9,735 karma · joined January 17, 2011
Co-Founder at Empirical Health (https://empirical.health). Don't die of heart disease.
Before: Co-Founder @ Cardiogram (ML for heart health)
CTO at Sift Science (YC S11, machine learning to fight fraud)
Data Science @ UCSF Cardiology
HealthCare.gov rescue team
Google (Android speech recognition, search ads ML)
brandonb.cc
To be eligible, you need either a BMI of >=35, or a BMI of >=27 and a set of specific health conditions (uncontrolled hypertension, chronic kidney disease, pre-diabetes, etc). You also can't be qualified for GLP-1s under Medicare's previous Part D coverage.
In practice these criteria are narrower than they look. Of the 13 million Medicare beneficiaries with overweight or obesity, about 4 million actually qualify, because most are excluded for already having a diagnosis, like type 2 diabetes or sleep apnea, that covers a GLP-1 another way.
For example, Medicare is launching a new program in July that pays a fixed rate for achieving defined outcomes, like lowering blood pressure or cholesterol. Medicare's explicit goal here is to create incentives to automate the repetitive parts of care delivery with software. (Much of preventive cardiology is surprisingly algorithmic and guideline-driven, so this is more plausible than it seems.)
This reverses the incentives of the current system, where CPT codes incentivize doing more "stuff" (but not necesarily delivering the most effective care efficiently).
If you're a software engineer who cares about health, and have been sitting on the sidelines till now, I think the next few years are a really interesting time to make a contribution.
The "Vitamin D" moniker has just stuck around since it was named in 1922.
That trial used a dynamically-adjusted dosage of a vitamin D3 supplement, where dosing was set as to keep blood levels within a target range of 40–80 ng/mL. IMO part of the reason this trial is showing better results than the previous clinical trials of vitamin D supplementation quoted in the above article is that vitamin D has bad effects if too low and too high. Adjusting the dose dynamically to achieve an optimal range gets you the benefits without some of the negative effects.
- Universal Lp(a) testing recommended for everyone - Lp(a) is the strongest hereditary risk factor for heart disease.
- Risk Equations switched to PREVENT, which predicts both 10-year and 30-year risk.
- Treatment is now recommended for younger adults, based on these 30-year risk scores.
- CAC scans recommended in more cases (for intermediate risk).
- Specific LDL targets are back, after being removed in the 2013 guideline
Unfortunately, official medical guidelines take a while to catch up. It's only in 2026 that the American Heart Association put out updated dietary guidance which makes it official that most people shouldn't limit dietary cholesterol. Fiber and saturated fat are more important drivers of blood cholesterol, which is still recommended as a major risk factor (alongside blood pressure, inflammation, HbA1c, and so on).
The post also tries to explain why there was a limitation on eggs and cholesterol in the first place, starting from the 1968 guidelines.
(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.)
The other uses are honestly pretty standard rhetorical patterns; they do not seem especially AI-flavored to me.
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).
This statement is from lipidologists (basically, people who study cholesterol), but the American Heart Association released similar guidelines in Mar 2026 which recommend Lp(a) testing for everybody.
Only 1 in 400 people test Lp(a) today, although that's up 22x in the last decade: https://www.empirical.health/blog/lpa-testing/
Four major drugs are in development that target Lp(a) specifically; one of them lowers it by 94%.
The patient population was 3600 people with high-risk diabetes, but not atherosclerosis. So they're at elevated risk compared to the average person.
PCSK9 inhibitors are still expensive (about $1,800 per year), with pretty limited insurance coverage. But this will likely change as the evidence builds.
It includes both mathematical formulas and PyTorch code.
I found it a bit more practical than the Sutton & Barto book, which is a classic but doesn't cover some of the more modern methods used in deep reinforcement learning.
These newest guidelines from the AHA/ACC, released yesterday, operationalize this partly by using a new set of equations that predict cardiovascular risk over a 30-year period (rather than 10 years).
> EMA-based training dynamics like JEPA’s don’t optimize any smooth mathematical function, yet they provably converge to useful, non-collapsed representations.
All the papers say EMA avoids “representation collapse” without justifying it. Didn’t realize there were any theoretical results here.
The specific biomarkers being predicted are the ones most relevant to heart health, like cholesterol or HbA1c. These tend to be more stable from hour to hour -- they may vary on a timescale of weeks as you modify your diet or take medications.