In these days before the book comes out – tomorrow! – there have been some wonderful write-ups about the book. Just thought I should note them here.

Kent Bottles wrote a piece titled “Check Lists and Decision Trees” that mulled whether structures like a decision tree can help people negotiate the huge information dumps that come with data-driven medicine. Besides the flattering proximity to Atul Gawande’s book, Bottles was generous in grasping the fact that I’m not claiming a decision-tree paradigm depends solely on rational decision making (which is inevitably unrealistic) but that I’m trying to find a way to bring our rational capacity together with our emotional needs – and therein lies better healthcare.

Brian Ahier wrote a terrific post on O’Reilly Radar, largely about the book. Riffing off one of my set phrases – it’s data, not drugs – Brian – who’s not only a health IT expert and blogger but also a member of his city council – nailed the promise and riddle of turning to a data-intensive model for healthcare. As he puts it, “Putting the patient at the center of healthcare and creating a strategy to process all of health data available today is a great start towards meaningful healthcare reform.” I was especially glad that Brian recognized the flexibility of my three principles for patient-centric health: Early is better than late; Let data do the work; and Openness is a powerful thing.

And Susannah Fox of the Pew Center lobbed a characteristically provocative take recently on e-patients.net. Her take: that the book could be retitled What to Expect When Your Expecting a Long Life. (Fine with me; those What to Expect books are huge!). In addition to the flattering notion that the Decision Tree compelled Susannah – a longtime health expert – to rethink her own health decisions, she astutely recognizes that I’m not just calling for self-tracking gadgets and gizmos – I’m really arguing that we should use whatever tools we have, including messaging from the FDA and other official bodies – to make health information clearer and more personalized.

It’s very heartening to me that three people who constitute experts in the field all seem to think the book is in tune with their own knowledge – that the notes are right, and that the composition is in the right key. My hope, of course, is that the book will also find a larger, less-expert audience, but my hunch is that unless I convince the experts, the lay audience won’t be there. If these three are any indication, I’m on the right track.

Lastly, I want to address some Twitter kibbitzing that these ideas are simplistic, naive, or somehow dangerous. I take some assurance that so far, this chatter comes from people who haven’t read the book – because the book itself goes to great lengths to explore both the promise as well as the perils and challenges of engaging in patient-centric healthcare (challenging both for the patient and the system). There is ample evidence presented in the book; the bibliography alone runs to 15 pages. Of course, I’ll answer this head-on when I have the chance. But broadly, I’ll offer this:

There is hope in the book, yes; there is a simplicity to the idea, indeed – but naive or simplistic it is not.

In my continuing experiments with getting the message out about the Decision Tree book through all available means and media, I came up with this short video that tries to convey the main challenge facing individuals and their healthcare, and the opportunity that a decision-tree approach offers (engagement improves outcomes).

I was ably aided by my friend David Knowles, a gifted writer and musician who contributed the soundtrack. My thanks to him. Frequent readers of this blog will no doubt be familiar with the ideas, but my hope is that the video may have some potential to engage a broader audience (so link to the video – it’s on Youtube here – and help spread the word!). As the video makes clear, we indeed face in a health crisis in the US (not just a healthcare crisis), and making people aware of the problem and the potential for them to work towards the solution is essential to moving the needle back towards better public health.

Also should say that MP3 podcasts of the Introduction and Chapter 1 are up and available. Enjoy!

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.

[this is a post I did for TheHealthcareBlog, crossposted here]

Want to know the future of medicine and healthcare in one sentence?

For my money, it goes like this: The real opportunity in healthcare is to combine our personal data with the huge amount of general biomedical and public health research, in order to create customized information that’s specific to our person and our circumstance. We need relevance, and the right information at the right time will help us make better choices for prevention, helping us stay healthier longer, it’ll help us navigate diagnosis, letting us select screening tests that are useful and not unnecessarily fearful, and it’ll let us make better decisions on care and treatment – when we’re trying to choose among various treatments to find our way back to health.

It’s in the last category – care and treatment – that I wrote a recent post at the Huffington Post about one man’s story with prostate cancer. Tom Neville got a diagnosis and then had to struggle to find information to help him make sense of what to do. Ultimately, he chose surgery, but the difficulty of the choice led him to create Soar Biodynamics, a company that offers decision-making support for men assessing their prostate health.

You can read his story here and learn more about his tool here, but for the purposes of this post I wanted to consider the kind of decision-making tool he created. It’s called a nomogram, and it’s one of my favorite discoveries in researching The Decision Tree.

A nomogram is basically a calculator – a way to assess our risk or outcome for a particular condition. A nomogram starts with an interface where a few telling datapoints can be entered, and then turns to an algorithm that crunch those numbers together with broader data about the condition. The result is a statistical prediction – the prediction can concern the outcome of the disease, or it can be a recommendation for particular treatment (a medical nomogram is not to be confused with mathematical nomograms, which are tools for calculating geometrical something or others).

The Framingham Risk Calculator, which calculates your risk of heart disease, is a kind of nomogram. Memorial Sloan-Kettering Cancer Center, the research institute and hospital in New York City, has developed almost a dozen nomograms for a range of cancer conditions. There are tools for predicting the spread of breast cancer, a tool for assessing lung cancer risk among smokers, a tool for predicting the prognosis after colon cancer surgery, and more. Dr. Pierre Karakiewicz at the University of Ottawa has developed nomogram.org, which offers prediction calculators on four different types of cancer. Nomograms are one of the best examples of Decision Tree thinking, the sorts of tools that are easy for patients and doctors alike to use and understand—particularly when they’re available online and free of charge. They’re brilliant and auspicious because the turn research around so that it faces the patient: An individual can interrogate medical science for how it applies to his specific circumstances, rather than having to navigate through stacks of research papers and findings for some wisp of relevance.

Nomograms are especially powerful when they’re combined with a screening test, because they help people understand what to make of the test and point to what to do with the result. They immediately customize the clinical data, be they nanograms-per-milliliter figures or spots on mammograms. Nomograms let patients ignore the inscrutable repository of jargon that is medical research in favor of something personal, something real, and something to go on. They allow us to make sense of a screening test’s result, and allow us to take some measure of meaning from it.

The University of Texas at San Antonio, for instance, has developed a prostate risk calculator that lets a man enter his PSA level along with his age, race, family history, and a couple of other metrics and churns out his risk of developing prostate cancer. Importantly, the calculator also calculates the risk of a high-grade cancer, accounting for the fact that not all prostate cancers are lethal. The value of such a tool, says Ian M. Thompson, professor and chairman of the department of urology at the University of Texas Health Science Center at San Antonio, who developed the calculator, is that it turns the PSA figure from one isolated data point into one of many inputs. “We need to build in characteristics about the person, their age, their race, their family history,” says Dr. Thompson. “It’s not just what one test tells us.”

Nomograms, of course, are no substitute for a doctor’s definitive assessment and treatment (or better yet, more than one doctor). And they are only as good as the data that goes into them; if they’re not kept up to date on the latest information and research, they can lead people astray. But especially for conditions where we have some agency – where we can take actions today that can enhance our tomorrow – they are a terrific tool.

The catch with nomograms is that they must be developed one disease at a time, which means they don’t scale up so well. Each one takes a great deal of work and expertise. But if I had millions of dollars for philanthropy, I’d spread it around to smart researchers across a lot of fields where nomograms could help people assess their risk for disease and potentially take actions today. It would be money well spent.

Calculator image via Flickr by Ian Ruotsala

For several months I’ve been a regular at the Quantified Self meetups, where people of great passion for self-tracking present the results of their experiments in self-monitoring. These vary in everything from people who record themselves sleeping (in search of better sleep) to people who monitor the amount of coffee they drink to the mililiter – and then compare that to their productivity.

One thing that I’ve noticed is that there’s a general presumption, among the QS crowd, that self-monitoring – i.e., feedback – is an experiment with evidence behind it. That is, that self-monitoring works, insofar as it helps people reach a goal, whether it’s to drink less coffee or get better sleep or lose weight. But there’s not often any actual evidence presented, beyond these experiments (where N=1, often enough). So tonight, drawing on research from my forthcoming book, I’m presenting a few points of fact that support the notion that feedback can lead to improvements in health. This post will serve as a primer for those not in the audience, and for those in the audience a repository of links to the research cited.

Thus, here are five claims or presumptions about the benefits of feedback, with the evidence.

1) Observing our actions leads to better actions.

For this one I turn to the National Weight Control Registry, a nifty study out of Brown University that is tracking more than 5000 people who have successfully lost weight and kept it off. The registry basically studies what these people are doing right. And in addition to the common-sense things like exercise often and change to a healthier diet (less fat and sugar, more vegetables and whole grains), the registry has found a strong association between the simple act of stepping on a scale and sustained weight loss. In particular, the study found that 75% weighed themselves at least once a week, and 44% weighed themselves at least once a day. Tracking our behavior leads to better behavior.

2) Engaging with Our Health Leads to Better Health

The idea here is that self-tracking not only improves the direct behavior, but that it has broader health implications, and can be quite positive for overall health. The evidence comes from a wonderful study called ALIVE! (short for A Lifestyle Intervention Via Email), conducted by the research wing of Kaiser Permanente.

This study sent out weekly reminder emails to study members, each message tailored to specific health goals. So the email would nudge them to meet their goal of eating, say, 3 vegatables a week, or getting exercise twice a week, and so forth. The subject would respond whether or not they met these goals, thus tracking their progress and tailoring the results for next week’s email. The results: study subjects reported more than 50% greater improvement in concentration and productivity than a control group, and were 50% more likely to successfully change their diet. More broadly, they reported significantly better physical and mental quality of life. A link to the research is here.

3) When patients participate, their outcomes improve.

This idea goes a step further than simple self-tracking – it argues that by giving people an opportunity not only to track their health but to use that data to make decisions regarding their health, there is an upside, in terms of better outcomes. This one speaks directly to physicians and care providers who are reluctant to open the door to their own decision making and involve the patient in the discussion of treatments and care choices. Well, the Centre for Studies in Family Medicine at the University of Western Ontario took up this question – Does involving patients in their care, giving them a participatory role in their healthcare, improve the outcomes?

Their research showed a clear YES – involved patients had better recovery, better emotional health, and had a surprisingly 50% fewer diagnostic tests and referrals. In other words, they were happier with their care and the care was more successful. Not bad.

4) When patients participate, healthcare can be more effecient (read: Cheaper).

This one is going one step further, claiming that not only do self-tracking patients have better health, but that they actually have cheaper healthcare, to boot. For the answer to this idea, I come to a Boston University study that enrolled nearly 30,000 employees of the EMC Corporation in an online tracking program called DASH for Health.

The patients tracked their weight, blood pressure, and other information, and received tailored recommendations for dietary choices that they may not be aware of (such as: adding cream to your coffee instead of milk amounts to nearly 8 sticks of butter a month). The study found that among patients with an increased risk for cardiovascular disease, employees using the DASH program had on average $814 less spent on their healthcare annually. For all patients, the costs were basically neutral.

5) Data means more when it’s our data.

This one is a response to the idea that healthcare woes in the US like obesity are an information problem – that there’s a lack of resources or research or information for people. That’s pretty clearly not true – we don’t lack for information or health advice. But what we do need is relevant information, information that’s tailored to our specific circumstances and conditions and that spells out the choices that our own situation holds.

This requires matching up our data with the right research data at the right time – when it’s more specific and most useful. This is relevance. Annette O’Connor at the Ottawa Hospital Research Institute has done some extraordinary work in this area, looking at the growing body of decision aids available to patients to help them understand their options. Increasingly, these decision aids are turning up online in the form of nomograms – decision tools that allow us to input our personal information – data gleaned from our physicians or our own tracking – and then use that information to guide us to a decision that’s right for us.

A couple years ago, O’Connor conducted a meta-analysis – a study of studies – examinging several hundred decision aids, with the goal of determining what effect they had, if any, on outcomes. It turns out that decision aids can actually be quite effective in improving our decisions and our outcomes. Patients using decision aids, O’Connor found, had significantly better understanding of benefits and harms, they perceived they were receiving better care, and they chose significantly less surgery (about 25% less). When they understood the pros and cons, it seemed, patients chose to avoid the knife more often, and were happier for it.

This research shows that self-tracking isn’t just for a geeky fringe – it’s actually a sensible strategy that anybody can take advantage of to gain some control of their health. Yes, often it requires a computer – but that’s a good thing, insofar as it’s easier to track and monitor stuff that way.

Just a quick post to acknowlege a few changes here. First, we’ve redesigned the website for www.thedecisiontree.com. Based on recommendation from Wired’s Nick Thompson, I hooked up with Jefferson Rabb, a brilliant designer and thinker (& musician!) to retool the blog.

Jeff also gave life to something I’d been thinking about but couldn’t, on my own, have ever created: The Decision Tree widget, which lets you build your own Decision Tree for a variety of health concerns. The widget is a great way to give people an idea of what the Decision Tree trope actually means, to put the idea to work. Jeff has done a superb job turning this whim into reality, and I’m eager to hear what people make of it. It’s also an experiment in – possibly – viral media. The widget is sharable, and you can embed it on Facebook or your own site (click the little icons or the “EMBED CODE” line at the bottom). Please feel free to play with it, pass it around, and let me know what comes of it.

Next, there’s an adaptation of the book in the new February issue of Wired. You can see it here online, but it even works better in the print magazine.

Last, I’ve put up an excerpt of the book – which is now less than a month from official release – here on the site. It’s chapter 1, and it gives a good peek at the style and ambition of the book, which (I hope) offers a synthesis of public health research, technology trends, and reporting.

OK – that’s all for now. Next word here will be about science & healthcare, not book marketing.

Just a quick update that I’ve begun posting now and again at the Huffington Post, for their living section. Some of the material will be familiar to readers of TheDecisionTree.com, where Brian & I wade deeper into the science of personalized medicine, but the more-populist forum of HuffPost is a good way to test the principles and larger messages that are sometimes taken for granted here.

My first post a few weeks back, Welcome to the Era of Personalized Medicine, argued that the idea of tailoring healthcare to individuals has arrived – but that it may take some work on our part to take advantage of it. The basic premise is that personalized medicine is about data, more than drugs (specifically the notion of pharmacogenomics where drugs are matched to specific genetic traits).

My second post just went up today: in the tired & true format of service journalism it’s a list: 3 Ways to Take Control of Your Health Today. In this one I confront the disconnect between the surfeit of health information and the failure most people make to actually turn that information into action (what Aristotle called akrasia). In an attempt to bridge disconnect, I propose that there are clear health benefits just by choosing to engage in our health – just by asserting control over our health. And that’s where I offer three ways to get started: Choosing to Care about our health, Deciding What to Care About, and finally Knowing That There’s No Such Thing As Perfection (either in our behavior or, ultimately, in our health).

Anyway, take a look. As always, feedback is welcome, here or there.

One of the themes of The Decision Tree – both the blog and the book – has been the idea of self-tracking: the notion that when people monitor their health, they are more likely to improve their health. In the aggregate, that’s true – research shows that when people start to track or even care about their health (when the start to feel vested in it, and in control of it) they tend to have better outcomes.

That’s a powerful and important message, one that I believe hasn’t really gotten widespread recognition – and I hope one benefit of the book is that this message will spread and help change people’s lives (as well as the approach of care providers, who may have heretofore been reluctant to engage their patients in their own care).

But as I’ve been talking about these ideas in recent weeks, one frequent and important question has come up repeatedly. I’ll paraphrase it like so: Does self-tracking scale? Or, as a non-geek would say, Is self-tracking just for geeks? Do you have to be a nerd to do it? In which case, is it a realistic strategy for the rest of us, those of us who aren’t comfortable with data and navel-gazing and tricked out gadgets? In other words: Is this a transformative strategy for mainstream society, or a trick for the marginal few?

Well, good question.

My answer comes in two parts. First, I completely acknowledge that right now, most of this self-tracking stuff is for the geek crowd. It’s got all the hallmarks of a early-adopter phenomenon: the tools aren’t always easy to use, they don’t always work right, and the whole idea is a bit complicated. In other words, there’s lots of friction to the notion. But early-adopters, as the term implies, tend to pave the way for the rest of us. They iron out the kinks and spot the bugs that make the next generation of tools and technologies easier and friendlier to use for all of us. I see every indication that the same thing is happening here. And in this case, the upside isn’t just a better way to watch television (the way Tivo early adopters paved the way for DVR ubiquity), but a better way to be healthy. That’s a big upside.

Second, what’s happening with self-tracking today is rather remarkable. It’s not just the idea that tracking tends to help people make better health decisions (though that’s true, and that’s huge in itself). It’s the idea that the principles of self-tracking tend to synch up, rather remarkably and serendipitously, with the principles of effective behavior change.

This is no small thing: there have been literally billions of dollars spent in recent decades researching how to get people to behave better (i.e., less self-destructively). The result has been a great set of basic understanding of 1) what we should do for better health and 2) how we can do it. Unfortunately, these principles have tended not to scale beyond the resources of any one research project. In other words, a research project will spend a lot of money figuring out how people should behave, but just when they prove their point, the money runs out and the project is over. The insights may be published, but there’s almost never that necessary second step that puts those insights into action.

Until now. Consumer technologies that let people track their own health results synch up – to a remarkable extent – with the insights of research. Part of it’s coincidence, and part of it is planning, but whatever the reason, the fact is that technology has finally progressed to the point that these insights – which boil down to giving people access to their data and then letting them share and compare their data with others – are the mainstay not just of research, but also of the nascent self-tracking industry.

Case in point: the iTunes apps store. A year or so ago, I did a post on this blog about the new health apps that touched on self-tracking. There were about 10 apps. Today, there are more than 5,000 apps that touch on self-tracking in the Apps store. Yeah, some of them are crap, and many of them are rip-offs. But surprisingly, many of them – including many free ones – are quite well thought out, easy to use, and intuitive.

In other words, they’re not just for geeks. They’re for all of us. This is a big idea. And it’s going to get bigger.

The big news of the day on my radar is the news that the American Cancer Society is cautioning that there may be a thing as too much screening, particularly for prostate and breast cancers. The New York Times has the story.

The ACS’s recommendation is based on the work of Dr. Laura Esserman, a professor of surgery and radiology at the University of California, San Francisco, and Dr. Ian Thompson, professor of urology at The University of Texas Health Science Center, San Antonio. The killer quote: “We don’t want people to panic,” said Dr. Otis Brawley, chief medical officer of the cancer society. “But I’m admitting that American medicine has overpromised when it comes to screening. The advantages to screening have been exaggerated.”

I’ve had the opportunity to hear both of these researchers speak, and they are smart eggs – and I’m thrilled that their work is getting such coverage. Dr. Thompson’s work, for instance, is discussed in my forthcoming book (This is one of those stories that I’m thrilled to see, on the one hand, because it dovetails so neatly with what’s in the book – while also a little chagrined to see, as an idea in the book gets wide currency before the book is out!).

Screening tests are one of the great tools of public health, where we can detect disease before it makes itself known. But there’s a distinction between what I call “dumb screening” and “smart screening.” Dumb screening is the idea that, given the tools, medicine should root out cancer whereever it lurks in whatever form, no matter the cost (to the psyche or the pocketbook). Smart screening, on the other hand, is the growing notion that all cancers are not the same; that there are some that are lethal and some that are not, and what we need to do is deploy the right tools to spot the right cancers. It’s a more delicate task, and a more difficult judgment to make. But really, it’s the path of all science – moving away from simplicity and towards complexity.

In many situations, screening works. Some 30,000 children in the United States have been spared mental retardation because of PKU testing. A blood test used to screen for colon cancer—the second-deadliest form of cancer for men and women overall, even though, ironically, it’s among the easiest to screen for—has been shown to save as many as 10,000 lives in the United States annually. These are the sorts of results that make people evangelize about a new screening test, because it allows the possibility of changing the future, of plucking people off one course and setting them on another that promises a longer, healthier life.

And screening is only going to get more common for three reasons. First is the emphasis on preventive medicine, based on the recognition by the medical establishment and the US government that having an earlier warning saves lives and money. Second are an emerging class of risk-based conditions like metabolic syndrome and high cholesterol that bring with them a new checklist of routine tests. The third driver is technology itself: New proxies for proximity, such as
CT scans and PET scans, give us a look deep inside the human body. These are tempting tools for screening large numbers of people for diseases that are otherwise invisible. Genetic tests, which skip over the imaging of our bodies and go straight to the molecular level of our cells, are another driver for implementing more screening tests.

If these technologies are deployed systematically and wisely, they can be a great boon to our health, both collectively and individually. But the fact is that screening tests aren’t always used wisely. Though a screening test can be the first step in a well-considered Decision Tree, a screening test without forethought can propel us into a zone of ambiguous probabilities and poorly calibrated risks.

In the case of prostate cancer, in particular, there’s been a growing sense that screening has downsides that outweigh the benefits. One noteworthy approach is taken by a startup called Soar BioDynamics. Soar sells a decision-support tool for men who’re trying to make sense of their PSA test results. The idea is to discern what, exactly, besides cancer could produce a high PSA level, so men don’t move too quickly toward biopsy and removal, with all the latter’s  egative consequences. Using the information from a man’s PSA test along with that from a few other easy tests and data points, Soar’s tool calculates the most likely scenarios for what’s happening inside a man’s body, ranging from an enlarged prostate, to an infection, to a lethal cancer. The calculations are presented as probability scores for diagnoses.

“We can cut way down on the false positives and eliminate detection of the cancers that aren’t progressing. You want to catch the bad stuff, but ignore the stuff you don’t need to know about,” says company founder Tom Neville. “The issue isn’t just what decisions you make, but what order you make them in. We’re trying to switch the order of events. There’s all this stuff driving people toward biopsy and treatment. We’d like to eliminate the unnecessary biopsies and only go to the expensive experts when it’s highly warranted. We’re not trying to do away with screening. The PSA test can be a valuable test, there’s a lot of information in there. But it’s important to know what the test actually shows.”

Soar charges for its service—$80 for one year of reports. But there are other, free tools out there that take a similar approach, turning research around so an individual can interrogate it for its applicability to his specific circumstances, rather than having to navigate through stacks of research papers and findings for some wisp of relevance. At the University of Texas at San Antonio, Dr. Thompson has developed a prostate risk calculator that lets a man enter his PSA level along with his age, race, family history, and a couple of other metrics and churns out his risk of developing prostate cancer. Importantly, the calculator also calculates the risk of a high-grade cancer, accounting for the fact that not all prostate cancers are lethal. The value of such a tool, Thompson said at a recent symposium hosted by the Canary Foundation, is that it turns the PSA figure from one isolated data point into one of many inputs. “We need to build in characteristics about the person, their age, their race, their family history,” says Dr. Thompson. “It’s not just what one test tells us.”

For a play-by-play look at how scientists trying to distinguish between smart screening and dumb screening, see my story on the Canary Foundation from Wired.

I’m spending the weekend at the Aspen Health Forum in, uh, Aspen. It’s an interesting lineup- there’s dr. Oz and Deepak Chopra and Goldie Hawn doing the wellness thing, dan glickman and Anthony fauci and Tom daschle doing the policy thing, and Adam bosworth and Linda stone and David Agustin doing the tech/medicine thing. With reform looming, it makes sense to dwell on the prospect of where to take healthcare, and there are some intriguing panels on global health and swine flu. I’m loking forward to catching up on those topics.
It is interesting to me, though, how there’s a problem- centric orientation to the discussion, but for the touchy-feely stuff from Chopra et al. I guess I like my optimism to come from science and strategy rather than talk of happiness and yoga. (Esther dyson tweeter this thought much quicker than I’ve explained it here).

I’ve been thinking about this in terms of the Decision Tree, and I’ve come to call it ‘bottom-up healthcare reform’- engaging individuals in the same goals of overall reform, vis a vis prevention and behavior change and smart screening appropriate treatments. I wish this approach was more explicitly on the agenda.

Anyway, there are some nifty panels. So I’ll be searching out for the smart insights and will wrap them up here.