Thank you for your support

A few moths ago I quietly put  a "Donate" button in the right margin of my blog. It was an experiment to see what it can do just by sitting there. But now that I have had a few donations come my way, I feel the need to explain why I decided to solicit donations.

I have now been blogging for a little over three years, and have a small but loyal following. One of the ways I have considered to support my blogging is by allowing ads and sponsorships on the site. Having considered that, I have ruled it out as a possibility for the moment. The reason is simple: even if I manage not to give in to the cognitive biases created by a financial relationship, there will still be the hazard of a perception of conflict. And I prefer to keep my blogging free of such real or imagined conflicts.

At the same time, when I do a meaty post, I spend considerable time on it, and often wish I had more time and resources to spend. So, I thought that direct donation through the site might be a good way to support my blogging.

The posts that seem most valuable, those deconstructing studies, take the longest time and the most effort to do. And it's no wonder: The amount of misinformation that is available to us is staggering, and it is not always clear how to filter it. Although I acknowledge that my interpretations are still informed by my own (conscious or unconscious) cognitive biases, I try to be as transparent as possible about where I am coming from.

Thank you all for your support and for encouraging me to continue this work.        

So, if you like Healthcare, etc., please consider a donation (button in the right margin) to support development of this content. But just to be clear, it is not tax-deductible, as we do not have a non-profit status. Thank you for your support!

Unstructured medical record: Another case of "not everything that counts can be counted"?

Have you ever wished that instead of choosing a single answer on a multiple choice exam you could write an essay instead to show how you are thinking about the question? It happened to me many times, particularly on my medical board exams, where the object seemed more to guess what the question writers were thinking than to get at the depth of my knowledge. And even though each question typically had a menu of 5 possible answers, the message was binary: right vs.wrong. There was never room for anything between these two extremes. Yet this middle ground is where most of our lives take place.    

This "yes/no" is a digital philosophy, where strings of 0s and 1s act as switches for the information that runs our world. These answers are easily quantifiable because they are easily counted. But what are we quantifying? What are we counting? Has the proliferation of easily quantifiable standardized testing led us to more and deeper knowledge? I think we all know the answer to that question. Yet are heading in the same direction with electronic medical data? Let me explain what I mean.  

There was an interesting discussion yesterday on a listserv I am a part of about structured vs. unstructured (narrative) clinical data. I don't often jump into these discussions (believe it or not), but this time I had to make my views heard, because I believe they are similar to the views of many clinicians.

My understanding of one of the comments was that the narrative portions of the medical record are less valuable, since they are more difficult to digitize, and, therefore, do not represent data per se. The narrative, it was stated, serves more as a memory and communication aid for the clinician, and furthermore it frequently misses patient perspective. Right or wrong, what I walked away with is that "narrative parts of the record are not as valuable as the structured parts."

I responded:

Isn't the point of the narration to synthesize the structured data into a coherent whole that feeds into the subsequent plan? I completely agree that the patient perspective is often missing, but the two are not mutually exclusive. In fact, I see the synthesis of both narratives giving rise to most valuable interpretation and channeling of the "objective" data. 

When I was in practice, I often had patients referred to me in consultation for lung issues. And while the S and O parts of the SOAP note (Subjective, Objective, Assessment, Plan) lent themselves relatively naturally to structured data, the A and P parts really did not. In the A I waxed poetic about how the S and the O fit together for me and why (why I thought that diagnosis X was more likely than diagnosis Y), and in the P I discussed why I was making the recommendations I was making. And while... this was a way to communicate with other docs and to jog my memory about the patient when I saw him/her next, this was the most valuable part for me, since it had already contained the cognitive piece of the process. And yet this was the part that was most challenging to fit into a predetermined structure of a EMR. 

The richness of patient participation would come in a dialogue between clinicians and their patients, which I still think requires a narrative, no? 

And then someone else piped in and talked about two-dimensional holes being forced to hold the multidimensionality of our health and being, and the disruption that this represents. And I have to say this is exactly my recollection of my interactions with the (very early) EMR that my group adopted. I had loved to sit and write my SOAPs by hand, even the S and the O, but especially the A and the P. There was something about the process that solidified and imprinted for me the particular patient. The printed dictation was secondary to that somehow, and I always tended to refer to my hand-written note. This is how I knew my patients. That was slow medicine.  

Perhaps this was a byproduct of how I grew up -- largely analog, not anywhere near as digital as the younger generations, and much more inclined to put my thoughts on paper through the interaction of my brain, hand and pen. Strangely, today I have a hard time writing by hand, and my preferred method is to type on my keyboard. So, perhaps some brain rewiring has taken place. All I can say is this: for me the act of writing down my thoughts about the patient made the encounter real and memorable. Even more, I think it lent a certain level of respect to the individuality of each encounter. It is possible that if I were in practice today, I could achieve the same by typing my note.

But the writing medium is somewhat separate from "structured vs. unstructured" data. The "ideal" structured record is all about yes/no checkboxes. Having to fit our analog thoughts into such limited and limiting digital environment is distracting. And a lot of data from brain science suggest that it takes us a while to get back to the same level of focus on a task or a thought once we have been distracted. A clinical encounter is woefully brief, and these distractions can and do reduce its efficiency and integrity.

As a health services researcher, I rely on digital data for my work. Yet as a clinician I railed against it, as many clinicians continue to do today. Is the answer to abandon all electronic pursuits? Of course not. But I am baffled by the attitude that it is clinicians' duty to adapt to the digital way of thinking rather than the other way around. There does not seem to be a recognition that the purpose and meaning of a clinical record is different to a clinician from what it is to a researcher or a policy maker. Yet the current EMR development seems to focus on the latter two constituencies virtually ignoring the clinical setting. Given our vastly improved computing capabilities over the last decade, why does a clinician still have to think like a computer? Moreover, if medicine is indeed a mix of art and science, do we really want our doctors to fit strictly into the digital model?

I believe that it is a lazy way out for developers. They are the ones that need to step up and create an electronic record that does not gratuitously disrupt the clinical encounter. This record needs to fit the work flow of clinical medicine like a glove. We cannot wait for some miracle to come along and magically transform our sick healthcare system. Today's EMR will not succeed unless it takes into account the art of medicine. The narrative parts of the record must be preserved and enriched by patient collaboration, not eliminated in the interest of easy bean counting. Because several smart people tell us that "not everything that counts can be counted, and not everything that can be counted counts." It's time to pay attention.   

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Plagiarism and advertising and COI, oh my!

Update #4, 7:45 AM Eastern, Sunday, June 17 (Happy Father's Day, everyone!)
As of this morning, the HealthWorks Collective has taken down the story as well. What is interesting to me is that neither OneMedPlace nor HealthWork Collective has put any explanation on their respective site, and the reader is essentially consigned to finding "ERROR 404." I sure hope that this is not either of the organization's attempt to sweep the whole thing under the rug. 


Update #3, 6:00 PM Eastern (and last for tonight, I hope)
Another e-mail from Matt Margolis, in which he requested that I let my readers know that 

...we took down our piece after an internal discussion, before you and I made any contact. Hence my request to remove a reference to us.

Still no answer on Aviisha -- perhaps it will appear in the "similar piece" they are planning to publish next week. looking forward to it. 


Update #2, 4:35 PM Eastern
Another exiting update for y'all. A few hours ago I got a message from a Matt Margolis at OneMedPlace informing me that the company has taken the post down after realizing their mistake. Well, why don't I just share the whole message (emphasis mine)?

Hello Marya,

Thank you for reaching out to us. We quickly realized there was an error in attribution and have since pulled the story. We would appreciate at this time that you do not include our name or our article in your post. However, we will be publishing a similar piece next week that will incorporate some of the facts in the NYT story. At which time, I am happy to answer any questions to help give depth to your piece.

Best,

Matt Margolis

Managing Editor, OneMedPlace

I responded asking for "more depth" with respect to whether Aviisha is a client of OneMedPlace. I will let you know what I hear when I hear it. And by the way, the HealthWorks Collective still has the story on its page. 


Update #1, 1:45 PM Eastern
As of right now, the OneMedPlace story page has been taken down. No one from OneMedPlace has communicated with me at this point. The story is still up on the HealthWorks Collective site here. 

About four years ago I decided that it was time to learn more about the recent abundant advances in brain science. Since then, I have read avidly on our neurobiology, behavioral economics and decision science. I have learned about our predictable irrationality, biophilia, heuristics and biases, and our drive to create linear explanations for phenomena where none exists. I also learned about priming, where subtle messages delivered prior to a task's completion (did you know that you can, for example, get kids to improve more on their exams by commending their hard work than their native brilliance?) influence the outcome of the task.

It is in this context of (some) understanding about how the human brain assembles its information pathways that I read this story from yesterday's OneMedPlace News (also reprinted by The HealthWorks Collective here under the byline of Herina Ayot, the Managing Editor for OneMedPlace). This story about a correlation between severe sleep apnea and cancer, starts out thusly:

Two new studies have found that people with sleep apnea, a common disorder that causes snoring, fatigue and dangerous pauses in breathing at night, have a higher risk of cancer. The new research marks the first time that sleep apnea has been linked to cancer in humans.

About 28 million Americans have some form of sleep apnea, though many cases go undiagnosed.

[...] For sleep doctors, the condition is a top concern because it deprives the body of oxygen at night and often coincides with cardiovascular disease, obesity, and diabetes.

All of this is true and truly concerning. The next two paragraphs state

In light of the recent studies, Aviisha Medical Institute, LLC is taking $200 off the cost of its home sleep test, which was originally $449.49, and offering free assessments for the duration of May. The special offer is intended to encourage the public to get tested for sleep apnea and raise awareness about the deadly consequences of untreated apnea. Studies estimate that 85% of sleep apnea sufferers don’t know they have the condition.

One may speculate that other diagnostic technologies developers may promote offers in light of this newfound cancer correlation, as well.

This is when I got a little uncomfortable thinking that this is an advertisement rather than a story. And the final statement really got my hackles up:

Although the study did not look for it, study author Dr. Miguel Angel Martinez-Garcia, of La Fe University and Polytechnic Hospital in Spain, speculated that treatments for sleep apnea like continuous positive airway pressure, or CPAP, which keeps the airways open at night, might reduce the association.

And how does sleep apnea cause cancer?

Lead author Dr. F. Javier Nieto, chair of the Department of Population Health Sciences at the University of Wisconsin School of Medicine and Public Health, commented that five times the risk of cancer is more than just a statistical anomaly. Previous studies in animals have shown similar results, while other studies have linked cancer to possible lack of oxygen or anaerobic cell activity over long periods of time, therefore, it’s possible poor breathing fails to oxygenate the cells sufficiently. 

But then even more happened. From the very beginning of the article, I had sensed something familiar in it. It was my recollection that I had seen this story before about the two studies presented at the American Thoracic Society last month. Dutifully clicking on the link provided in the first paragraph of Ayot's story, I found myself on the NYT's "Well" blog reading the post from May 20, 2012, by Anahad O'Connor. Here is how it starts:

Two new studies have found that people with sleep apnea, a common disorder that causes snoring, fatigue and dangerous pauses in breathing at night, have a higher risk of cancer. The new research marks the first time that sleep apnea has been linked to cancer in humans.

About 28 million Americans have some form of sleep apnea, though many cases go undiagnosed. For sleep doctors, the condition is a top concern because it deprives the body of oxygen at night and often coincides with cardiovascular disease, obesity and diabetes.

And then, disappointingly, toward the end of the post:

Although the study did not look for it, Dr. Martinez-Garcia speculated that treatments for sleep apnea like continuous positive airway pressure, or CPAP, which keeps the airways open at night, might reduce the association.

A couple of things shocked me (in addition to the final statement about CPAP):
1). Ayot's story was almost verbatim (with the exception of the Aviisha advertisement) reprinted from O'Connor's story. There did not seem to be an attribution, unless linking the the original post counts as one. Please, someone who is well versed in this, tell me if this is an acceptable way to attribute. I'll tell you now that in the academic circles this would be (and has been) called plagiarism. As you may recall, I am pretty sensitive to this, having had my work plagiarized recently.
2). The thinly veiled advertisement (inserted into the body of this story which is practically copied from the NYT word for word). The advertisement would not bother me if it had stayed on the OneMedPlace web site -- after all they seem to be a PR agency. But is was reprinted on the HealthWorks Collective's site as a legitimate news item, and I don't believe that HWC is a purveyor of advertorials.

I browsed the web site of Ayot's employer OneMedPlace to see if there is evidence that Aviisha is a client, but did not find any, though my search was admittedly perfunctory. I have reached out to the company for a comment and to understand whether a conflict of interest may exist with Aviisha, but have not heard from them at this time. I will update the post if and when I hear from them.

So drawing on my limited understanding of how the brain works, here is what I am thinking this piece aims to accomplish:
1). Create an awareness of a condition that is apparently common and largely undiagnosed, and do it using words from a high-impact publication
2). Prime the reader with the idea that we understand how it causes cancer (if only in laboratory animals, maybe)
3). Set up Aviisha (and "other diagnostic technology developers") as solution providers
4). Create a linear path to CPAP as the answer

But this is just my uneducated guess at how the human brain may perceive this story. Of course, I could just be playing right into my cognitive biases.   

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A FORCE against disease mongering

Have you been over to The Oransky Journal lately? If not, go and see what is happening there. What is happening is a microcosm of the larger debate we are having about detection and diagnosis of real disease versus overdiagnosis of phantom conditions whose treatment is worse than anything that the potential disease may deliver.

The issue is as follows. In his talk at TEDMED in April, Ivan gave an excellent and measured presentation about the folly of pre-disease classifications and the harm they can bring. As my readers are well aware, this is the subject of great interest to me -- after all, it is a travesty that contact with the so-called "healthcare" system is the third leading cause of death in the US, and that overtreatment costs us at least 10 cents of each healthcare dollar, and probably much more (you will find a slice of my posts on this issue here). So, Ivan's talk was timely and cogent.

After he posted the talk on his blog, he received a letter from a group called FORCE (Facing Our Risk of Cancer Empowered) who, as it turns out, coined the word "previvor," one of the many words Ivan used to illustrate the philosophy of disease mongering. The letter voiced a vigorous objection to Ivan's use of the word to "misunderstanding" its meaning. But what really happened?

Apparently, "previvor" defines a group of people who are at a heightened risk for cancer, but have not yet been diagnosed. It seems that the majority of FORCE's constituency consists of women with the BRCA gene mutations, which put them at an extraordinarily high risk of several cancers, most notably breast and ovarian. Moreover, these cancers tend to occur at an early age, and are generally quite a bit more aggressive than those not associated with these mutations. We are not talking a trivial rise in the risk either; BRCA1, for example, raises one's lifetime risk for breast cancer to about 80%! To mitigate this risk, many women with these types of mutations undergo prophylactic mastectomies and oophorectomies. These are life-changing events, and their genetic make-up hangs like a Damocles' sword over the offspring of these women as well. So, what's the problem with using whatever word suits them?

The issue is the group's definition of this neologism "previvor." As quoted in Oransky's post (italics mine):

“Cancer previvors” are individuals who are survivors of a predisposition to cancer but who haven’t had the disease. This group includes people who carry a hereditary mutation, a family history of cancer, or some other predisposing factor. The cancer previvor term evolved from a challenge on the FORCE main message board by Jordan, a website regular, who posted, “I need a label!” As a result, the term cancer previvor was chosen to identify those living with risk. The term specifically applies to the portion of our community which has its own unique needs and concerns separate from the general population, but different from those already diagnosed with cancer.

So, the definition is quite broad, as you can see, especially the "some other predisposing factor." Who doesn't have one? Just by virtue of being alive we have predisposing factors to many diseases, including cancer. And aging is one of the strongest predisposing factors to cancer as well. The concern is that a broadly defined term like this plays right into our national paranoia about our health and our enthusiasm for screening as the primary mode of prevention. And if you really don't feel well informed about why screening is not all it's cracked up to be, I urge you to dig through the annals of this site thoroughly (if you don't have much time, you can get a solid primer on the issue from my book). In my view, given the extent of the harm from overdiagnosis and overtreatment, Oransky's call-out of this word in the ultra-visible forum of TEDMED was a public service.

And indeed, it turned out this public service has gone well beyond just delivering the information. The discussion that ensued over the last couple of days with FORCE has shown what this organization is made of. An 80% lifetime risk of breast cancer is a grave matter, and the group is an important force in advocating for these patients and supporting their families. But as it turns out, it stands for even more than that. I commend Dr. Friedman, the Executive Director of the group, for being open to narrowing the definition of the term "previvor." This willingness signifies a real desire to do the right thing not only for her constituency, but also for the public at large. Even more, she should be proud that her organization is taking a stand against disease mongering.

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Healthfinder.gov: Education or indoctrination?

Ever heard of healthfinder.gov? It's a web site from the US Department of health and Human Services

...where you will find information and tools to help you and those you care about stay healthy.

Sounds like a laudable goal, right? Great! Now, help me! Here is the "help" that I found when I went to the page called "Colorectal Cancer Screening: Questions for the doctor":

What do I ask the doctor?

It helps to have questions for the doctor written down ahead of time. Print out these questions and take them to your next appointment. You may want to ask a family member or close friend to come with you to take notes.

So far so good. But here is the list that follows:

• What puts me at risk for colorectal cancer?

• When do I need to start getting tested?

• How often do I need to get tested?

• What screening test do you recommend? Why?

• What’s involved in screening? How do I prepare?

• Are there any dangers or side effects involved?

• How long will it take to get the results?

• What can I do to reduce my risk of colorectal cancer?

Note the wording: "When do I need to start getting tested?" "How often do I need to get tested?" And these "needs" come well before the "why?" In fact, the "why" never really comes. The oblique "why" about which test is recommended is too little too late. The real "why" is why, or even whether, I need to get tested in the first place. I am happy to see a question on the dangers of screening, but again it leaves plenty of room for the clinician to minimize and patronize.

The list of questions is built upon one (erroneous) assumption: Everyone is bound to perceive the risk-benefit equation of colorectal cancer screening the same way. We know this is false, and each person needs to make an individual decision based in what we know today and according to the values he/she places on the outcomes. The way the questions are written, they simply reinforce the bullying attitude of the screening bias, making those who swim against this tide feel irrational and unreasonable. But may I point out that some of us spoke out against universal mammography screening even before it became the main-stream recommendation? So perhaps there are good reasons to be more cautious with screening for everything, even colon cancer.

Science evolves, our knowledge evolves. What we think we know today will be modified tomorrow. I take a strong exception to this dogmatic and one-sided formulation of how to have a discussion about testing whose risk and benefit profile may not (and should not) elicit the same unbridled enthusiasm from everyone. So please, healthfinder.gov, rethink your "helpful" questions so as to educate, rather than indoctrinate.

Hat tip to @DCPatient for pointing me to this page  

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Why "efficacy" does not equal "effectiveness"

As you may have noticed, one of the biggest medical conferences is in progress right now in Chicago -- the American Society of Clinical Oncology. It is the premier meeting of cancer doctors, where new data are the order of the day. This stream of data is reflected in a constant barrage of media coverage. One of the stories I saw today serves as a great illustration of the title of this post: why efficacy does not equal effectiveness. Although I do discuss this in my book, I thought it might be nice to apply these ideas to a current story.

The MedPage story opens with this:

Older patients with advanced cancers treated in the community had survival that fell short of that for patients who received the same regimens in clinical trials, analysis of a large government database showed.

Most of the differences were small in absolute terms, except for patients with stage IV colorectal cancer treated with the FOLFIRI chemotherapy regimen. Patients in the community had a median survival of 16.1 months, which was 30% lower than that of patients treated with the same regimen in a cooperative-group randomized clinical trial.

Moreover, survival among FOLFIRI-treated older patients was 4 months shorter than that of older patients treated with FOLFOX chemotherapy, Elizabeth B. Lamont, MD, reported here at the American Society of Clinical Oncology meeting. 

This is a common conundrum, and a poster child for the distinction between efficacy and effectiveness. So, what are these two "e"s? Efficacy is essentially a statistical distinction between the outcomes of interest among patients who undergo a particular treatment compared to those who do not, or those who get treated with a placebo. Efficacy is measured under the controlled circumstances of a clinical trial, where only very specific patients are enrolled, very specific treatments are administered, and very specific outcomes are monitored. These trials usually randomize patients to either the treatment or the placebo, thus ensuring that treated and untreated patients are the same in all ways save for the treatment under examination. Efficacy is frequently represented by what we call surrogate outcomes, such as laboratory measurements or radiographic tests. In cancer studies, for example, a frequent surrogate outcome that is used is so-called progression-free survival. This refers to the duration of time from treatment that a person is alive AND the tumor has not shown any evidence of growth on a scan. Another frequently used surrogate measure is blood pressure as a marker for heart disease. These are surrogates because, although correlated with such important outcomes as death and heart attacks, respectively, they themselves do not tell us with any precision how our interventions impact these ultimate measures. I will not belabor at this time why we rely on efficacy and surrogate outcomes, since I go into detail about that in the book.

Effectiveness, on the other hand, is something altogether different. Effectiveness tells us exactly what happens in the real messy world to outcomes that matter, such as death and quality of life, in conjunction with the treatment in question. We have known for a long time that the outcomes we see in naturalistic studies are often much less spectacular than those reported in RCTs of efficacy. Why is this? And more importantly, which do we believe? The second question is easier to answer than the first: we believe what happens in the real world, because it is precisely what happens in the real world rather than in the laboratory of clinical research that matters. As to why this difference exists, there are many reasons for this, most of which I have discussed elsewhere on this blog and in Between the Lines. Some of the reasons may have to do with patient selection, which in real life tends to be less restrictive than in RCTs. For example, individuals who are more ill may get the intervention that was intended to be given to those with lesser illness severity. In this population the intervention may not prove to be as effective as in those who are not as ill. This is called "confounding by indication," and we talked about it most recently here. Other reasons may be that other conditions patients have in real life, which tend to be excluded from RCT populations, attenuate the impact of the intervention. When looking at mortality in cancer, patients with end-stage heart disease may be excluded from the RCT, but treated in the wilds of clinical practice. And this treatment may give us a Pyrrhic victory, where cancer is indeed held at bay, but the patient dies of his heart disease. And here is yet another reason for the efficacy-effectiveness disconnect: attribution. In clinical trials there is a meticulous process that has to be followed in order to attribute the cause of death to a particular disease. In real life -- not so: death certificates are a notoriously dicey source of information on the causes of death.

So here are some of the challenges with applying RCT data to the real world, illustrated so palpably in the story we started out with. Please, do not misunderstand my message: I am not saying that RCTs are useless. What I am saying (I must sound like a broken record by now) is that we need different types of studies to see the whole picture. RCTs by their nature are exclusive undertakings whose findings are only narrowly translatable to the real world. Naturalistic observational data are key components of building the entire jigsaw puzzle of how our interventions really work.                

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How to game mortality data

There is a great illustration in this BMJ article of what I discuss in Chapter 2 of my book: the type of mortality that matters. In the figure below from the paper, note that, as the new diagnoses of each of the cancers rise (green lines), the attendant "Deaths" (red lines) stay unchanged. If you look at the Y-axis of each graph, it tells us that the unit of measurement is "Rate per 100,000 people." So the red lines represent population mortality.

"Population mortality" means that the denominator for this value is all people in the population who are at risk for the disease in question. This means that for prostate cancer, for example, we include only those men who have a prostate and exclude all women and men who have had a prostatectomy. Population mortality stands in contradistinction to case fatality. The latter is defined as deaths among all the people diagnosed with the disease. So, for prostate cancer, case fatality would be deaths among all men who have been diagnosed with prostate cancer.

It is not difficult to see how case fatality is a somewhat circular, even self-referential, measure of our diagnostic prowess, but says very little about how well we are doing with disease treatment. If we have tests that are capable of picking up the most minute of diseases, those that are not likely to cause death in the first place, then the denominator becomes inflated with this noise, while the numerator, the actual fatalities, does not change. This leads of course to an apparent reduction in deaths from the disease, but a reduction that is an artifact of overdiagnosis. Population mortality, on the other hand, cannot be gamed this way, as you can see in the figure above. This is the only mortality that gives us honest feedback without a bias about how we are doing with our early detection and other interventions. In the case of the cancers in the figure, the answer is "not so well."

You can learn more about this in Chapter 2, where I even have a figure that illustrates this critical distinction.        

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Three wrong reasons to dismiss the azithromycin data

There is a lively discussion over at KevinMD about the azithromycin study in the New England Journal of Medicine, which I blogged about here. A certain ethos has emerged in the comments that bears unpacking. There are 3 distinct points that I am feeling unsetlled by:

#1. As a retrospective cohort study it should be ignored.
This is an intellectually indefensible position in my mind -- it is a study that uses a set of methods developed and validated for this type of data. Yes, it's complex; yes, it's messy; no, it's not to be ignored. The discipline of pharmacoepidemiology relies heavily on observational data. To expect anything more is to indulge in misapprehensions that a). it is feasible to run a RCT to detect such rare signals, and 2). that a RCT like that would give us a definitive answer.

#2. They really had to add a lot of zeros to the denominator to make the numerator seem impressive.
This is a baseless accusation, since 1 million prescriptions is not that difficult to generate. Z-pak has been on the market for over a decade, and according to this article, last year 55 million prescriptions for azithro were handed out in the US. So, just a back-of-the-envelope calculation for excess deaths per year at this rate is well over 2,000. And this is just in one year! So, as a safety signal this is not something to be trivialized.

#3. A concern that this information will keep patients from doctors' offices and delay needed treatment.   
I find this to be rather a hollow concern (though I am sure that the person putting it forward believes it wholeheartedly). As another commenter pointed out, the overuse and misuse of antibiotics is completely out of control! And yes, cardiac deaths from azithromycin are but a small part of the issue, where the elephant in the room is the evolution of resistance. It is not just these latest data that should keep patients as far away as possible from unnecessary healthcare encounters, seeing as how these encounters are the third leading cause of death in the US. Why aren't we worried that this entire monster is keeping patients away? And quite frankly, why isn't it?

So, all in all, I am very glad that Rob Lamberts chose to blog the study, and the discussion has been worthwhile. The comments have really confirmed for me that it is not only the lay public, but also healthcare professionals, who have a hard time interpreting data. And when a study is somewhat challenging, it is generally easier to let our cognitive biases run amok.      

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Septic shock doesn't need my rescue bias

Rescue bias and auxiliary hypothesis bias are tempting distractions when it comes to dealing with data that are counter to our preconceived notions. In general, the role of our cognitive biases in all kinds of discourse, including clinical and scientific, is under appreciated. For this reason I devoted fully two chapters of my book to cognitive biases.

Which makes it even more frightening that, as I read this NEJM paper on the failed drotrecogin PROWESS-SHOCK study, I am still looking for reasons why it failed, other than that just doesn't work. After so many years of trials and tribulations with Xigris, and hopes that this lone therapy ever to have been approved for treating this deadly disease, I am having a hard time letting go.

Yet the rial was meticulous, as all trials designed and overseen by B. Taylor Thompson are -- he is just a brilliant clinical researcher that we all need to learn from. The design considered all the right issues a priori -- multiple interim looks at the data, a possible need to increase the power, heterogeneous treatment effect, and others. Although there were a few numerical imbalances between the treatment and the placebo groups -- blood cultures were positive 4% more frequently and the offending pathogen overall was 5% more likely to be identified in the Xigris than in the placebo group -- I have to accept that these are not the reasons for the observed failure to improve either the 28-day or the 90-day mortality.

I have seen and even blogged about these data before -- the press release had some of them, but, more importantly, Taylor presented them at the Society of Critical Care Medicine meeting back in January. So, this is nothing new. Yet reading all of the details in the pages of the NEJM brings back that pre-conscious cognitive wall that I have to climb over in order to get to reality. And the reality is that the drug just did not work.

But the reality is also that sepsis patients have a better chance of surviving this deadly assault than they did 10 years ago. In the original PROWESS study 28-day mortality in the placebo group was 31%, and these were all kinds of sepsis patients. In the current study, among most severe sepsis patients, those with septic shock, the 28-day mortality was on the order of 25% in both arms -- that's a staggering reduction in this most ill septic population! We really need to appreciate this. In part this trend may be due to some evolution of the disease-host interaction. But in part it has to be because of the concerted effort to understand sepsis better, to study various treatment options, and, most importantly, to implement these learnings at the bedside. In no small part we owe these advances in sepsis care to the short life of drotrecogin alpha.

The data are clearly anticlimactic, and for this reason the current paper has no sex appeal -- just look at the (lack of) press coverage about it. Yet, this is a clear example of countering the traditional publication bias: Here is a manufacturer-funded negative study published in a premier peer-reviewed journal. I realize that it was always a high-profile undertaking, but let's pause for a moment to enjoy this small victory for those who have railed against the spread of biased information in the medical literature.

I don't know if and when another therapy will emerge courageous enough to brave the rough waters of sepsis and septic shock. But one thing is clear: Xigris has served out its purpose, and no amount of rescue bias from me will save it. Rest in peace.    

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"Imagine the Future of Medicine" Project

Yesterday's post hit a nerve: Who is planning the future of medicine? I don't mean the futuristic visions of single individuals, or even the palpable excitement that permeates rooms filled with health IT discussions. I am referring to an integrated vision for what we want to deliver, a vision that can then inform our actions. I am talking about strategy rather than tactics, as pointed out by Patrick Jonas in the comments here. Would you build a house without first developing a blue print? Why not? After all, we can all get excited about IKEA kitchen cabinets and stainless steel bathroom appliances. But if we don't first work with a team of people to think about the role of the structure informing its design, all we will have in the end is a pile of (shiny) rubble. So why is it that we are so averse to building a blue print for medicine? Or is it that we think it is politically impossible?

If I had to choose one most important thing I carried away from the Health Foo Camp, it would be this (as embodied by Regina Holliday): "Never doubt that a small group of thoughtful, committed people can change the world. Indeed it is the only thing that ever has." And in the age of the internet, which allows groups of committed individuals to come together in fractions of a second, there is no excuse not to try.

So, here is what I propose: How about we start having that discussion here? I will accept blog posts on the theme of "Imagine the Future of Medicine" to be posted on this site. I don't just want to hear from doctors, or those who have previously made their views known (though I encourage you to contribute too). I want to hear from everyone who has something to contribute to the discussion. I want to hear from patients, pharmacists, engineers, architects, nurses, professors, story tellers, artists, poets, anyone who can articulate their vision for what medicine of the future can and should be.

Before we start, we need to lay out the values that should inform everything in medicine. From were I sit, "do no harm" is still the most important one of all. Equally important seems to be access to safe, humane and compassionate care aligned with the patient's values, and that is guaranteed to all. Did I miss something important? Remember, we are going for high level here to refer back to when we recommend more specific details to make sure they fit this overarching purpose.

I don't want to micromanage this. I want to free-range it and see where it goes. Heck, I am not even sure that I will get any responses from all you busy people who have more important daily duties to fulfill. But I want to try. We seem to be so fixated on the trees that we are not even sure what forest we want to end up in. Let's paint that forest.

There are a couple of rules to keep in mind:
1. I will not publish any profanities, personal attacks or marketing pitches.
2. To keep the reading manageable, I ask to limit your posts to 500 words. If you feel that you really need an extra 100 or 200 for an extremely important message, I will consider that.
3. I reserve the right to do some light editing for grammar, syntax and clarity.
4. If you are unable to make yourself clear, please, request editorial help before you send in your contribution.

So, let's make Margaret Mead proud and see where this field experiment ends up.

You can e-mail me your contributions for review at healthcareetcblog AT gmail DOT com                

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Free-range thinkers create Foo for thought

I cite this favorite quote from Max Planck in my book (and every chance I get):

A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.

I think this applies to all walks of life, not just science. Yet sometimes an argument so compelling comes along that, though reluctantly at first, one by one the old guard drop at its feet. This is what happened to me this weekend at the Health Foo Camp in Cambridge, MA.

First, what is Health Foo? Well that was my first question when I received an invitation to attend this strangely named meeting. A Foo Camp is something put together by O'Reilly, the pioneering digital media group. Started 12 years ago, these meetings are thematic gatherings of "Friends of O'Reilly," hence "Foo," intended to bring together a diversity of thought about a specific field. The camp that I attended was the second such gathering in the healthcare space, supported in part by the Robert Wood Johnson Foundation, and held at Microsoft's New England Research and Development Center in Cambridge. How can I ever thank O'Reilly, RWJF and Microsoft for this mind-shifting event?

As I mentioned in my previous post, the attendee roster was so full of luminaries that I frankly wasn't sure that the invitation had not ended up in my Inbox by mistake. But mistake or not, what a privilege to attend! I spent the weekend getting to know the faces and the substance behind such familiar names as Regina Holliday, Paul Levy, Alan Greene, Ted Eytan, Susannah Fox, Gilles Frydman and others. And what still has my mind spinning is my conversations with people I don't normally interact with -- computational scientists, game designers, food advocates and international public health movers and shakers.

The most risky aspect of this meeting was the very essence of its success: we were to free-range. No agenda was set; space, food and company were provided. The resulting sessions ran the gamut from the usual nerd porn of probability to such far-reaching topics as memory and the role of faith, poetry and the arts in medicine (my personal favorite, where I got to play in the sandbox of participatory painting led by Regina. Take that, left brain!)

I have to say I spent a part of the weekend in a bit of a fog. What is gamification of medicine? What does "deep modularity" mean? But the full impact of such diversity of knowledge did not hit me until I was heading West on the Turnpike away from the meeting in the direction of home. It felt like a deep air pocket, and for a moment I couldn't catch my breath.

My epiphany was this: I have been sitting in my office and analyzing, writing and thinking about how to slow down this juggernaut of digitalization in healthcare. My logic has been to identify the problems, particularly the overdiagnosis and overtreatment and the attendant harm, all in the context of an obscene price tag, and to say that not only does medicine not need the radical digital revolution that is being imposed on it, but the very definition of medicine needs to change. What I failed to consider is that medicine is a module that needs to fit into the rest of what we call our modern life. So, slamming on the brakes in hopes of stopping this locomotive before it squashes the medical system is the wrong approach.

I can hear what you are thinking. "There goes another one." "She drank the Kool-aid." "If she gets all starry-eyed about technology, there is no hope for the rest of us." Well, I am not a fan of Kool-aid, but I do like Shakespeare. My realization is as follows: If we don't start thinking about what we want medicine to be, we will continue getting medicine that looks and works like a Rube Goldberg machine, a conglomeration of unrelated levers that may or may not achieve the desired results. The stakes are too high, stakeholders too many, and the resources being used staggering.

The digital revolution will continue its break-neck pace regardless of my opinions. In its quest to take over the world, it will continue to advance into every aspect of medicine. But instead of positioning it as a confrontation between Godzilla and King Kong, I have decided that a more constructive way is to start to imagine what medicine can and should be in the future. The current model is moribund, if not altogether dead. Nature abhors a vacuum, and unless we fill it with something that heals and nurtures that has come out of a concerted multidisciplinary blueprint, it will continue to grow into a hydra that will eventually swallow us.

I know, I miss the slide rule too. But we did not get to be on Twitter by chaining ourselves to the old paradigms. Regina Holliday taught me, among so many other things this weekend, the word "chaordic." It is a neologism that combines the ideas of chaos and order into one force of nature. I think there has been enough chaos in the relentless penetration of technologies into medicine. I am tired of screaming at at the back of the digital monster like a crazy lady. We need to get ahead of it with an open mind and even some excitement, and start imagining where we can direct it for the better health of the public. Our magical thinking will not change the fact that this tidal wave will destroy us if we lack the imagination to ride it. A gathering like the Health Foo Camp is what fuels that imagination. Let's grow and harness it!


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Why I have a propensity to believe the azithromycin data

Before I take off for the Health Foo Camp in Cambridge, MA, where I will be rubbing elbows with such luminaries as Susannah Fox, Ted Eytan, e-Patient Dave deBronkart, Regina Holliday, Nick Christakis and Nancy Etcoff, I thought I'd say a few words about one of my favorite topics: antibiotics. In particular I want to talk about azithromycin. A lot has been said about it this week already in the wake of the NEJM study (alas, the full paper is behind a pay wall) indicating an increase in the risk of cardiac death among patients exposed to this drug compared to those either not getting antibiotics or those receiving a different class of antimicrobials. And a lot more will be said in the future as the FDA reviews this risk. This is not specifically what I wanted to talk about. I wanted to focus on the methods.

The study was done meticulously, as far as I can see. It was a large Medicaid database from Tennessee (this may present a generalizability problem, of course, though I cannot think of a specific reason why it would). The study design was a retrospective cohort, which, as we already know, sets the study up for all kinds of biases. So, why should we believe anything the study showed? And particularly given many people's distaste for invoking causality from observational data?

There are at least 2 reasons why we should pay attention to these results. The first is that we are talking about the ultimate harm: death. When it comes to harm, my philosophical approach leans in the direction of caution. What this means scientifically is that I accept a much lower threshold for the certainty that the data convey than when it comes to evidence of benefit. This is an extension of the precautionary principle, where the burden of proof of safety now lies with the drug.

But there is a second, possibly more important reason that I am inclined to believe the data. The reason is called succinctly "propensity scoring." This is the technique that the investigators used to adjust away as much as feasible the possibility that factors other than the exposure to the drug caused the observed effect. In my book I briefly discuss propensity scoring in Chapter 21. And here is what I say:

Propensity scoring is gaining popularity as an adjustment method in the medical literature. A propensity score is essentially a number, usually derived from a regression analysis, describing the propensity of each subject for a particular exposure. So, in terms of smoking, we can create a propensity score based on other common characteristics that predict smoking. We take advantage of the presence of some of these characteristics also in people who are non-smokers to yield a similar propensity score in the absence of this exposure. In turn, the outcome of interest can be adjusted in several ways for the propensity for smoking. One common way is to match smokers to non-smokers based on the same (or similar) propensity scores and then examine their respective outcomes. This allows us to understand the independent impact of smoking on, say, the development of coronary artery disease. 

And if you are able to access Table 1 of the paper, you will see that their propensity matching was spectacularly successful. So, although it does not eliminate the possibility that something unobserved or unmeasured is causing this increase in deaths, the meticulous methods used lower the probability of this.

One final word about azithromycin. There are data that suggest that macrolides (the class of drugs that includes erythromycin, clarithromycin and azithromycin) are actually associated with improved outcomes in the setting of community-acquired pneumonia, or CAP. This is why these drugs are in the CAP treatment guideline. The point is that again, as in everything, the benefit of using azithromycin in any individual case will have to be weighed against this newly-identified risk.

  

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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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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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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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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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