Medicine and the internet: Harnessing the yottabytes

What if medicine in the US is just like the internet? What if it is just as difficult to separate the chaff from the wheat in medicine as it is on the web?

Both the curse and the blessing of the web is its accessibility. This means that anyone's voice can be heard. And it also means that anyone's voice can be heard. So, we are just as likely to stumble upon drivel as we are on information gold. And what takes time and skill is separating the two into neat piles, one to be ruthlessly discarded, and the other cherished for how it enriches us. To be sure without the web we might not have had access to either, and it is the egalitarian nature of the internet that gives us such a variety of sources in our information diet.

Now, let's look at medicine. Every day we hear about how much noise there is in the field, and this noise is difficult, if not impossible, to separate from the signal. Some signals are becoming much clearer, and they tell us that by being too egalitarian in medicine, we have likely been causing great harm. Take, for example, PSA and mammography screenings. The drumbeat of harm associated with these highly non-specific tests and the resultant chase after false positive results, is getting deafening, and rightfully so. Every day we hear that researchers have uncovered a breakthrough mechanism or treatment, and we hear with increasing frequency that a treatment previously thought to be sacrosanct is a bunch of rubbish. What gets lost among all this noise is the possibility of a true breakthrough in disease management or treatment or cure.

Think how hard it is to separate general valuable content from bunk on the web. Now, think of the logs of increase in the levels of difficulty of this task in medicine, where difficult concepts are further shrouded in the opaque cloth of arcane and obfuscating terminology. In fact, it is so difficult, that the class previously designated as the interpreters of this information for the lay public, physicians, are unable to keep up. There is a need for a whole new class of interpreters now -- researchers and patient advocates. And while this is good for the market and the economy, since it creates jobs that had not existed before, it begs a more critical evaluation vis a vis its impact on public's health. It also begs the question of the value of this gadgetry and information glut in medicine -- what is truly the wheat and what is the chaff? And what happens when you continuously try to drink from a fire hose? And do we turn down the stream, or is there another way?

Is it feasible to limit this stream of idea and information generation? Furthermore, is it sensible to do so? Many worry that putting limitations on this is tantamount to stifling innovation. But what is innovation? The most pertinent definition to the current discussion in the Merriam-Webster dictionary is "a new idea, method or device." Nowhere does the definition incorporate the value of this idea, method or device. Perhaps it is left to the free market to determine this value and ultimate use of such innovation. Well, in a market that claims to be free, but is filled with cynical machinations in the form of favoritism, subsidies and pricing games, is objective value really what is valued? And indeed, given the complexity of these "innovations", is it even possible for the end-user to judge their value, even if the market were free?

Yet, even despite all these challenges to establishing the value of innovation on the back end, I am not sure that centrally limiting idea generation is either feasible or right. In the case of ideas on the web, I have come to the conclusion that such microblogging platforms as Twitter can be invaluable filters of information, where my network of favorite tweeters whom I follow faithfully provides me with the wheat that has already been cleaned, yet not always overprocessed. Is this possible in medicine? I know that the FDA and CMS are supposed to provide some filtration for such medical information and interventions, but each is statutorily handcuffed and gagged not to stray beyond their legislative agendas. Therefore, a value filter should not be a body beholden to the letter of the law, or to political or financial interests. It needs to be driven by the spirit of scientific curiosity, objective evaluation and pragmatism. Most importantly, it must be open to a conversation that incorporates respectful dissent and many different perspectives.

Twitter arose out of the drive to share information, and it has shaped itself as a tool for developing value in the gargantuan and ever-growing world of yottabytes. Perhaps it is citizen bloggers and tweeters, including e-patients and clinicians and researchers and writers and others, who will ultimately solve this information glut in medicine by extracting the kernel of usefulness from this morass of vegetation. Harnessing this power systematically and accurately is the next challenge of our information age.

Because ultimately, for human cognition and health, less is more. And we are still human.              

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Data mining: It's about research efficiency.

I have taken a little break from my reviewing literature series -- work has superseded all other pursuits for a little while. But I did want to do a brief post today, since this JAMA Commentary really intrigued me.

First thing that interested me was the authors. Now, I know who Benjamin Djulbegovic is -- you have to live under a rock as an outcomes researcher not to have heard of him. But who is Mia Djulbegovic? It is an unusual enough surname to make me think that she is somehow related to Benjamin. So, I queried the mighty Google, and it spat out 1,700 hits like nothing. But only one was useful in helping me identify this person, and that was a link to her paper in BMJ from 2010 on prostate cancer screening. On this paper (her only one listed on Medline so far), she is the first author, and her credentials are listed as "student", more specifically in the Department of Urology at the University of Florida College of Medicine in Gainesville, FL. The penultimate author on the paper is none other than Benjamin Djulbegovic, at the University of South Florida in Tampa, FL. So, I am surmising from this circumstantial evidence that Mia is Benjamin's kid who is either a college or a medical student. Why does this matter? Well, there seem to be so few papers in high impact journals that are authored by people without an advanced degree, let alone in the first position, that I am in awe of this young woman, now with two major journals to her name -- BMJ and JAMA. This is evidence that parental mentorship counts for a lot (assuming that I am correct about their relationship). But regardless, kudos to her!

Secondly, the title of the essay really grabbed me: what is the "principle of question propagation", and what does it have to do with comparative effectiveness research (CER) and data mining? Well, basically, the principle of question propagation is something we talk about here a lot: questions beget questions, and the further you go down any rabbit hole, the more detailed and smaller the questions become. This is the beauty and richness of science as well as what I have referred to as "unidirectional skepticism" of science, meaning that a lot of the time, building on existing concepts, we just continue down the same direction in a particular research pursuit. This is why Max Planck was right when he said

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.

So, yes, we build upon previous work, and continue our journey down a single rabbit hole our entire career. Though of course there are countless rabbit holes all being explored at the same time. It is really more of a fractal-like situation than a single linear progression. What is clear, as the authors of the Commentary point out, is that this results in the ever-escalating theoretical complexity of scientific concepts. What does this have to do with anything? This, the authors state, argues for continued use of theory driven hypothesis testing, given that medical knowledge will forever be incomplete. And this brings them to data mining.

Here is where I get a little confused and annoyed. They caution the powers that be from consigning all clinical research to data mining, at the expense of more rigorous studies to pursue hypothesis testing. They argue that mining data that already exist is limiting precisely because it is constrained by the scope of our current knowledge, and that we cannot use these data to generate new associations and new treatment paradigms. They further state that emerging knowledge will require updating these data sets with new data points, and this, according to the authors

...creates a paradox, which is particularly evident when searching for treatment effects insubgroups—one of the purported goals of the IT CER initiative. As new research generates new evidence of the importance for tailoring treatments to a given subpopulation of patients, the existing databases will need to be updated, in turn undermining the original purpose to discover new relationships via existing records.

Come agin? And then they say that "consequently, the data mining approach can never result in credible discoveries that will obviate the need for new data collection". Mmhm, and so? Is this the punch line? Well, OK, they also say that because of all this we will still need to do hypothesis testing research. Is this not self-evident?

I don't know about you, but I have never thought that retrospective data mining would be the only answer to our research needs. Rather, the way to view this type of research is as an opportunistic pursuit of information from massive repositories of existing data. We can look for details that are unavailable in the interventional literature, zoom in on the potentially important bits, and use this information to inform more focused (and therefore pragmatically more realistic) interventional studies.

Don't take me wrong, I am happy that the Djulbegovics published this Commentary. It is really designed more as an appeal to policy makers, who, in their perennial search for one-size-fit-all panaceas, may misinterpret our zeal for data mining as the singular answer to all our questions. No indeed, hypothesis testing will continue. But using these vast repositories of data should make us smarter and more efficient at asking the right questions and designing the appropriate studies to answer them. And then generate further questions. And then answer those. And then... Well, you get the picture.

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