A new paper in NEJM Catalyst makes the case that AI might finally bend the cost curve in healthcare, just not in the direction patients were hoping for.
“Show me the incentives and I’ll show you the outcomes.” National health expenditures jumped 60% to nearly $5T per year in the last decade, and widely cited estimates from McKinsey suggest AI could shave about $360B off the annual total.
- Venrock’s Bob Kocher and Brian Zhao teamed up with USC’s Erin Duffy to explore the areas where those savings will allegedly materialize, and they found the same overarching problem with all of them.
You get what you pay for, and fee-for-service pays for volume. No matter where they looked for potential savings, the authors believe AI is even more likely to have the opposite impact.
- Drug Development – Faster AI-driven discovery means more new drugs and more eligible patients, but a healthier population doesn’t happen overnight. The net effect for the foreseeable future is more pharma spending, not less.
- AI Scribes – Under FFS, freeing up physician capacity is a direct path to more visits, which is a hop and a skip away from more fees, tests, referrals, and prescriptions.
- DTC “AI Doctors” – Could be deflationary if they replace pricier human encounters, or inflationary if every chat ends in an escalation and a testing cascade. Early evidence points to cascades.
- RPM and CCM – AI dramatically cut the cost of delivering remote care by reducing the clinician time needed to analyze the data, but blanket deployment means more billable monitoring and more incidental interventions (see: United Healthcare’s now-paused move to narrow RPM coverage to two conditions).
- Admin Automation – The AI-generated savings are real, but in consolidated hospital and insurance markets, that translates to higher margins, not lower prices.
Flip the model, flip the outcome. Every one of those buckets cuts the other way under value-based care arrangements, where the AI force multiplier is more likely to get pointed at complex patients and preventive care rather than volume and more volume.
- At-risk providers are already using AI to expand access, improve screening, and deliver better treatment. The AI upside is there, it’s just not showing up in the places most people are looking.
- The authors’ most direct policy fix is simple: more value-based care, and more outcomes-based reimbursement efforts like CMS’s new ACCESS model.
The Takeaway
While AI holds enormous potential to improve healthcare, this paper highlights exactly why it takes more than new tech to bend the cost curve. It also takes the right reimbursement models wrapped around it.