Sergey Brin's Search for a Parkinson's Cure

Article at Wired magazine

Brin’s tolerance for “noisy data” is especially telling, since medical science tends to consider it poisonous. Biomedical researchers often limit their experiments to narrow questions that can be rigorously measured. But the emphasis on purity can mean fewer patients to study, which results in small data sets. That limits the research’s “power”—a statistical term that generally means the probability that a finding is actually true. And by design it means the data almost never turn up insights beyond what the study set out to examine.

Increasingly, though, scientists—especially those with a background in computing and information theory—are starting to wonder if that model could be inverted. Why not start with tons of data, a deluge of information, and then wade in, searching for patterns and correlations?

This is what Jim Gray, the late Microsoft researcher and computer scientist, called the fourth paradigm of science, the inevitable evolution away from hypothesis and toward patterns. Gray predicted that an “exaflood” of data would overwhelm scientists in all disciplines, unless they reconceived their notion of the scientific process and applied massive computing tools to engage with the data. “The world of science has changed,” Gray said in a 2007 speech—from now on, the data would come first.

Patient Needs versus Profit

Article by Atul Gawande at The New Yorker

When you look across the spectrum from Grand Junction to McAllen—and the almost threefold difference in the costs of care—you come to realize that we are witnessing a battle for the soul of American medicine. Somewhere in the United States at this moment, a patient with chest pain, or a tumor, or a cough is seeing a doctor. And the damning question we have to ask is whether the doctor is set up to meet the needs of the patient, first and foremost, or to maximize revenue [emphasis added].

I found the Atul Gawande article from this article, which talks about the 3 key parts of the news that you usually don't get (historical context/big picture, sources of the information, what we don't know).

What Gawande did was to structure his search for truth as a quest narrative. Instead of hiding the details about how he comes by his information, he makes that the very focus. Along the way, he makes us apprentices in his quest for truth. We finish the article with a highly refined sense of how Gawande has acquired and verified the information he presents, as well as a framework for further inquiry of our own.

Back to Gawande's article, this is a great analogy and really hits home for me (pun was NOT deliberate):

Providing health care is like building a house. The task requires experts, expensive equipment and materials, and a huge amount of coördination. Imagine that, instead of paying a contractor to pull a team together and keep them on track, you paid an electrician for every outlet he recommends, a plumber for every faucet, and a carpenter for every cabinet. Would you be surprised if you got a house with a thousand outlets, faucets, and cabinets, at three times the cost you expected, and the whole thing fell apart a couple of years later? Getting the country’s best electrician on the job (he trained at Harvard, somebody tells you) isn’t going to solve this problem. Nor will changing the person who writes him the check.

A great analysis of recission

Article at Taunter Media

Half of the insured population uses virtually no health care at all. The 80th percentile uses only $3,000 (2002 dollars, adjust a bit up for today). You have to hit the 95th percentile to get anywhere interesting, and even there you have only $11,487 in costs. It's the 99th percentile, the people with over $35,000 of medical costs, who represent fully 22% of the entire nation's medical costs. These people have chronic, expensive conditions. They are, to use a technical term, sick.

An individual adult insurance plan is roughly $7,000 (varies dramatically by age and somewhat by sex and location).

It should be fairly clear that the people who do not file insurance claims do not face rescission. The insurance companies will happily deposit their checks. Indeed, even for someone in the 95th percentile, it doesn't make a lot of sense for the insurance company to take the nuclear option of blowing up the policy. $11,487 in claims is less than two years' premium; less than one if the individual has family coverage in the $12,000 price range. But that top one percent, the folks responsible for more than $35,000 of costs – sometimes far, far more – well there, ladies and gentlemen, is where the money comes in. Once an insurance company knows that Sally has breast cancer, it has already seen the goat; it knows it wants nothing to do with Sally.

If the top 5% is the absolute largest population for whom rescission would make sense, the probability of having your policy cancelled given that you have filed a claim is fully 10% (0.5% rescission/5.0% of the population). If you take the LA Times estimate that $300mm was saved by abrogating 20,000 policies in California ($15,000/policy), you are somewhere in the 15% zone, depending on the convexity of the top section of population. If, as I suspect, rescission is targeted toward the truly bankrupting cases – the top 1%, the folks with over $35,000 of annual claims who could never be profitable for the carrier – then the probability of having your policy torn up given a massively expensive condition is pushing 50%. One in two. You have three times better odds playing Russian Roulette.