Great news – The Decision Tree is now available in a Kindle e-reader edition at Amazon.com!

Here’s the link: Decision Tree on Kindle

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Whew, what a week.

The Decision Tree debuted to some great acclaim and attention, and I think it’s useful to update some of the highlights here.

Freakonomics blog @ NYTimes.com: A q/a with the Freaknomics blog about decision making, when screening makes sense, and the utility of genetic testing.

Big Money: I Wanna CT Scan Your Hand: An excerpt that discusses how the high price of CT scans adds to healthcare costs.

TheAtlantic.com: The Wonder Drug Myth: Another excerpt, this one about the infrequently discussed miss-rate of drugs.

BoingBoing: An astute read of the book by Bill Guerstelle.

Gizmodo on Sleep Gadgets: A piece I wrote for them about how gadgets like sleep trackers can help us monitor and improve our health.

Also, I’m grateful to power tweets from Tim Ferriss, Dan Pink, Steve Case, Deepak Chopra, and countless others. Hopeful for another big week!

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There are patients – and then there are active patients. And some of the people I talked to for my book I’d call very active patients. They have struck out on their own and made radical decisions about their healthcare.

Teri Smieja is one of these heroes. When she learned that she had a genetic risk for breast and ovarian cancer, she embarked on a series of decisions – illustrated beautifully in this excerpt in Wired Magazine – that resulted in her getting two preemptive surgeries.

Todd Small is another. A 40-something Seattle father who happens to have multiple sclerosis, Todd first came to my attention when I reported a story about Patientslikeme.com for the New York Times Magazine. At the time, Todd was actively engaged in his medication – he learned from his fellow PatientsLikeMe community members that his dosage for a drug called baclofen was probably too low, and adjusted it accordingly (working with his doctor, I hasten to add).

When I checked back in with Todd recently while reporting the book, I was surprised to hear that he was about to embark on an even more radical decision: he had heard about an experimental stem cell treatment for MS, and was about to give it a whirl. Todd, who admits he’s no whiz at science, couldn’t make out what the research was saying. So he turned to the PatientsLikeMe community. “If somebody could decode this into simple layman’s terms, it would be much appreciated,” he posted in an online forum.

Soon enough, his fellow members did decode the research – and the consensus seemed to indicate that the procedure, though somewhat risky, was a reasonable bet. It was nothing to sniff at: the treatment begins by extracting 400 milliliters—a little more than a can of soda—of bone marrow from the leg. For several weeks, those cells are used to grow more cells, after which the patient returns and about 50 million cells are injected into the spinal column.  The cells seem to repair some of the damaged myelin in the central nervous system, lessening the symptoms of MS. About 60 patients had been treated with the procedure, and 55 had reported major improvements in their symptoms, including a former Canadian golf pro who was able to return to the game.

To Todd, the idea “was a no-brainer,” he told me later. “I just gotta go for it. If I don’t do this, I’ll be kicking myself. And in another year, I’ll have to quit my job at the shop. I have a family. I have two kids. I owe it to them to at least try this.”

Such self-guided research unnerves the medical establishment. That way, they warn, quickly leads to quack cures and dangerous treatments. That’s no doubt true in many arenas. But the power of PatientsLikeMe is that its members take their science seriously. They demand published research, not anecdotes. They’re quick to debunk phony cures and quackery. They consider themselves not just beneficiaries of research, but participants in an ongoing research project.

PatientsLikeMe co-founder Jamie Heywood calls this “personalized research.” On a smartly run, well-organized Web site, patients can play a huge role in informing each other; they can decentralize and distribute information that once was available only through a personal physician. This means that people can share not only their stories but also best practices and results. The crowd can create its own research, becoming what Jamie describes as “an insight engine.”

The same spirit propels CureTogether, a Web site that lets people with dozens of conditions, from allergies to vulvar vestibulitis, track their treatments and symptoms. Like PatientsLikeMe, CureTogether has an insatiable appetite for tracking patient data, and a faith in collaborative insight. “It’s driven by the patients, not by scientists.” Carmichael calls the insights that result from the nearly 7,000 members at CureTogether “collective wisdom.”

PatientsLikeMe and CureTogether can be seen as a direct challenge to physicians’ omniscience: The companies not only let members track their disease progression, they tacitly encourages them to try to turn those progression curves in a positive direction. This is what’s unnerving to many doctors. But it’s also what makes the self-guided approach so compelling. When it’s done with data, when it’s done as a feedback loop, it can actually result in good, more informed decisions.

Not all of these approaches are right for everyone. Constant self-tracking of the sort that PatientsLikeMe requires—updating one’s symptoms and dosages and progress—can be tedious, especially for somebody who already has a chronic illness. And not everyone is the “early adopter” type. But the truth is, you don’t have to be an early adopter to understand the virtues of mindfulness. At their best, Web sites like PatientsLikeMe and CureTogether offer a true middle path—one that has a grounding in science, yes, but also an understanding that we ordinary folk tend to look to each other, rather than textbooks or research papers, for advice on how to lead and improve our lives. The stories we share about our lives, especially stories about our health, can be incredibly powerful.

The key is to combine our affinity for stories and narrative with our capacity for rational decision making. It’s in the combination where there’s relevance, and where there’s relevance, there’s an occasion for action.

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Quick snip of my latest post on Huffington Post, which is generating a terrific response today.

Every day, we make dozens of decisions without thinking about them: what to feed the kids, how fast to drive to work, whether to hit the snooze bar. We make most of these decisions without a second thought. We go with our gut.

For other decisions, though, we have to pause, consider our options, and bring our best judgment to bear. This can be uneasy territory — and it can get especially fraught with decisions about our health, when we often lack a strategy for weighing all the information on the table. We’re not sure where to start.

But making smart decisions about our health doesn’t have to provoke anxiety. It turns out we’re well equipped to consider a range of options and make the right call. We just need to keep a few principles in mind.

Read the rest here.

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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.

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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!

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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.

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[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

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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.

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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.

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