Once big data systems agglomerate enough data about you to predict whether you are likely to get sick or badly injured, insurers will be able to deny coverage (or charge so much for it that it amounts to the same thing) to anyone who is likely to get sick, forcing everyone who might ever need insurance into medical bankruptcy, and turning Medicaid into a giant "high-risk pool" that taxpayers foot the bill for.
For this to happen, we only need for insurers to have discretion over whom they insure (something they lost under Obamacare) and for insurers to not care too much if they over-exclude a few low-risk people who false-positived into the big data "high-risk" bucket -- when you go fishing with a dragnet, you always get a few dolphins along with your tuna.
The existence of more personal health data and better algorithms for sorting it suggest that private medical insurance is just incompatible with 21st century technology.
Cathy "Weapons of Math Destruction" O'Neil makes the case forcefully in her Bloomberg column: "The asymptotic limit is a subpopulation of poor, sick people who cannot afford insurance and live off Medicaid, and a bunch of healthy people who are well covered but don’t need insurance. As Republicans have learned in recent weeks, high-risk pools are incredibly expensive."
So what to do? One approach would be to handcuff insurance companies in their use of big data technologies, preventing them from using predictive algorithms to assess risks -- or even from collecting the data in the first place. If you think that's utterly inconceivable, I agree.
The other option is universal health insurance, meaning that everyone would be covered by something akin to Medicare. It wouldn't solve all the problems that Mankiw mentions -- particularly the question of which expensive treatments to allow -- but it would at least maintain the kind of risk-sharing that insurance was meant to achieve.
Big Data Is Coming to Take Your Health Insurance [Cathy O'Neil/Bloomberg]
(Image: Rudy Herman, CC-BY)