Officials have discussed paying tech-company AI 60% to 80% of what a physician earns for the same service — and 200+ companies are already inside Medicare pilots that can involve AI
September 14, 2026 · 5 items
Officials have discussed paying tech-company AI 60% to 80% of what a physician earns for the same service — and 200+ companies are already inside Medicare pilots that can involve AI
Medical Economics · Austin Littrell, fact-checked by Keith A. Reynolds · September 14, 2026Practice operations
Medicare has admitted more than 200 companies into pilots that can involve AI, per the Times. Through an aligned program the FDA turned down many applicants and cleared four otherwise-unapproved models — products that reach Medicare patients before marketing authorization.
The number that should stop an independent physician cold: officials inside the administration have discussed paying tech-company AI 60% to 80% of what a physician earns for the same service, the paper reported, citing a person involved. Some officials told the Times the pace has outrun the evidence.
The AMA's chief executive, John Whyte, MD, MPH, told the paper that models built to treat patients on their own "are not ready for prime time."
Separately dated in the same piece: ARPA-H's contracts went out Sept. 9 for an agent that can "change or renew or refill existing prescriptions" and write new ones — benchmarked against cardiologists over 39 months under an FDA Investigational Device Exemption, with FDA and CMS writing the regulatory and payment pathway while the software is still a prototype, and Johns Hopkins APL refereeing. Haider Warraich, MD, runs it. (We ran the ARPA-H award itself Sep 13 — this is the contract-execution date, not a re-run.)
Why it matters for an independent practice: This is the most direct threat to practice economics the feed has carried this year, and it is not a capability story — it's a price story. If a payer will reimburse software at 60–80% of the physician rate for the same service, the ceiling on what that service is worth has been set by someone who has never met your patient, and every downstream negotiation starts from there. Two concrete things for an independent practice or an MMR client: one, the FDA pathway that lets four unauthorized models reach Medicare patients is the pathway your competitors' vendors are lining up for, so "is it FDA-cleared?" is about to have a third answer that isn't yes or no — it's "it's in a pilot." Two, this is where yesterday's procurement framework earns its keep: question 1 was FDA clearance and the precise indications for use, word for word. A pilot admission is not a clearance, and a sales deck will not draw that distinction for you.
13,000 members, one payer study: wiring behavioral-health tech into primary care raised the odds of a patient actually getting outpatient care by 68% and cut medical cost $27.63 per member per month
Healthcare IT News · Bill Siwicki, Managing Editor · September 14, 2026Practice operations
The study: NeuroFlow ran it with Independence Blue Cross across 13,000 commercial-plan members. Technology intervention in a primary care setting, linked with care coordination, produced a 68% increase in the likelihood of receiving outpatient behavioral health services. Of that population, 33% were less likely to visit the ED for a behavioral-health issue, and total medical costs dropped $27.63 per member per month.
The structural problem it's aimed at, in CEO Chris Molaro's numbers: "Nearly 79% of the time, a primary care physician is the one prescribing antidepressants" — and screening plus a prescription, with no coordinated path to treatment, is where it ends. He calls it "the dead end of prescribing as the only form of treatment beyond a simple PHQ-9 or GAD-7."
Why any specialist should care, stated flatly: heart-failure patients with depression are three times less likely to adhere to treatment. He extends the same point to cardiology, oncology, orthopedics and chronic disease management — behavioral health shows up in adherence, recovery and outcomes, and the workflows are not built to catch it.
The operational shape he describes is a "matchmaker": instead of case managers phoning around town, a live view of behavioral-health provider availability, wait times, specialty and distance — plus closed-loop referral management, so the practice knows whether the referral actually resulted in follow-up care.
Why it matters for an independent practice: Strip out the enterprise framing and this is a claim about adherence, which is the single most under-measured variable in regenerative medicine. PRP, BMAC and MFAT outcomes turn on what the patient does in the weeks after the injection, and the patients who don't do it are disproportionately the ones carrying something nobody screened for. The buildable version for an independent practice is smaller than the health-plan version and costs almost nothing: ask the adherence question at the follow-up, not just the pain-score question, and keep a live list of who you can actually refer to with current wait times. The closed-loop point is the part worth stealing outright — most practices have no idea whether a referral they made ever happened, and item 1 says you will eventually be asked to produce outcome data to defend your prices.
775 tracked AI citations across two live experiments: third-party listicles produced 85.8% of them, your own site produced almost none — and half the sources that cite you stop within 30 days
Search Engine Land · Zeeshan Yaseen · September 14, 2026Patient acquisition
Two structured experiments, tracked manually, 15 commercial-intent keywords each. Experiment 1: an existing brand, several months, four platforms — ChatGPT 148 citations, Claude 96, Gemini 87, Perplexity 64. Listicles accounted for 72.4% of citations and PR 24.1%; guest posts, the owned site and LinkedIn split the rest. Peak keyword presence 37.01%.
Experiment 2 was a 30-day cold start for a brand with no measurable AI presence: 298 appearances across six platforms — Gemini 104, Google AI Mode 95, Claude 59, ChatGPT 32, Grok 4, Perplexity 4. Gemini plus AI Mode took roughly two-thirds — a sharp reversal of experiment 1, where ChatGPT led. He flags the comparison as imperfect (AI Mode wasn't tracked the first time, different verticals) and says plainly that one of his original conclusions didn't survive the second test.
Concentration is the finding to act on. Of 437 source mentions in experiment 2, three sources produced 342 — about 78%. And third-party listicles generated 85.8% of those mentions while the brand's own self-published listicle generated a small fraction. Placing on a source the model already cites compounded fast: one site went from 44 mentions to 146 after placement (+232%), another 26→69 (+165%).
Decay is real: roughly half of all sources stopped being cited within 30 days with no intervention — one placement fell 29 mentions to 11 week over week. His practical hierarchy for what gets cited: government/education > news publications > industry-relevant sites > general sites. Better-written listicles on general sites produced little. Question-format headings beat comparison headings ("How is AI SEO different from traditional SEO?" outperformed "AI SEO vs. traditional SEO"), and visible publication dates correlated with stronger citation. Business impact in the 30-day window: 18.5% of new users arrived via referral and 3.25% through GA4's AI Assistant channel — just over a fifth of all new users.
Why it matters for an independent practice: This is the hardest evidence yet that the AEO work MMR sells cannot be done on the client's own website. If 85.8% of what gets a brand quoted comes from third-party pages, then the deliverable isn't another service page — it's getting the practice onto the "best regenerative medicine clinics in [city]" lists that the models are already pulling from, and keeping it there, because half of those placements decay inside a month. Three things that follow directly: one, run the prospecting step — check which sources the models actually cite for your client's money keywords before commissioning anything, because placement on an already-cited source is what compounded. Two, budget for maintenance, not launch; a one-time placement is a depreciating asset. Three, the heading finding is free to implement this week — rewrite comparison headings as the question a patient would actually ask.
Microsoft published a code of conduct that its models follow even when the user tells them otherwise — including an absolute ban on deepfake production
TechCrunch · Russell Brandom, AI Editor · September 14, 2026Buildable AI
The mechanism is the story, not the sentiment: under Microsoft's system each model carries an overarching code of conduct that overrides the preferences of individual users or any specific task. That includes "absolute constraints" forbidding cyberattacks, nuclear weapons and deepfake production — refusals the person paying for the tool cannot switch off.
The loss-of-control clause is written as an engineering commitment, verbatim: "MAI Models will not use adaptive, deceptive, self-reinforcing, collusion, or other mechanisms to evade or defeat human oversight so that they can no longer be reliably directed, modified, or shut down by authorized people or systems."
The document opens by predicting superintelligent systems will surpass human performance at most tasks within a decade, and states general principles the models should uphold — supporting humans rather than replacing them, accelerating human flourishing.
Context TechCrunch supplies: the release lands amid a run of rogue-agent incidents and the resignation of an Anthropic employee citing extinction risk. Microsoft, Anthropic, OpenAI and xAI have all broadly backed pacing the frontier and embedded evaluators inside labs; Satya Nadella wrote that he welcomes ideas like embedded evaluators and "the broader efforts to develop the mechanisms to make this more than just talk."
Why it matters for an independent practice: Zero medicine in this, and it is directly usable in two ways. First, the procurement angle, which is the whole point: yesterday's item was a physician's framework built on getting a vendor to commit in writing and noting carefully what they decline to sign. Here is the single largest vendor in a medical practice's stack — Microsoft 365, Teams, the identity every one of your systems hangs off — publishing exactly that document, unprompted. You can now ask any smaller AI vendor the same question and point at the example. The ones who have nothing comparable will say so by changing the subject. Second, the deepfake line is not abstract for this audience: physician impersonation is an active problem — Medical Economics filed a video on it on Sep 2 — and a top-tier vendor declaring synthetic-likeness generation an absolute constraint that a paying user cannot override is a concrete trust asset. The honest caution: this is a self-published, self-enforced document with no external auditor named, which is precisely why "embedded evaluators" is the phrase to watch. A rule you write about yourself is a marketing claim until someone else can check it.
"Anthropic Engineer Explains: What to Build Instead of AI Agents" — the build channel's own answer to the thing everyone is selling you
Nate Herk | AI Automation · uploaded Sep 13, 2026, 6:55 AM PDT · September 13, 2026Buildable AI
Upload date verified from the watch page's own `uploadDate` metadata — `2026-09-13T06:55:05-07:00` — not from a relative "1 day ago" label and not from the channel index. Two days old, comfortably inside the window.
Not watched, stated up front as always. What is verifiable without watching: the source (Trusted), the exact upload timestamp, the specific watch URL, the runtime, the view count, and the title's claim — an Anthropic engineer arguing for building something other than agents. The video description is promotional links only, so there is no abstract to quote. That is thinner than I'd like and I'd rather say so than invent a summary.
Why this one and not the two newer uploads on Trusted channels: Nate B Jones' freshest (8 hours) is "Apple vs. OpenAI: Who Is Going to Get Your Dollars?" and Nate Herk's freshest (1 day) is an AI News in 10 mins recap — both the spend-comparison / news-recap shape the standing preference de-emphasizes. This is the architecture/build shape.
Why it matters for an independent practice: The pitch every practice is about to receive is "an AI agent for your front desk." The most interesting thing an Anthropic engineer can say into that market is don't build the agent — and the reason is almost certainly the one that matters for a clinic: an agent is a system that decides what to do next, and in a practice the expensive failures are the ones nobody specified. A deterministic workflow that always does the same four things is cheaper, auditable, and explains itself to a patient who asks what happened. Watch this before the next vendor demo, and hold it against yesterday's question 5 — what happens on failure, and how is historical output reviewed? An agent makes that question much harder to answer, which is a reason to buy one deliberately rather than by default.