Healthcare, etc.

May 11, 2012
Blind and toothless: The future of middle class in America?: Since it's Friday, we'll steer clear of academic subjects and just tell a story. This is a story of a good friend of mine who works in an editorial position for an interdisciplinary humanities journal. This journal is based at a prestigious private liberal arts institution, and thus she is on staff at that institution. This story is about her, but also about how well the Massachusetts health law is working for people like her. I will call her Jo.

Jo is in her early 50s, a single mom of two lovely teenage boys. Their father who is separated from Jo, although in the picture, does not provide any child support for various reasons. Thus she carries sole financial responsibility for her family.

Well, you say, she is on the journal's editorial staff, she is loaded, right? Nah, surely you are not so naïve as to think that this is a high-paid position at a humanities publication. Let's just say that she is not the 1%; why, she is not even in the top one-half. Would you believe me if I told you that a high-ranking editorial staffer gets a salary that is only 2x above the federal poverty level threshold? So much for those wealthy elite academic East Coast types! But she does get her healthcare insurance through her employer, and this is fortunate, right? Well, here is the rub.

Jo has a complex health condition that affects her entire body, including skin, teeth, eyes, heart, lungs and viscera. Despite this, she is optimistic, upbeat and one of the most patient parents I have ever met. But here is what gets her goat: it is the very fact that she gets her health insurance through her employer! How can this be?

Here is how. The cost of her basic restrictive HMO insurance through her employer is over $5,000 per year. And this flat rate is not affected by the salary level of the employee; in other words, the janitor and the university president get to pay the same amount of money for the same plan. In addition to this, her office visit copay is $20, and her medication co-pays are between $10 and $30 for a month's worth of medication. As you can imagine, for someone with a complicated chronic condition, these co-pays can add up quickly, and they do, to a shocking $2,500 per year. And this is before any allowance for the boys' medical needs or dental or eye care for any of them. Once you add everything up, Jo spends over 1/4 of her entire not-so-stellar income on healthcare. But if we do add dental and eye expenses into the mix (remember, her condition affects these organ systems as well), her healthcare expenditure comes to 40-50% of her annual income! And this is just for draconian restrictions of an HMO! AND, this does not cover all of the time that she spends on the phone finding providers that take her insurance and on schlepping miles away to see that single specialist that the HMO will pay for.

But wait, you say, you live in Massachusetts, the land of socialism, gay marriage and healthcare for all. Why can't she just dump this lousy and expensive employer-provided coverage and go to the Mass Connector, where everyone is equal and all get what they need for what they can pay? And furthermore, you say, isn't the PPACA, the new healthcare law of the land that is fashioned after Romneycare in MA, going to take the choice away from people and MAKE them get insurance through these exchanges rather than through their employers? Jo must be an idiot not to be taking advantage of this communist healthcare state! Hmmm, let's see now.

Jo has had a number of discussions with the staff at the Connector. And yes, you are right in that, if she switched to one of their plans, she would qualify for a plan very similar to her present one for about 1/5 of what she pays now. And the co-pays? Why those would shrink to $0 to $10. How's that for a deal? But here is the catch: According to people she has spoken with at the Connector, a person who has health insurance offered through her employer is not permitted by law to take advantage of the Connector deals if the employer plan offers coverage that meets the minimum standard in the law. And hers does. Ironic, isn't it?

So Jo goes on struggling with the financial burden of her crappy and expensive employer-provided insurance, only now she has to pick and choose: Can she afford to replace her failing dental bridge ($4,700) or should she just choose something so frivolous as feeding her children instead? This is a choice that is not a choice at all, is it? And if these are the choices that we can expect with the PPACA, well, then, we have the law that we deserve: one that will make sure that the investors in the "healthcare" marketplace continue to get handsome returns. But start getting used to people who cannot see their computer screens showing up to work without their teeth!

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May 4, 2012
Press coverage of health data: Just like Pharma's DTC?: I am warning you now: This is going to be a rant.

Yesterday's Wall Street Journal had a front page story on observational data, and how researchers are growing more concerned about its accuracy even as the volume of such research is growing exponentially. And then today, there was this from multiple news outlets:
The HealthDay story quotes the senior author of the study thusly [emphasis mine]:
"The results of our research allow us to definitively answer the question of whether jogging is good for your health," Peter Schnohr, chief cardiologist of the long-term Copenhagen City Heart Study, said in a news release from the European Society of Cardiology. "We can say with certainty that regular jogging increases longevity. The good news is that you don't actually need to do that much to reap the benefits."
And then at the very end it says this:
The study was slated for presentation Thursday at a meeting of the European Association for Cardiovascular Prevention and Rehabilitation, called EuroPRevent2012, in Dublin.
Data and conclusions presented at meetings should be considered preliminary until published in a peer-reviewed medical journal.
So, let recap. WSJ says that observational data are of concern because they can be tough to confirm, so we should be skeptical. HealthDay and others insist that this observational study shows definitively that jogging prolongs life, so what are we supposed to believe?

The problem and the disconnect, I believe are not with the studies themselves. The problem is with the way these stories are reported and the end result of that: Just like direct-to-consumer advertising from Pharma, these stories call us to immediate action, even when the results are preliminary. We rail against Pharma's DTC, but take this kind of press coverage as a given. This type of reporting, where half-baked data are presented as the final word, disappointingly enabled by the investigators themselves (who doesn't want 15 minutes of fame?), makes observational data look like something they are not. On the one hand, we are told that here is the result. On the other, after some contemplation and peer review, we realize that the study did not show what it was said to have showed. Bingo, the sweeping conclusion is that all observational studies are bad and biased, so let's just throw out the baby with the bath water.

The press are doing a huge disservice to the public and to science itself by presenting everything in such black-and-white terms. We know that it is the initial message that grabs attention; hence "jogging adds years to your life." To make the next message stick, something powerful needs to be cooked up; hence, "Analytical Trend Troubles Scientists." Saying that "well, we overstated what the jogging study showed" isn't nearly as sexy. How about we back up a bit and say it like it is: the devil is in the details, and those details don't make nifty headlines.

I am grateful to Gary Schwitzer for slapping this kind of sloppy reporting, but he cannot eliminate it alone. We all must speak out against it. We abhor Pharma's DTC marketing practices. Why do we give the press a free pass for the same behavior? Cover accurately or don't cover at all!

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April 26, 2012
Fast science: No time for uncertainty: Reading Barbara Ehrenreich's "Bright-Sided" has been liberating in that is has given me permission to let my pessimistic nature out of the closet. Well, it's not exactly that I am pessimistic, but certainly I am not given over to brightness and cheer all the time. My poison is worry. Yes, I am a worrier, in case you had not noticed. So, imagine how satisfying it is for me to find new things to worry about. As if climate change were not enough, lately I started to worry about science.

No, my anxiety about how we do clinical science overall is not new; this blog is overrun with it. However, the new branch of that anxiety relates to something I have termed "fast science." Like fast food it fills us up, but the calories are at best empty and at worst detrimental. What I mean is that science is a process more than it is a result, and this process cannot and should not be microwaved. Don't believe me? Let me give you a couple of instances where slow science may be the answer to our woes.

1. Lies and damned lies
Remember this story in the Atlantic that rattled us with its incendiary message? Researcher John Ioannidis has been making headlines with his assertion that most, if not all, of what we know in medicine is in doubt, given how we do and publish research. And how we do and publish research has everything to do with the speed of "progress." Academic careers are made with positive results, to sell news the media demand positive results, and to respond to this demand academic journals prefer only to publish positive results (this last phenomenon is referred to as "publication bias," and is something Ben Goldacre rails against at length). A further manifestation of this fast science is that "no replicators need apply." I am, of course, referring to an extension of the publications bias, whereby journals are not interested in publishing even a positive study that replicates a previous finding -- this is simply not sexy. Thus, results have to be quick and positive to grab a share of our attention and sell academic prestige, journals and news.

2. Science output to drive business profits
In his book Supercapitalism, Robert Reich describes the growing demand by investors over the last several decades to squeeze ever-growing profits. It is clear that this chase after short-term profits has resulted in job loss in the US through outsourcing, the widening of the economic gap, and even the crash of the world economy following the collapse of the mortgage-backed securities house of cards. Much of the profit can be counted on to come through scientific innovations which may or may not improve our quality of life.
In medicine, where scientific progress is applied to our fragile being, being reasonably sure of our findings seems pretty important. Yet speed is once again the order of the day. I will grant you that speed is of importance in such diseases as advanced cancer, for example, where we may and should accept a level of uncertainty that we would ordinarily run away from in other circumstances. But doesn't it make sense to be much more cautious before broadly accepting an intervention that happens before one gets sick, one that is meant to diagnose either early disease or a precursor to one? Should we not demand slower science before we allow anyone to medicalize such normal events in life as menopause and aging? Should this caution also not apply to screening for diseases that may or may not impact us in the long term, yet the chase could hurt us substantially in the immediate future?
But this is not the way to stimulate the economy or to make a profit. The half-life of a medical device, for example, is less than 1 year. After that a new "improved" version of the device is expected, whether it does or does not improve outcomes. For decades we were told to get screening mammography after the age of 40, only to find out now that the risks of this may well outweigh its benefits for many. The American Lung Association has just endorsed CT screening for lung cancer among current or former heavy smokers, yet the jury on its risk-benefit-uncertainty equation should still be in the thick of deliberations.

3. Science denialism
We hear a lot about how people are turning away from science. The state of Tennessee is about to descend back into the dark ages when superstitions instead of scientific theories dominated the classroom. A strong and largely anti-scientific lobby wants to bury any mention of human-driven climate change; fortunately, it looks like they are not succeeding. The anti-vaccination groups are getting more instead of less vocal following repeated debunking of any link between vaccination and autism. Science denialism is so rampant that there was even a need for a conference on how to address it. What gives?
While blaming everything on fast science alone may be reductionist, fast science in the setting of our growing societal innumeracy is a recipe for disaster, as we are seeing unfold. Our schools have failed spectacularly in their duty to educate kids about the process of science, while at the same time arming them with the "single-right-answer-to-every-question" attitude toward knowledge. This pernicious combination, along with the publication and reporting of sexy science at the expense of the more thorough analytic and introspective approach, seals the impression that the roller coaster of scientific knowledge represents not the very essence of how science should be done, but that science (and scientists) has failed.
Is slow science the answer to this fiasco? Only in part, I am afraid. Without altering fundamentally how we teach science at all levels, it would not be the cure, even if it were possible to execute. No, I am afraid that without teaching what science is, it is not even possible to get it to slow down.

Let me reiterate: the pace of scientific discovery is slow. This does not mean that we need to hide every step of it from view until we get the results that we deem worthy of sharing. On the contrary, I agree with those who think that sharing at the more interim steps can only improve what we do. Yet the innumeracy, fame and fortune are forces that put such free sharing in peril by misrepresenting it as the final answer to everything. And when the answer is changed, which is not only expected, but indeed desired in scientific pursuits, the public opinion punishes science.

Let me end with a quote I read on one of my favorite web sites, Brain Pickings, in a review of the book boldly entitled Ignorance: How It Drives Science:
Are we too enthralled with the answers these days? Are we afraid of questions, especially those that linger too long? We seem to have come to a phase in civilization marked by a voracious appetite for knowledge, in which the growth of information is exponential and, perhaps more important, its availability easier and faster than ever.
[...]There are a lot of facts to be known in order to be a professional anything — lawyer, doctor, engineer, accountant, teacher. But with science there is one important difference. The facts serve mainly to access the ignorance… Scientists don’t concentrate on what they know, which is considerable but minuscule, but rather on what they don’t know…. Science traffics in ignorance, cultivates it, and is driven by it. Mucking about in the unknown is an adventure; doing it for a living is something most scientists consider a privilege.
So, let's celebrate uncertainty. Let's take time to question, answer and question again. Slow down, take a deep breath, cook a slow meal and think.

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April 24, 2012
How to avoid the "Titanic effect" in Pharma: Today I was going to tell you the tale of my son's broken wrist (he is fine now, this happened in January, but the insurance issues are fascinating), but I got distracted thinking about another fascinating subject that many do not understand well: confounding by indication. I especially started thinking about it in the context of how decisions and policies are made, and how not having the right data at the right time leads to this "Titanic effect" for a technology. What do I mean by this? Well, let me explain.

Some say the Titanic sank simply because of poor preparation -- not enough life boats, not enough training on the evacuation procedure, in other words "not enough imagination" to plan for a catastrophe. It was derailed in its course by an entirely predictable natural calamity that had not been planned for adequately, even though the risk was obvious in retrospect. Was this just on of those "unintended consequences" that could have been avoided with more clear vision? Perhaps, but the Titanic is, ahem, water under the bridge. But we can focus on some more mundane and current potential missteps and make some guesses.

Let's talk about medical technologies, and drugs in particular. Let us say that there is a new sepsis drug that has been tested among patients with sepsis but without organ failure. This drug appears to prevent organ failure in a fraction of the treated patients, and also reduces mortality by 6%. The only obstacle to widespread use of this drug is its acquisition cost, which is much higher than what the hospital's critical care pharmacist is used to paying for other drugs. Because of this high cost, the drug, despite being on the formulary, gets administered only to those patients who have developed not one, but two organ failures. The savvy pharmacist looks at the outcomes of these patients and, after comparing them to those of the patients who did not receive the drug, concludes that the new sepsis drug, instead of saving lives, actually kills. The P&T committee discusses this, dumps the drug from the formulary and other hospitals follow suit. What's wrong with this picture?

Several fallacies are at work here, including an overly broad inference of causality and bias. But the most important lesson is to do with confounding: because of its apparent expense, the drug has been niched into a population of patients who a). were not the ones that exhibited the evidence of benefit in the trials, and b). have a very high risk of mortality at baseline. So, not only is it not valid to conclude that the drug killed these patients, but it is not even valid to say that the drug does not work -- it may well work in the populations that it was shown to work in, but not in this, much more ill, population. You see the difference? It is like saying that you umbrella failed to keep you dry when you opened it only after you already got soaked.

So confounding by indication is one reason that drugs "fail" -- they are given to people who are by definition not going to do well, and the confirmation bias pushes us to say see, it's expensive and doesn't work. So how do we overcome this phenomenon and make sure that appropriate patients get access to useful technologies? I believe I have a very simple answer: don't squeeze the toothpaste out of the tube if you don't want to have to cram it back in. Huh?

In other words, do what I always advocate: be ready with the relevant data before the train leaves the station, before the cat gets out of the bag, before the horse gets out of the barn. It is very well known that cognitive biases, once established, are difficult to overcome. The pharmacist's first concern is for being able to use his very limited resources efficiently, and to guard from spending his monthly budget on a potentially useless intervention in a single patient only to be left with no resources to care for all of the other patients. Yet many manufacturers at launch send their reps to the pharmacist with two virtually unrelated stories: one about efficacy and the other about the acquisition price and its impact on his budget. When the drug is expensive, the efficacy pales in comparison to the price tag, and the pharmacist has no choice but to restrict the use of the drug, thereby consigning it to failure by confounding by indication. Sound familiar?

Is there a way to avoid this scenario? I think so. It is self-evident that you have to have good data. The surprising thing is that good data are necessary, but not sufficient: the timing of these data is critical as well. It is easier to help people form an opinion where none exists than to change one that is already there. So, to be successful, the manufacturer with a good technology must have a coherent effectiveness and cost-effectiveness proposition right out of the gate. Not only that, but it is imperative to help the clinician understand what patients might benefit from the technology (no, not all patients should be on your drug). This is the kind of a collaboration that will ultimately benefit all stake holders: 1). Appropriate patients will get the opportunity at better outcomes, 2). The pharmacist will understand up front the value proposition and the potential scope of use, and 3). The manufacturer will profit from providing a beneficial service. Isn't this the intent of all this drug development?

If all this seems all too obvious, it is because this is not rocket science. But why, then, do I see so many companies get into trouble with this very scenario? Is it just the case of "best laid plans" or is it a real blind spot that needs to be illuminated? You tell me. Given the investment that goes into drug development, I think it makes sense to approach this gap earnestly, instead of just shuffling the deck chairs on the Titanic.

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April 24, 2012
Do complex problems always require complex solutions?: A mind-blowing talk by Gerd Gigerenzer on making decisions under conditions of uncertainty -- this is for you, heuristics mavens! (Note an interesting nuance about medical decisions at about 16:15).

A big h/t to @Medskep

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April 20, 2012
To solve complex problems, tinker!: A fabulous TED talk by the economist and writer Tim Harford (@TimHarford) about the virtue of making good mistakes (aka tinkering, which I have written about in the past). This is why science, at least in theory, works so well -- trial and error move our knowledge forward. Also a great reminder of why our educational system is failing.

A big h/t to Kent Anderson of The Scholarly Kitchen for posting this on their web site.

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April 17, 2012
Can probability solve the healthcare crisis?: Here is the video of my Ignite Boston 9 talk from March 29.

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April 14, 2012
How I fell in love at TEDMED: Over the exhilarating four days this past week, we all fell in love a little bit -- with the city, the Center, the meeting, the ideas, and one another. The city was Washington, DC, a touch past its cherry-blossom blush; the meeting was, of course, TEDMED. The ideas were of about honoring our health, environment, food, and about making health and healthcare efficient and kind for all.

I fell in love with dreamers. Though their dreams were varied, their paths to fulfilling them all converged into the same stream. Like a trip down the Amazon that the biggest dreamer of all, Jay Walker, the curator and the force behind the meeting used as a metaphor for TEDMED 2012, they accepted their tortuous and demanding journeys and, much to our delight and benefit, made a stop at the Kennedy Center. And although I will only mention a few, many others will stay with and inspire me for the months to come until TEDMED 2013.

I fell in love with Bryan Stevenson, who spoke about his grandmother and identity and justice.

I fell in love with Rebecca Onie, who, while transforming the care of the urban poor is also transforming the face of student activism.

I fell in love with Traces, a Montreal performance group who made my heart stop with their daring acts of precision. Our healthcare system can learn a lot from these young people.

I fell in love with Jacob Scott and Sandeep Kishore, both of them young, energetic and passionately committed to changing the face of medical education.

I fell in love with Ed Gavagan, who told the story of his confrontation with death with courage, humor and honesty.

And yes, I fell in love with and was made to weep by Robert Gupta's transcendent violin and Stephen Petronio's defiant vulnerability.

TEDMED 2012 was a feast, and now I am back to the journey of my real life: calls to make, e-mails to return, analyses to do, papers to write, talks to give, a book to get to market. It all seems just a little drab compared to the four days I spent in this intellectual and emotional climax. But like a great yoga session, TEDMED was restorative, rejuvenating, and remarkably inspirational. The mix of hard core science, the arts, history and frank curiosity sparked personal ideas and renewed personal commitments to executing my dreams for a better society. Spurred by Sekou Andrews' and Steve Connell's raw poetry performance, like a youngster in love for the first time, I am ready to GO! So I am off to do what E.O. Wilson suggested a scientist needs to do: think like a poet and work like a bookkeeper.

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April 9, 2012
Five ways to tame the risk-benefit-uncertainty troika: There was a story on NPR this morning that sent me in a radical direction. It discussed the increase in use of brachytherapy for localized breast cancer. The idea is that this is a concentrated dose delivered much more locally and rapidly (over 5 days) than the conventional external beam radiation (over 6 weeks). The issue is that it has not been tested rigorously in a randomized controlled trial yet, and some oncologists are concerned about its outcomes as they compare to the conventional approach. One of the concerns stems from an increase in the rates of subsequent mastectomies, which are double in brachytherapy relative to conventional. At the same time, there are clear benefits, not the least of which is the period of exposure and the hassle associated with daily trips for radiation for 6 weeks.

Several points popped up in my head in response to the story:
1. When discussing this predominantly women's disease, the expert voices mostly heard from were male (5 of the 6 doctors quoted). Does this matter? Not sure.
2. The priorities addressed by the experts were the traditional outcomes -- survival, recurrence, metastasis. The priorities described by the patient were about her time and convenience today.
3. The concern about the procedure stems from the observed doubling of the need for mastectomy within 5 years among patients treated with brachytherapy, an event "rare no matter what what kind of radiation women got."

So what's my point? Do I think that more rigorous testing is not indicated? Not at all; we need a more rigorous evaluation of the technique. No, what I am wondering is at what point should a procedure like this (or any intervention, for that matter) become available to patients as an option to be considered? Should its availability be determined in a dark room by bespectacled men around a conference table, or should it be put on the menu of choices, along with its risks, benefits and uncertainties, as soon as it looks safe enough, whatever that looks like?

The larger question this raises is what is the degree of uncertainty that we are willing to accept around interventions that become available, be it a drug or a procedure or a device? How do we incorporate patients' priorities for outcomes that are important to them into these decisions? Remember ACT-UP and how they moved the FDA to make more rapid decisions about treatments for HIV/AIDS? Have we swung too far in the opposite direction today, whereby we want a virtual guarantee of safety before a technology is approved?

I offer these 5 potential question to help with making these decisions:
1. How severe/deadly is the disease in question?
2. What is (are) the known potential benefit(s) of the intervention?
3. What is (are) the known potential risk(s) of the intervention?
4. What uncertainties bracket this risk-benefit equation?
5. How does the patient feel about the extent of this risk-benefit-uncertainty balance in the context of her condition?

I think that the first four are the questions that the FDA struggles with every day. They are the gatekeepers for the availability of new technologies, and, therefore, for the relevance of the fifth question. What I am wondering is whether it is not better to start bringing a lot more of the public perspective to the discussion much earlier, so that the patient can have the option of evaluating more choices sooner. I know I may be treading on thin ice here, but I am ignoring any market forces or special interests for the moment. The question I am asking is "In the best of all possible worlds, where no one is trying to sell you anything, when is the best time to give the patient an opportunity to accept or reject an intervention, given the risk-benefit-uncertainty profile?"

Bottom line: There are no guarantees. Just because something is available on the market does not mean that it is completely safe or completely effective. Most importantly, it does not mean that we come even close to being certain about these attributes. As a corollary, just because there are uncertainties about the risks and benefits of an intervention, does it mean that it should not be available as an option for a patient? My guess is that there is a balance of this troika of properties that may be optimal on average, but I am also guessing that that this average balance will miss a substantial volume of outliers. Just as some people thrive on the thrill of bungee jumping while others clamp down just at the mere thought of it, so some patients may surprise us with their position on this risk-benefit-uncertainty continuum.

I apologize if my argument is not clear -- I am definitely thinking about this stuff actively. The one thing I am absolutely sure of is this: Unless the public and clinicians are educated about how to have these conversations, we will always have to rely on and, consequently, blame someone else for making decisions for us.

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April 6, 2012
Infographics: devil in details: I got an e-mail yesterday asking me if I would be interested in displaying an info graphic on my blog. After a few back and forths, I decided to do it. After all, a picture is worth a thousand words. But as with everything, buyer beware: the devil is in the details.

A couple of caveats:
1. I cannot back up all of the numbers myself, and the references at the bottom seem to represent single source data, rather than the totality of the evidence. So take with a grain of salt.
2. There are a couple of notable errors in the graphics:
a. The graphic on other first world nations' spending says that the US spends 2x what they do in Japan. What the graph shows is that this is the case as the proportion of the GDP (also the x-axis is not labeled as such). So, the statement is not entirely accurate.
b. In hospitals overcharging, the caption states that hospitals charge 200% more for meds compared to ex US. The number adds up to 100% more, which is 2x.
3. In general, there are actually some valid reasons why we pay more for stuff in the US. I do not, for example, know if the international data are adjusted for various economic factors, such as purchasing power.
4. The statement that doctors are overpaid is a laugh -- take it from someone who has been in the trenches. I worked dawn till dusk and beyond, nearly every day of every week. I was making barely enough to keep food on the table and a roof over our heads. No lavish vacations, no BMWs, etc. It is not the MDs that are overpaid. Go to the C-suites and corporations, and then you are getting warmer.

Despite the disagreements that I have with the data, I thought it might be a good point of discussion. Would love to hear your thoughts about it.

Created by: Medical Billing and Coding Certification

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April 5, 2012
Happy to be in the "top three": So, I must be in the "top three" then :)

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April 4, 2012
How to make safer decisions in medicine: I love when an article I read first thing in the morning gets me to think about itself all through my morning chores and then erupts into a blog post. So it was with this little gem in the statistical publications "Significance." The author suggests making gambling safer by placing realistic odds estimates right on the poker machines in casinos. He even goes through the generation of the odds of winning and losing and how much based on really transparent assumptions. In fact, what he has in effect constructed is a cost-benefit model for the decision to engage in the game of poker on these machines. Seems pretty simple, right? Just a few assumptions about how long the person will play, some objective inputs about the probabilities, and PRESTO, you have a transparent and realistic model of what is probable.

In medicine, there is a discipline known as Medical Decision Making, and what it does is exactly what you see in the "Significance" article: its practitioners construct risk- (and, hence, cost-) benefit models for decisions that we make in medicine. To be sure,these turn out to be rather more complex, since the inputs for them have to come from a large and complete sampling of the clinical literature addressing the risks and the benefits. But that's the meat; the skeleton upon which this meat hangs is a simple decision tree with "if this then that" arguments. In this way these models synthesize everything that we know about a specific course of action and put it together into a number driven by probability.

They usually go something like this. We have a group of women between 40 and 49 years of age with no apparent risk factors for breast cancer. What is the risk-benefit balance for mammography screening in this specific age layer? One way to approach this is to take a hypothetical cohort of 1,000 women who fit this description and put it through a decision tree. The first decision node here is whether to perform a screening or not. What follows are limbs stretching out toward particular outcomes. Obviously, some of these outcomes will be desirable (e.g., saving lives), while some will be undesirable, ranging from worry about false positive results to unnecessary surgery, chemotherapy, radiation, and even death. Because these outcomes are so heterogeneous, we try to convert everything to monetary costs per quality of life (quality because there are outcomes worse than death, as it turns out). But what underlies all of these models is the mathematics derived from clinical studies, not pulled out of thin air. This is the most useful synthesis of the best evidence available.

To be sure, MDM models are rather more complicated than the poker example. They require a little more undivided attention to follow and understand. Furthermore, I personally did not get a whole lot of exposure to them in my training, but perhaps that has changed. Like anything to do with probability, these models tend to be off-putting in a society that has consigned itself to wide-spread innumeracy. And doctors are certainly not immune from misunderstanding probability. Yet without them perceptions rule, and our healthcare becomes a reckless gamble. In our ignorance we collude to build profits that come with medicalizing small deviations from the perceived normality. Sadly, the primary interests that drive these profits are not usually doing so with probabilistic forethought either, but rather on the basis of red hot conviction that they are right.

Doctors and e-patients need to lead a radical transformation in how we handle decisions in healthcare. It is very clear that willful ignorance has not served us well, and we are all too easily led into panic about every pimple. Resilience can only come when we question our assumptions. Alas, our intuitive brain is almost certain to mislead us when faced with complex information; why else would we need explicit odds listed on poker machines? The absurd complexity of information in medicine deserves no less. It's time to start the probability revolution!

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March 30, 2012
My solution to the healthcare crisis: Here is my talk from Ignite Boston last night -- I solved the healthcare crisis!

Zilberberg igniteboston2012
View more PowerPoint from murzee

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March 28, 2012
Why the PPACA hearing is the very definition of insanity: I haven't said much about the SCOTUS hearing of the PPACA, but it is time to break my silence. I have been listening to some of the details of the arguments, and I cannot help but be nauseated. It feels to me like the Justices are behaving like my teen: they take literalness to its absurd limit. Health insurance and cell phones, really? This betrays a complete disregard for probability. What are the chances that you will within your lifetime in the US need to dial 911 in the absence of a landline or another human with a cell phone within shouting distance? And what are the odds that you will at some point in your life require a medical consultation? I rest my case.

The more I think about it, the more convinced I am that the bill should have introduced single payer a priori -- funding our access to medicine through a tax. Yes, a tax. Perhaps the government is not the most efficient agent of this, and the overhaul could have been accomplished through some public-private hybrid model. In the end, as I have said here, our priorities are misaligned, as are our perceptions of what is important in this debate. We spend 97% of all the healthcare money on medicine, and we spend well over 97% of our national discussion about health on access to healthcare and medical interventions, which can only make a 10% difference in our health. The real money, so-to-speak, is in public health, which contributes 60% to our true health and gets only 3% of the expenditures and practically no conversational energy.

So, once again I find myself turning to the wisdom of Albert Einstein, who defined insanity as "doing the same thing over and over again, and expecting different results." The SCOTUS circus is the poster child for this insanity. Whatever the outcome, and I am not at all optimistic about the individual mandate, my sense is that nothing will change until we start paying attention to the root causes of our collective illness.

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March 27, 2012
Coronary CT angiography: More and less: I am still scratching my head over this study that was just published in the NEJM on coronary CT angiography among patients admitted with a suspected coronary syndrome. There are many potentially confusing points about it and how it was reported. The three things that I found most confusing were:
1. The formulation of the null hypothesis
2. The definition of the outcomes
3. The flow of patients through the diagnostic algorithm.
Let's see if we can clear some of that confusion.

The intent of the study was to see how adding the CCTA to the usual diagnostic testing in the ED impacted cardiac mortality or a myocardial infarction within 30 days. The null hypothesis was posed in an interesting way:
The study was powered to test the null hypothesis that the rate of major cardiac events among patients who did not have clinically significant coronary artery disease as assessed by CCTA would exceed 1%.
This is confusing, and I had to read and reread several times to understand what it meant. I finally realized that what they were saying was that in order to disprove that CCTA is not a useful test (this is the alternative hypothesis) they would have to show that the rates of the primary outcome were not above 1%. Make sense?

The next issue I had trouble with, as I always do in cardiology studies is their choice of endpoints. The primary endpoint was 30-day cardiac death OR MI. This means that anyone who either died of a cardiac cause or had a heart attack within this time period was counted as an event, which would argue against CCTA usefulness if they reached the critical mass of over 1%. Mind you this was limited to those patients whose CCTA did not reveal significant disease. There were some secondary outcomes examined as well, all at the 30-day time point: death, MI, revascularization procedure, and resource utilization. Note these outcomes were not combined, but were examined singly, and the pool of patients for these were all those randomized.

As an aside, cardiology studies frequently use combined outcomes, such as death and MI due to sample size considerations. That is both cardiac death and MI should be rare events in the group examined. When these events are rare, in order to get at their statistical significance exceedingly large sample sizes are needed. For this reason, these trials frequently combine several events, so as to enrich the frequency and commensurately drop the needed sample size.

But here is where it gets a little confusing. Looking at the "Safety" section of the Results, the authors state that no one who received a CCTA had a cardiac death or an MI within the 30-day period. However, 1% of the patients randomized into the CCTA arm did have a MI within 30 days. How can this be? Well, we need to keep in mind that not all those randomized to CCTA (n=908) actually got a CCTA (n=767), and that the majority of those who did have one did NOT have significant coronary disease (n=640). Thus, both the numerator and the denominator for the primary and the secondary outcomes are different.

Then there is this quick sentence in the "Efficiency and Use of Resources" section:
Coronary disease was more likely to be diagnosed in patients in the CCTA group than in patients in the traditional-care group (9.0% vs. 3.5%; difference, 5.6 percentage points; 95% CI, 0 to 11.2).
It is almost an afterthought, but it is important. It is puzzling that the outcomes in the two groups are completely identical, and this is not limited to those in the primary endpoint pool, namely people without significant disease. The question arises about what this means. Since there is no difference in cardiac death or MI, this increase in the diagnostic rate may imply overdiagnosis in the group receiving CCTA. There is a slight increase in the revascularization rates in the CCTA group (3%) over the standard care group (1%). This endpoint is much more subjective that most people realize, as a lot of judgment by the cardiologist and the surgeon goes into the decision. So this endpoint does not rule out overdiagnosis as a possibility. On the other hand, the study was not powered to detect a difference in the secondary outcomes, so it may be that the diagnoses are valid, but we do not have enough events to judge.

One final point of frustration with the study reporting was hitting dead ends in the flow of patients through the respective algorithms. I was, of course, looking for the rates of false positive CCTA tests and their outcomes. Unfortunately, I kept getting stuck at the catheterization and stress test steps, not knowing who exactly went on to have this testing. Since in the CCTA out of the 767 tests 47 were indeterminate and 80 were indicative of moderate-to-severe coronary disease, the 37 catheterizations performed were likely among these patients, though we are not told for sure. Of these catheterizations, 9 were negative for a significant stenosis, but again we are not told how this reflects back to the CCTA results. In fairness it is worth noting that fewer people in the CCTA arm (18%) underwent a follow-up test, compared to the standard care arm (62%). But again, this is just one test replacing another, or even being added on top of others. In addition, more of the former group were discharged from the ED (50%) than among the latter (23%) without being admitted.

So what does it all say to me? Well, CCTA seems to aid in the diagnosis of coronary disease among certain people presenting to the ED with chest pain. Given such a low pre-test probability of significant coronary disease (17%, derived by dividing the negative CCTA [640] by the total CCTA tests [767] and subtracting that from 1), I have to wonder about the performance of the test that is quite sensitive but not that specific (high risk of a false positive in a low-risk population). And even though fewer patients needed to be admitted from the ED in the CCTA group, I wonder if this does not have more to do with the differential availability of the testing rather than with its superiority.

Given the context that I laid out above for the results presented, I would not be rushing to adopt CCTA for all patients who present to the ED with a certain type of chest pain. And after all, though a "black swan" event, catastrophes do occur in follow-up catheterization even when the patient is disease-free. This is a good reason to be careful and circumspect in our quest for primum non nocere.

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March 23, 2012
How our healthcare spending is like that drunk joke: You know that joke about the drunk crawling around under a street light? A cop comes up to him and asks what he is doing. The drunk explains that he is looking for his wallet. The cop, getting ready to help the man, asks where exactly he dropped it. The drunk points to a distant corner of the dark side of the street. The cop, baffled, inquires why the man is looking here. With inimitable logic the drunk responds, "This is where the light is."

What does this story have to do with anything? Well, I went to a great HIT tweet-up in Cambridge yesterday, organized by Scratch Marketing and led by Janice McCallum. No, they did not at all remind me of the drunk in the joke. But the lively discussion about data by about two dozen attendees inspired by Janice's thoughtful presentation certainly made me realize that our healthcare policy is like that drunk. Here is what I mean.

Our healthcare expenditures are completely devoid of any attempt at probabilistic thinking. I thought about the old Rand Corporation data, which I have presented here before. Juxtaposing them with the data on our National Healthcare Expenditures really drives home my message that we need to get a whole lot better at applying probabilities to our decisions. And this specifically applies to policy.

Just look at the glaring imbalance: while fully 60% of all premature deaths are due to behavioral, social and environmental factors which reside in the realm of public health, 97% of all NHE is spent on the medical side. If I add the 2% of the total NHE spent on research into the public health piece of the pie (this is exceedingly generous, as public health research gets a bafflingly tiny portion of the total US research budget), we still have 95% spent on personal health and its administration and only 5% on public health.

So, if the probability of premature death due to a public health-related condition is 60%, why are we only spending 3% of all the healthcare dollars on fixing it? Another way of posing this question is, if the probability of premature death from issues related to access to adequate medical care is 10%, why are we spending 97% of all the NHE on that piece of the pie?

If this isn't just like that joke, I don't know what is. Only in this case it is much less funny than in the case of the drunk.

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March 22, 2012
More on "mammography saves lives" story: A story in HealthDay with the title of "Two Studies Find Routine Mammography Saves Lives" talks about studies presented at the annual meeting of the ~~American Society for Clinical Oncology (ASCO)~~ European Breast Cancer Conference (EBCC). The studies, both from the Netherlands, allegedly showed that mammography does indeed save lives. If true, these data would contrast with the preponderance of evidence that has stirred up such a ruckus recently about the utility of mammography, complete with references to rationing and death panels. But let's look at what's reported in today's story more closely.

Cutting through all the definitive bravado, here is a little piece of science that was reported:
Compared with the pre-screening period 1986 to 1988, deaths from breast cancer among women aged 55-79 fell by 31 percent in 2009," Jacques Fracheboud, a senior researcher at the Erasmus University Medical Center in Rotterdam, said in a meeting news release. We found there was a significant change in the annual increase in breast cancer deaths: before the screening program began, deaths were increasing by 0.3 percent a year, but afterwards there was an annual decrease of 1.7 percent," he added. "This change also coincided with a significant decrease in the rates of breast cancers that were at an advanced stage when first detected.
Note, the reference is to deaths from breast cancer without any mention of all-cause mortality. (You can read about why the latter is important here.)

The report next states that over the first 20 years of the screening program
... 13.2 million breast cancer screening examinations were performed among 2.9 million women (an average of 4.6 examinations per woman), resulting in nearly 180,000 referral recommendations, nearly 96,000 biopsies and more than 66,000 breast cancer diagnoses.
So, doing the math, I come up with about 31% false positive rate at the biopsy stage (that's 96,000 biopsies minus 66,000 positives for cancer, all divided by the 96,000 total biopsies). If we use the 180,000 "referral recommendations" as our denominator of all positive tests, and stick with the 66,000 true positive rate, then the false positives grow to (180,000-66,000)/180,000 = 0.63, or 63%. If we spread the 33,000 false positives over the 13.2 million examinations, that equates to 0.25% chance for a false positive. Yet the report goes on to say that (emphasis mine):
For a woman who was 50 in 1990 and had 10 screenings over 20 years, the cumulative risk of a false-positive result (something being detected that turned out not to be breast cancer) was 6 percent.
Six percent? This is clearly a place where my high school math teacher's mantra of "show your work" is applicable.

The next piece of information that I would like to understand better is this:
Over-diagnosis (detection of breast tumors that would never have progressed to be a problem) occurred in 2.8 percent of all breast cancers diagnosed in the total female population and 8.9 percent of screening-detected breast cancers."
How exactly was this computed? Again a case for "show-your-work."

And then there is this (emphasis mine):
Regular screening "decreases deaths by over 30 percent, [with] limited harm and reasonable costs. Additionally, cancers are detected at an earlier stage, which means not only decreased mortality but also morbidity; the patient may not have to have chemotherapy or a mastectomy," she noted.
OK, so, if I got it right, it is breast cancer mortality that is decreased by 31%, not all-cause mortality. This really should have been spelled out more clearly, not to mention that the actual, or absolute, reduction likely pales in comparison to this relative drop. And what about diagnosing earlier stage disease? Lead time bias, anyone?

The second study was a computer model, and I will not go through it at this time as I need to move on to other work. But you get the picture: the numbers given in the report are limited and at times they don't add up. Mixing up cancer mortality with all-cause mortality leads to erroneous conclusions. And finally, forgoing reporting on the absolute risk reduction in favor of the inflated relative reduction is not helpful for understanding the true risks involved.

One final thought: Yes, I do have cognitive biases, and it is difficult for me to avoid them. I happen to fall into the camp that thinks screening for sublclinical diseases, at least in our current technological setting, is disease mongering. At the same time, I would like to think that if the data really showed a significant benefit without great risks, I would give them a second look.

The bottom line is this: at least for me, the report confused the issue more than it has clarified. Perhaps the study, once published, will answer all of the questions that I have posed adequately. But at this stage, it is a shame that such strong statements as...
"These results show why mammography is such an effective screening tool," said one U.S. expert, Dr. Kristin Byrne, chief of breast imaging at Lenox Hill Hospital in New York City. She was not involved in the new research.
and this...
"We are convinced that the benefits of the screening program outweigh all the negative effects," Fracheboud said.
... are not backed up by appropriate evidence.

h/t to @ElaineSchattner for the story

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March 21, 2012
Aspirin or bias?: Update 4PM Eastern, 3/21/12

Wanted to append a couple of thoughts I tweeted earlier about these studies, in case you don't follow me on Twitter or just missed them:

Around 2:30 PM Eastern:
And then closer to 3:00 PM Eastern:

Thoughts?

There is a fascinating review by Cancer Research UK of the new and old aspirin data with respect to its effects on cancer and cardiovascular complications in the context of a heightened risk for bleeding. The review is full of fabulous information about what we know and the uncertainties that remain, all with practical suggestions at the end, so go there and read it.

But here is what I wanted to highlight in the graph that I am reproducing from a screen shot:
There is something very interesting going on here. Just as the risk of bleeding begins to drop, so does the risk of developing cancer. This could be a complete coincidence, but perhaps not. An alternative explanation is that people who already have cancer, though it may not yet be diagnosed, may be at a higher risk for a bleeding complication. Those who develop a bleeding complication presumably are taken off aspirin. But remember, they may already be harboring a cancer that will rear its head in the near future. But what about those who do not bleed and therefore are able to tolerate aspirin for a longer time? They also seem to have a drop in their risk of incident cancer. But of course this may have nothing to do with aspirin's preventing cancer, so much as with its ability to unmask a cancer that is already present and essentially weed them out from the future risk pool for cancer development. And when you weed out those at a higher risk for clinical cancer, by definition you have a group with a lower than standard risk, creating the potential for a selection bias. Make sense?

Conversely, the risk of a cardiac event starts to increase roughly at the same time as the risks for cancer and bleeding begin to drop. This to me suggests confirmation that aspirin may prevent cardiovascular events early in the course of taking it. Furthermore, given my hypothesis above about aspirin's weeding out those with an early cancer, perhaps its cardiovascular impact is for some reason limited to those with an early cancer or with another reason for aspirin-induced bleeding.

All-in-all the data do not convince me to start taking aspirin -- I am still at odds with Dr. Agus on that. The selection bias that I described above may very well mean that aspirin's role is not as a cancer prevention, but more likely as a sort of a stress test for those with a subclinical cancer. So we are left again with the the chicken-and-egg question. But isn't that, after all, what makes science exciting?

Would love to know what others think -- does this make sense? Are there other possible explanations?

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March 20, 2012
The probabiltiy dozen of participatory medicine: Yesterday my rant about uncertainty and probability got quite a bit of play in cyberspace, and I am glad.

Uncertainty is ubiquitous. We consider the odds of rain when choosing what to wear. We do (or at least we should do) a quick mental risk-benefit analysis before buying a burger at Quickie-Mart. We choose our driving routes to work based on the probability of encountering heavy traffic. We do this mental calculus subconsciously but reliably, mostly getting it right. What is odd, though, is that there are certain parts of our lives where we expect complete and utter certainty. I will not get into the political aspects of this fallacy, but I do want to continue down this line of reasoning about healthcare.

As I said yesterday, and many many times in the past, the only certain thing about medicine is uncertainty. And here is what I want you to understand deeply: the amount of uncertainty is much greater than you think. So, every time you say to yourself "I think there is a lot of uncertainty in this information," multiply it by 100, and then you may get close to just how uncertain most information is.

And again, I want to emphasize that this uncertainty gets magnified in the office encounter. So, what is the solution, short of having everyone understand the totality of evidence? Yesterday I said that the solution is to teach probability early and often, and this is indeed the best long-range answer. But is there anything we can do in the short-term? The answer, of course, is yes. And here is what it is.

Everyone needs to learn what questions to ask. Instead of nodding your head vigorously to everything your doctor says, put up your hand and ask how certain s/he is that s/he is on the right track. Here is a dozen questions to help you have this conversation:

1. What are the odds that we have the diagnosis wrong?
2. What are the odds that the test you are ordering will give us the right answer, given the odds of my having the condition that you are testing me for?
3. How are we going to interpret results that are equivocal?
4. What follow-up testing will need to happen if the results are equivocal?
5. What are the implications of further testing in terms of diagnostic certainty and invasiveness of follow-up testing?
6. If I need an invasive test, what are the odds that it will yield a useful diagnosis that will alter my care?
7. If I need an invasive test, what are the odds of an adverse event, such as infection, or even death?
8. What are the odds of missing something deadly if we forgo this diagnostic testing?
9. What are the odds that the treatment you are prescribing for this condition will improve the condition?
10. How much improvement can I expect with this treatment if there is to be improvement?
11. What are the odds that I will have an adverse event related to this treatment? What are the odds of a serious adverse event, such as death?
12. How much will all of this cost in the context of the benefit I am likely to derive from it?

And in the end, you need to understand where these odds are coming from -- the clinician's gut or evidence or both? I prefer it when it integrates both, which, I believe, was the original intent of evidence-based medicine.

Perhaps for some of us this is a stretch: we don't like numbers, we are intimidated by the setting, the doc may be unhappy with the interrogation. But it is truly incumbent on all of us to accept the responsibility for sharing in these clinical decisions. I believe that the docs of today are much more in tune with shared decision-making, and understand the value of participatory medicine. And if they are not, educate them. Ultimately, it is your own attitude to risk, and not just the naked data and the clinician's perceptions of your attitude that should drive all of these decisions.

Knowledge is empowering, and empowerment is good for everyone, patient and clinician alike. As patients, taking control of what happens to us in a medical encounter can only bring higher odds of a desirable outcome. For physicians, a cogent conversation about their recommendations may help safeguard against future litigation, not to mention augment the satisfaction in the relationship.

And thus starting to discuss probabilities explicitly is very likely to get us to a better place in terms of both quality and costs of medical care. And in the process it may very well train us how to make better decisions in the rest of our lives.

I would love to hear about your experiences discussing probability, be it in a medical or non-medical setting. And as always, thanks for reading.

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March 19, 2012
How medicine is like quantum physics: When a patients goes to his doctor to get fixed a pivotal triad of presentation-diagnosis-treatment ensues. The three steps are as follows:
1. History, physical examination and a differential diagnosis
When the patient shows up with a complaint, a constellation of symptoms and signs, a good clinician collects this information and funnels it through a mesh of possibilities, ruling certain conditions in and others out to derive the initial differential diagnosis.
2. Diagnostic testing
Having gone through the exercise in step 1, the practitioner then decides on appropriate diagnostic testing in order to narrow down further the possible reasons for the person's state.
3. Treatment
Finally, having reviewed all the data, the clinician makes the therapeutic choice.

These three steps seem dead simple, and we have all experienced them, either as patients or clinicians or both. Yet the cause for the current catastrophic state of our healthcare system lies within the brackets of each of these three little domains.

The cause is our failure to acknowledge the vast universe of uncertainty dotted sparsely with the galaxies of definiteness, all shrouded in false confidence. And while the cause and the way to address it are conceptually simple, the remedy is not easy to implement. But I am jumping ahead; first I have to convince you that I have indeed discovered the cause of this ruin.

Let's examine what goes on in step 1, the compilation of history and physical to generate a differential diagnosis. This is usually an implicit process that takes place mostly at a subconscious level, where the mind makes connections between the current patient and what the clinician has learned and experienced. What does that mean? It means that the clinician, within the constraints of time and the incredible shrinking appointment, has to listen, examine, elicit and put together all of the data in such a way as to cram them into a few little diagnostic boxes, many of which contain much more material than a human brain can hold all at once, even if that brain is at the right tail of human cognition (or not). What overtakes at this step is a bunch of heuristics and biases. Have we talked about those enough here? Just to review, heuristics are mental shortcuts that can serve us well, but can also lead us astray, particularly under conditions of extreme uncertainty, as in a healthcare encounter. If you want to learn more about this, read Kahneman, Slovic and Tversky's opus "Judgement under uncertainty: Heuristics and biases." As for cognitive biases, I will not belabor them, as there is enough material about them on this web site and elsewhere to overload a spaceship.

The picture that emerges at this step is one of fragments of information gathered being fit into fragments of studies and experience, stirred with mental shortcuts and poured into a bunch of baking tins shaped like specific diagnoses. Is there any room in this process for assigning objective probabilities to any of these events? Well, there is an illusion of doing so, but even this step is done by feel, rather than by computation. So while there is some awareness of a probabilistic hierarchy, it is more chaos than science. Given this picture, it's a wonder it actually works as well as it does, don't you think?

The next step in this recipe is the diagnostic workup. What ensues here is utter Wild West, particularly as new technologies are adopted at breakneck speed without any thought to the interpretation of data that they are capable of spitting out. Here the confusion of the first step gets magnified exponentially, just as it seduces us into further illusion of certainty. The uncertainties in arriving at the differential get multiplied by the imperfections of diagnostic tests to give the encounter truly quantum properties: you may know the results or you may know the patient, but you may not know both at the same time. What I mean is what I have always said on this blog: no test is perfect, and because of this simple truth, unless we know the pre-test probability of the disease in a particular patient, as well as the characteristics of the test, we have no idea about the context of these results. Taking them at face value, as we know, is a grave error.

What follows these results is frequently more diagnostic hit-or-misses, as the likelihood of harm and escalating expenditures without any added value rises. Then comes the treatment, with its many uncertainties and the potential for adverse events, and what are we left with? A pile of costly and deadly steaming manure. So, what's a doc to do?

I think that there is a very simple solution to this, and in its simplicity it will be incredibly hard to implement: education. And I don't just mean medical education. Everything that I have talked about in this post echoes back to the concept of probability. In the secondary education, at least as I remember it, probability is left to Advanced Math. By the time a student becomes eligible to take this course, she has been made to feel that she does not have the facility for math, and that, furthermore, math is boring and useless. So, while my friends in education may have a much better idea of what percentage of kids leave high school having been exposed to some probability, my guess is that it is woefully small. And those that do get exposure to it walk out of class perfectly able to bet on a game of craps or a horse race, but no clue how to apply these ideas to the world they live in.

And so those who progress into healthcare and those who don't have heard the word "probability," but cannot quite understand how it impacts them beyond their chance of winning the lottery. And unfortunately, I have to tell you that, if I relied on what I learned in medical school about probability, well, let's just say it is highly improbable that we would be having this discussion right now. This is why I do now and will for the foreseeable future harp on all of these probabilities, so that when you are faced with your own medical decisions, you will at least know the right questions to ask.

I know I need to wrap this up -- I saw that yawn! Here is the bottom line. First, we need to acknowledge the colossal uncertainties in medicine. Once we have done so, we need to understand that such uncertainties require a probabilistic approach in order to optimize care. Finally, such probabilistic approach has to be taught early and often. All of us, clinicians and patients alike, are responsible for creating this monster that we call healthcare in the 21st century. We will not train it to behave by adding more parts. The only way to train it is to train our brains to be much more critical and to engage in a conversation about probabilities. Without this shift a constructive change in how medicine is done in this country is, well, improbable.

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