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# Let's Spread the Risk! (I Mean in Health Care... Not Flu)

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Reprint from Open Salon.

As you all know by now, how health care in the US should be overhauled has been the subject of intense discussions over the last few months. So far, based on what I've been reading on the interwebs, it seems what everyone's focused on are peripheral topics, like how the US Government is never good at managing anything except for the military (and I'm sure some would argue that they can't even do that); or how a single-payer system would lead to the fall of Western civilization (or at least the United States, which is of course synonymous with Western civilization, depending who you ask). I've yet to read anything about the important concept of spreading the risk, with a few exceptions (see here).

My wife has begged me to simplify this, so here goes:

Say you've got a casino. It's a small, pathetic little casino, so it only has about 100 desperate gamblers in it at any given moment. Now say it's possible for one guy on his last dime to win \$10,000. This is a 1% probability. In order for your pathetic little casino to avoid bankruptcy in the case this happens, each of those 100 patrons needs to lose at least \$100.00 at the tables (pass the loaded dice).

But, what if two gamblers win \$10,000? Well, the casino is still screwed. In order to avoid the casino going under if that happens, each patron needs to shell out at least \$200.

But say that you've got one of those really cool, swanky casinos that have tame lions or topless servers or something. This casino has 10,000 gamblers at any given moment, each losing, say \$110.00. Now if 100 of them (1%) win \$10,000, since the casino has already earned \$1,100,000, paying out \$1,000,000 isn't a problem. Even if 105 people win \$10,000, your casino is sill laughing since the total payout is still below \$1,100,000. So, by increasing the number of patrons, the casino can spread the risk of paying out 10 grand in the event more people win than anticipated (a 1% probability).

Easy, right? Stay tuned for my lectures on the Poisson-gamma and Conway-Maxwell-Poisson distributions.

As discussed in the first comment below this article here, the best method to spread risk is when you include everybody from a given population. The comment writer used the example of the people contributing to social security in the US.

With this in mind, the concept of spreading the risk can easily be applied to health care. As just mentioned, it'd be better to spread risk by including everybody in a given state with its own health care system than having several smaller groups contributing to private insurance. Remember that under the current health care policy in the US, each heath care plan is considered a distinct group. There are probably hundreds of these plans or groups in each of the 49 states, if we exclude Texas, which is apparently about to secede.

I'm sure I had you at 'topless', but if you'd like more proof, check out what the American Medical Association had to say in a January 2008 op-ed titled, interestingly enough, "Spreading the Risk":

One of the greatest challenges in reforming the U.S. health system is how to make health insurance affordable and available for chronic-care patients, and anyone else who has expectedly high medical costs.

The current approach surely isn't working, given how it contributes to the rising number of people without health insurance.

In employer-based insurance, the low-risk patients subsidize high-risk patients; all pay similar premiums rather than being assessed charges based on their likelihood of using health services. That makes care more affordable and accessible for the high-risk patients.

But many low-risk patients, especially young adults and some lower-income workers, have joined the ranks of the uninsured rather than pay the high premiums insurers deem necessary to support all patients. In the individual market, high-risk patients struggle to get any insurance at all. Insurers instead cherry-pick low-risk patients -- or, as recent cases in California have shown, kick some patients off the rolls the moment they are deemed to be high-risk.

The solution to solving both these problems is replacing the current market-regulation approach to high-risk patients.

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Dominique is an Associate Professor in the Zachry Department of Civil Engineering at Texas A&M University. His research work aims at reducing the negative effects associated with motor vehicle crashes. When he's not developing mathematical and (more...)

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