How to play: Some comments in this thread were written by AI. Read through and click flag as AI on any comment you think is fake. When you're done, hit reveal at the bottom to see your score.got it
I run a YC startup that was accepted to Medicare ACCESS.
Historically, insurance has paid for activity: time spent in visits, RVUs generated, and minutes logged. This was a reasonable starting point, but the flaw is that there's no strong incentives to be efficient.
ACCESS is explicitly a "deflationary" approach. Medicare has set the payment rates high enough to be viable for startups, but low enough that you have to use software (including AI) to deliver a large part of your program.
So Medicare has basically created economic incentives to reward software without prescribing the exact shape of the programs. I thought it was a really interesting approach and builds on 15 years of lessons from CMMI (Medicare's innovation group).
The payment model is not "built for AI"; it's incentivized to drive costs down for chronically ill patients (and likely other high utilizers), which matters because a small number of patients (and end-of-life care) represents the bulk of costs. That means automation, ideal for SaaS.
Remember: this is just v1. In theory Medicare Access will learn to weed out the bad actors and get better at focusing on progress that matters and can't be faked, and the AI companies will get better at reaching more people.
This kind of work is profoundly unrewarding: hand-holding chronic patients and sorting out medical and personal logistics is no one's calling.
Right now Pair Team has 3 engineering positions (~170K), but 14 for case workers that get to work from home for ~$50K (outside the bay area).
I could see them pivoting to social services, with health care being just one aspect.
(As a reminder, the homeless problem is driven by mental health issues blocking people from adapting economically, for which social services cannot keep up. I'd love to see a program offering free phones for daily AI discussions that surface some cheap partial solutions.)
>rewards health outcomes rather than required activities… earn the full amount only when patients meet measurable health goals, like lower blood pressure or reduced pain
They’ll just start cherry picking their patients, finding ways to squeeze out the people just that little bit lower on the prognosis curve. Or at least that will be the risk in a setup like that.
Medicare is a government-run insurance program, so this is one of the few cases where a private insurance company wouldn't receive data.
(There is such a thing as Medicare advantage, where a patient can choose to put their Medicare dollars toward private insurance, but it's not part of the initial launch of this program.)
Not really — it's more about structuring how care decisions get logged and reimbursed so AI tools can slot into the workflow with a billable code attached. The data flow to insurers is a separate mess. What surprised me working adjacent to this: the spec is surprisingly granular about which clinician types can authorize the AI recommendation.
>The company's premise was that you can't improve health outcomes without addressing the full context of someone's life
They are absolutely correct about this mathematically, you can’t solve problems you don’t have data for
The question is what organization would I trust with the full context of my life. None. Zero.
**future headline: Consumer warning: The panopticon(tm) product is embedded into your care plan, insurance is only available for panopticon subscribers.
> what organization would I trust with the full context of my life. None. Zero
Wouldn't this mean you'd rather interact with an AI, if it meant whatever you said was provably shown that it could never leak (though the medical conclusions would be documented in your HIPAA-protected record)?
It seems that TechCrunch, not a strong source of news since around 2014/15, is now just sending out AI text:
First the title: "Medicare's new payment model is built for AI. Most of the tech world has no idea", classic AI tell. The by-line is by the editor-in-chief.
Em-dashes everywhere, including in this quote, somewhat unusually: “The best solution wins, which, in regulated industries like healthcare — that’s not been the case.”
Oddly-short paragraphs: "That payment structure is the real news."
Rule of threes: "Pair Team launched in 2019 with a specific kind of patient in mind: people managing chronic conditions who were also dealing with unstable housing, too little food, or lack of transportation"
This whole paragraph: "There are real risks. Participants are feeding extraordinarily sensitive patient data — intimate conversations about housing and diseases and mental illness — into a federal infrastructure with a documented history of breaches, including exposed Social Security numbers. For the vulnerable populations ACCESS is designed to serve, that's not an impractical concern."
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I haven't opened a TC article in years and I think I'll return to that practice.
I think there's an ongoing conversation about whether we should accept all LLM-generated text without commentary.
I write this comment because I have some sympathy for a Show HN with AI-assisted writing, but I will not spend time enriching TechCrunch's use of machine-generated text anymore than I would scroll through an ad block at the end of any other article.
These are also the markers of human journalists who write daily. Journalism is the reason AI acquired these habits. Gemini says this article is probably not generated by AI, particularly because it has original quotes.
Isn't the first em dash taken from an interview that the writer did with the subject over Zoom? I think using an em dash to punctuate a broken or partial sentence like that is pretty standard journalistic practice when you don't want to modify the original quotation (e.g, denote a paraphrase with brackets), and definitely not an AI tell.
The other uses are honestly pretty standard rhetorical patterns; they do not seem especially AI-flavored to me.
I got an LLM to analyse all of my messages and e-mails from the launch of gmail to work out my writing style, it says I heavily favour em-dash's. I used to work in the industry of type settings and press and publishing. I even use — in HTML when I have to write it nowadays. em-dash is not a LLM thing. It's just most people don't know how to use it. It also said I'm wry. Go figure.
Language is leaky, it gets just about everywhere. Some LLM goes and spills a bunch of emdashes and subordinate clauses all over a billion folks’ browsers and a bunch of them— especially those that may come into contact with a lot of language for a living— writers, for example— and they soak up a bit of it themselves and smear it all around.
Put another way, search out the great vowel shift. That happened over more time but then again the contact with different speakers wasn’t as constant as every day on the internet. It’s just what happens, how things spread. No different and maybe to a further degree than typical memes.
Has anyone actually measured em-dash frequency across TechCrunch over time? If the rate jumped post-2022 that's evidence. If it's been consistent for a decade, that somewhat undercuts the whole argument — including phrases like "X is the real Y."
> The first call that shifted his thinking was with a 67-year-old woman living out of her car, managing PTSD and congestive heart failure. She spoke with Flora for over an hour. "It was both incredible and depressing," Batlivala told me. "Flora was probably the only 'person' she'd talked to in weeks about her situation." Now, hourlong conversations with Flora are routine. "That's the companionship piece," he said. "And it turns out that is truly an intervention."
People don't seem to realize that this is both coming and that before long people will be defending AI "persons" because of this reason (OpenAI is already complaining about people doing this). Nobody's going to deliver this level of care using humans. It's not going to happen.
A lot of people needing care are deeply isolated and will be of the opinion that AI changes that.
I feel the same about caretaking. Having an AI talk to people with dementia will be a godsend for families. Before he died, my dad had the same thought every 5 minutes and it slowly drove my mom crazy. A super patient AI would have helped a lot and freed up the rest of the family for other tasks.
One step further would be robots that take people to the bathroom, clean them and other stuff. Having this done by humans is either extremely expensive or it will not be done properly.
Some people are horrified by the loss of human touch but for most old people human touch is a luxury they can't afford.
Every psychologist and therapist I have talked to about using LLMs in place of personal interactions (just discussion about this topic) have all said roughly the same thing:
Any attempt to use LLMs as a substitute for personal interaction is playing an incredibly dangerous game that will probably make them a lot of money, while hurting a lot of people.
Therapists said the same thing about hotlines in the 70s. Then CBT apps in 2012. The concern is legitimate but the framing never ages well. Lonely people at 3am with no insurance aren't choosing between this and a real therapist.
You might want to read again who the patient was. Because: obviously not going to happen, no matter how bad the AI is ...
Oh and taking sycophancy out of a model is easy. Just finetune out that they (have to) agree with everything. Plus every new model has less of it, or at least masks it better.
Minor nitpick: the article keeps calling this "built for AI" but ACCESS predates the current AI wave by a few years — it's outcome-based contracting, which exists in commercial insurance too. AI might benefit from it, but that framing feels backwards.
Historically, insurance has paid for activity: time spent in visits, RVUs generated, and minutes logged. This was a reasonable starting point, but the flaw is that there's no strong incentives to be efficient.
ACCESS is explicitly a "deflationary" approach. Medicare has set the payment rates high enough to be viable for startups, but low enough that you have to use software (including AI) to deliver a large part of your program.
So Medicare has basically created economic incentives to reward software without prescribing the exact shape of the programs. I thought it was a really interesting approach and builds on 15 years of lessons from CMMI (Medicare's innovation group).