10,054 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
The authors derive the $1T number from $1.3T in total cost savings and $304B in incremental spend (incremental spend is due to insuring more people). The $1.3T in cost savings come from five big buckets: lower pharmaceutical prices, Medicare-level payments to providers, reduced administrative overhead, less fraudulent billing, and fewer avoidable emergency department visits and hospitalizations.
The buckets themselves don't necessarily survive much scrutiny.
Take "Medicare-level payments to providers". Hospitals have an operating margin of 2-5%. Medicare pays 50% less than private insurance. So doing this would require either layoffs, cutting salaries for doctors/nurses/etc, or both. This may well be the right decision for society as a whole--that's a big part of the debate here--but there's no free lunch.
The line item of "fewer avoidable emergency department visits and hospitalizations" assumes greater insurance coverage leads to greater access to primary care. It's true that great primary care prevents hospitalizations, and can be a net cost saving under certain assumptions [1]. But, we're actually in a primary care shortage. Existing insurance payments for primary care are low enough that private practices are going out of business and fewer residents are going into family medicine. Cutting rates (the paragraph above) would make this worse.
For "less fraudulent billing," a lot of people in the industry believe that Medicare has a large amount of undetected fraud. That's unfortunately the flip-side of reduced administrative overhead. The authors assume an 8% savings here, but the 2003 paper they cite uses the word "fraud" only twice and doesn't give a number.
Healthcare reform is hard.
[1] Reasonable breakdown on the economics of advanced primary care models: https://olearykm.medium.com/the-cost-equation-for-new-primar...
This study is interesting in that it quantifies the benefit as 12.5 extra dementia-free years. People with 0 risk factors had 30 years of dementia-free survival, vs 17.5 years for people with all 3 risk factors measured.
The risk factors in this specific study are blood pressure, diabetes (HbA1c), and smoking. You could reasonably also include cholesterol/ApoB, inflammation (hs-CRP), and perhaps Lp(a) as potential risk factors.
The ZEUS trial tested an anti-inflammatory injection (ziltivekimab). These phase III results showed a hazard ratio of 0.99, i.e., no effect.
This is evidence for a lipid-first model -- i.e., lipids drive oxidation, which then triggers inflammation.
I found the underlying chemistry and scientific process really interesting.
I tried to answer two main questions in this specific article: 1. What was the actual process from 2013-2026 that led to enlicitide? 2. If we were restarting this work in 2026, would AI have helped? If so, at what specific stages?
Happy to answer questions if people have 'em.
But the sweat angle is could add signal alongside other techniques like Raman or MIR spectroscopy, and maybe a combination of these and an ML system would be accurate enough to use in practice.
Unfortunately this particular intervention wasn't successful (not much difference between treatment and control groups). But conceivably, afib triggers is something that varies from person to person and perhaps a future design would tease this out.
It also reduces Lp(a), the strongest hereditary risk factor for heart disease, by 28%.
Previous PCSK9 inhibitors like Repatha were injectables (similar to GLP-1s). Only about 1% of people eligible for injectable PCSK9 inhibitors use them, so having a convenient daily pill is a potentially huge win for prevention.
Rather than hand-engineering these features, most modern systems are built on a wearable foundation model that's been trained reconstruct the signal (similar to how an LLM is trained to predict the next word). Those foundation models are picking up on these signals and likely others.
You're right that calibration with a cuff is required of all systems currently on the market.
The Signal Ring folks' claim they can do a blood pressure number without calibration, which is quite novel and seems to be their "secret sauce". They did run a clinical study as well, so presumably more details will come out whenever that's published.
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.