- And since only 3% of all healthcare data is currently being used, there’s a ton of targets for AI to salivate over. Trouble is, most of these stores are rightfully off limits due to rules and regs around patients’ protected health information. The walls and bars can’t keep the Big Tech AI players from daydreaming, though. Take Amazon Web Services, where they’re especially charged up over the dawn of the agentic AI era. “AWS meets customers wherever they are on their agentic AI journey, offering everything from ready-to-deploy agents to tools for building sophisticated custom solutions,” explain three AWS thought leaders. “Unlike traditional AI that simply responds to prompts, these agentic AI solutions can reason, plan and take autonomous actions to accomplish complex healthcare goals with minimal human oversight.”
- Actually they’re thinking even further ahead. “By bridging the gap between data collection and intelligent action, AWS is enabling a future where healthcare systems don’t just react to illness but proactively nurture health,” the trio enthuses in a Sept. 22 blog post. In this developing scenario, “every data point, every interaction, every decision is part of a larger, intelligent system designed to serve the whole person.”
- Shadow AI isn’t going away anytime soon. Too many employees like bringing their own AI to work too much. And an awful lot of them don’t want their employer to know. In some industries, the trend is merely troubling. In healthcare it can introduce serious risks to patient safety. To manage the growing hazards of shadow AI, healthcare security leaders have to ferret out stealthy AI users. “This means deploying tools that continuously detect unauthorized applications, AI usage and data flows—especially those involving sensitive patient information,” IBM’s VP of data security Vishal Kamat says to TechTarget. “What makes shadow AI particularly dangerous is its invisibility and autonomy.” Get the rest.
- Bracingly honest quote of the day. My job when I’m sitting at the table [with the Florida Board of Pharmacy] is to protect the patients of that state. The challenge I face is that the regulated entities such as the pharmacies—or the third-party vendors of technology platforms such as AI—likely have more information about these technologies than I do.—Jeff Mesaros, PharmD, JD, MS, immediate past president of the National Association of Boards of Pharmacy (NABP)
- A college student warns peers about using AI for medical advice. If students consider AI answers as adequate substitutes for professional medical judgment, we risk delayed diagnoses, inappropriate medication use, mental health issue aggravation or false reassurance. And when something goes wrong, AI cannot be held accountable. If we entrust our minds and bodies to AI in our most fragile moments, who takes responsibility when the consequences are life or death? The answer is no one.—Undergrad Goran Narancic at Brown University
- Research news of note:
- From AIin.Healthcare’s sibling news outlets:
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The 4% that changes AI medical transcription
Keyword mistakes break notes. Speechmatics’ new medical model posts a 4% keyword error rate and 96% keyword recall on medical terms, alongside 93% general accuracy. Names, doses, and timelines land on the first try. Read more. Is Your AI Notetaker Putting You at Risk? AI meeting tools can quietly introduce security and compliance risks when they record or store conversations without the right safeguards. In healthcare, that means potential exposure of PHI and costly governance gaps. This free Security Checklist from Fellow gives IT, Ops, and compliance leaders seven simple checks to evaluate any AI notetaker against standards like HIPAA, SOC 2, and ISO 27001. Use it to spot red flags early and ensure your organization stays protected. Before Shadow AI spreads inside your org, download the checklist to reduce your risk here.
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