After my recent post on the Healthcare Blog about calculators (aka nomograms) for risk assessment and treatment guidance, I got an email from James Michaelson at the Laboratory for Quantitative Medicine (what a name!) at Harvard Medical School. He pointed me to some calculators they've cooked up - and they are simply outstanding, pushing far beyond anything I've seen out there otherwise. The lab's philosophy is centered around something it calls "binary biology", and the mission statement is fascinating:

Each of us is but the aggregate consequence of the enormous number of fundamentally discrete events that occur among the many molecules, genes, and cells of which we are comprised. For more than a decade, our group has used this viewpoint to try to make sense of multicellular organization and its diseases.

I love the embrace of randomness, the almost existentialist detachment of it. And of course, it's exactly right: We have consciousness, but we are but biological machines. The fact that we think we're unique and supreme individuals with some sort of higher purpose is what so often leads us astray, especially when making healthcare decisions. It turns out that when given a prognosis - say a 15% chance of a drug working - we tend to assume that we're going to be in the 15% for whom it works rather than the much-more-likely 85% for whom it doesn't. Psychologists call this "illusory superiority," or the "Lake Wobegon effect," after Garrison Keilor's riff on a place where "...all the children are above average."

Some people may take this as depressing or even nihilistic sentiment, but I actually find it somewhat empowering: It basically says, OK - we're all just some cell functions and protein expressions and chemical interactions. So how well can we understand those functions and interactions, how can we quantify them, in order to best predict how they will combine to our specific circumstance? Now that's a calculator I want my doctor to have.

Like the nomograms at Memorial Sloan Kettering, the LQM has some cancer calculators. Under the rubric of CancerMath.net, there are therapy, outcomes and survival calculators for breast cancer, melanoma, and renal cell carcinoma. And over at PreventiveMath.net there's a splendid calculator that determines best practices for people based on their age, gender, smoking status, height and weight. This calculator largely draws on my favorite assessment source, the US Preventive Service Task Force recommendations. But the outcomes are delivered in a clear and easy-to-understand form: by "days of life added."

So if I, a 41 year old, 5-foot-11-inch, non-smoking male who weighs 155 pounds, were to begin taking a baby aspirin a day, I'd gain 286 extra days of life (on average), and if I were to get assessed for hypertension I'd gain 139 days. It's pretty powerful stuff. (my only complaint is a title like "PreventiveMath.net" is going to scare some people off). Here's a screenshot of the clean, clear interface:

And each of these listed items is a hotlink to more information. Check out the Lab's full set of tools and statements at LifeMath.net. I'm sorry I didn't know about Dr. Michaelson's group earlier, so that I could include these terrific tools in my book. Still, I'm glad to get the word out here.