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.
Showing posts with label CER. Show all posts
Showing posts with label CER. Show all posts
Tuesday, August 16, 2011
Wednesday, January 19, 2011
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
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
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.
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
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?...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.
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.
Friday, October 22, 2010
Comparative effectiveness 101
I have to say I do not understand the opposition to including costs into the comparative effectiveness research (CER) equation. Perhaps I simplify too much, but here is how I think about it.
Comparing two therapies is like using a microscope to focus at different levels of depth. Starting from the lowest magnification, we can ask "do they both work?" Of course in order to make this question answerable with data, we need to define what we mean by "work". Once we have defined that, the question becomes whether or not both comparators produce an effect in the same (desired) direction. If they do not, then the comparison can stop here, as the one with the positive direction of effect wins out. If they do both produce a desired effect, then we focus on the magnitude of that effect. There are several ways to do this, including comparison of 1). the effect size and variability of each to one another, 2). the proportion of patients who achieve a certain threshold of response (aka response rates), and 3). the adverse events frequency and severity. If these parameters are identical between the two, then the decision clearly hinges on the cost. Why is this so hard to accept? We would not want to pay more money for an identical car, so why would we pay more for a drug?
Of course, life is never quite this simple. Most of the time we will find small differences that are amplified by marketing messages as the reason to prefer a particular therapy. And this is all fine and good, and it is OK if a patient prefers one to the other because of some of these subtle differences. The question here becomes "how much are we willing to pay for a unit of this difference?" And unless the patient herself is willing to write the check, this is the pivotal issue facing our society today in the realm of the healthcare debate. So far our politicians have refused to do the real math, and are pandering to the opinion that we can pay for whatever we want in healthcare. This stance resonates with the public, terrified of the fictional death panels, and, more importantly, with the business of medicine, as this approach provides ample fuel for this engine of economic growth. But does it really improve our health, the primary goal of healthcare? I think not, just look at the state of our health and compare it to the rest of the world. And secondly, what is it doing to our national budget?
I will concede that I have simplified a complex issue. But not that much, believe it or not. With a modicum of math literacy people can easily wrap their brains around these concepts and make their own decisions, rather than being manipulated by disingenuous obfuscations of politicians concerned more with stuffing their coffers with corporate money than looking out for the well-being of the nation.
Comparing two therapies is like using a microscope to focus at different levels of depth. Starting from the lowest magnification, we can ask "do they both work?" Of course in order to make this question answerable with data, we need to define what we mean by "work". Once we have defined that, the question becomes whether or not both comparators produce an effect in the same (desired) direction. If they do not, then the comparison can stop here, as the one with the positive direction of effect wins out. If they do both produce a desired effect, then we focus on the magnitude of that effect. There are several ways to do this, including comparison of 1). the effect size and variability of each to one another, 2). the proportion of patients who achieve a certain threshold of response (aka response rates), and 3). the adverse events frequency and severity. If these parameters are identical between the two, then the decision clearly hinges on the cost. Why is this so hard to accept? We would not want to pay more money for an identical car, so why would we pay more for a drug?
Of course, life is never quite this simple. Most of the time we will find small differences that are amplified by marketing messages as the reason to prefer a particular therapy. And this is all fine and good, and it is OK if a patient prefers one to the other because of some of these subtle differences. The question here becomes "how much are we willing to pay for a unit of this difference?" And unless the patient herself is willing to write the check, this is the pivotal issue facing our society today in the realm of the healthcare debate. So far our politicians have refused to do the real math, and are pandering to the opinion that we can pay for whatever we want in healthcare. This stance resonates with the public, terrified of the fictional death panels, and, more importantly, with the business of medicine, as this approach provides ample fuel for this engine of economic growth. But does it really improve our health, the primary goal of healthcare? I think not, just look at the state of our health and compare it to the rest of the world. And secondly, what is it doing to our national budget?
I will concede that I have simplified a complex issue. But not that much, believe it or not. With a modicum of math literacy people can easily wrap their brains around these concepts and make their own decisions, rather than being manipulated by disingenuous obfuscations of politicians concerned more with stuffing their coffers with corporate money than looking out for the well-being of the nation.
Monday, September 27, 2010
Does disproving a statistical null automatically render the clinical null disproved?
A good friend of mine lost her mother to pancreatic cancer recently. The whole process from diagnosis to her death took 6 weeks. And despite wonderful care from a palliative medicine team, the process proved grueling to her family. And no wonder: how do you assimilate a loved one's going from healthy to dead in six weeks? Of course, my friend's family made all the right choices, forgoing aggressive treatment in favor of maximizing their mother's comfort and quality of life. Their experience made me think of the new generation of cancer treatments, experienced by my father in his dying days, and how it all fits in the healthcare debate.
Tarceva, or erlotinib, is a kinase inhibitor manufactured by Genentech, indicated for the treatment of some cases of non-small cell lung carcinoma, and most recently approved by the FDA for advanced pancreatic cancer. Reading the package insert, it becomes clear that the FDA-approved 100 mg dose of this drug, if given in combination with gemcitabine, prolongs median survival by <2 weeks, from 6 months in gemcitabine+placebo arm to 6.4 months in the gemcitabine+Tarceva arm, for a p=0.028. This difference imparts statistical significance at the conventionally set p<0.05, and therefore renders Tarceva better than placebo. Period.
Delving a tad more deeply into the peer-reviewed publication of the phase 3 trial, one gleans a few other facts. I quote:
Survival and ResponseThe final analysis was conducted after 486 deaths (239 on erlotinib and gemcitabine and 247 on placebo and gemcitabine). Overall survival was significantly longer in the erlotinib and gemcitabine arm with an estimated HR of 0.82 (95% CI, 0.69 to 0.99; P = .038; log-rank test stratified for performance status, extent of disease, and pain score at baseline; Fig 1A). Median survival times were 6.24 months versus 5.91 months for the erlotinib and gemcitabine versus placebo and gemcitabine groups with 1-year survival rates of 23% (95% CI, 18% to 28%) and 17% (95% CI, 12% to 21%), respectively (P = .023). A multivariate Cox regression analysis showed that erlotinib treatment (HR, 0.82; 95% CI, 0.69 to 0.99; P = .04) and female sex (P = .03) were significantly associated with longer overall survival. While there was an imbalance in male:female ratio between the arms, the treatment effect remains significant when adjusted for sex.
Results of subgroup analyses of survival by baseline stratification factors and other factors such as sex, race, pain intensity score, and age are displayed in Figure 2.
Progression-free survival was significantly longer in the erlotinib and gemcitabine arm than the placebo and gemcitabine arm with an estimated HR of 0.77 (95% CI, 0.64 to 0.92; P = .004; log-rank test stratified for performance status, extent of disease, and pain score at baseline; median, 3.75 months v 3.55 months; Fig 1B).So, what do we have overall? We have a hazard ratio of dying that very nearly crosses 1.0, thus coming perilously close to not disproving the null; we have a prolongation of median survival by 1/3 of a month, and a progression-free median survival prolongation by 1/5 of a month.
But given my fondness for Gould's "The Median is not the Message" essay, let's practice full disclosure and look at the tail of the Kaplan-Meyer curves above. As the text points out,
...1-year survival rates of 23% (95% CI, 18% to 28%) and 17% (95% CI, 12% to 21%), respectively (P = .023).Indeed, these are significant differences, both statistically and clinically. Within the trial this represents the difference in favor of survival for 18 additional patients, thus rendering the cost of roughly $1.5 million for 1 year of life saved by my back-of-the envelope calculation. Of course, this difference is not adjusted for confounders, so it is difficult to say of the number is real of under- or over-estimated. Because the adjusted analysis is given as a hazard ratio of death, I cannot calculate the corresponding adjusted cost.
So, for me this begs the following question (and I would love to hear the thoughts from my colleagues who proudly proclaim being science-based and thus eschewing placebo effect as a valid way to get a therapeutic response): Is what we are seeing here real or is this in fact equivalent to a placebo effect? Is the median survival prolongation of <2 weeks indeed a real effect that means something to the patient and the clinician, or is it just clinical noise, if you will? In other words, should "disproving" the statistical null by default disprove the clinical null? Or does the bar for disproving the clinical null need to be set just a tad higher than a statistically significant increase in life expectancy of 2 weeks?
Obviously, no one knows a priori who will do better and who will not. So, without a crystal ball, it is one's values and preferences that have to drive these decisions. But does the society have a say in any of this, as we struggle with equitable distribution of a limited resource? Is $1.5 million for 1 year of life a good societal investment? And what is the quality of this life? And given that at least 1/2 of all treated patients get far less than extra 2 weeks of life, how do we strike the balance between a reasonable expectation of a response and a false hope?
My friend's family based their choices on their mother's wishes and their values and utilities for her comfort. The choice they made was very different from that made by my parents with regard to my father's palliation. Both were right for the respective families. But one may have been far too costly, both financially and emotionally.
Tuesday, December 8, 2009
An executive articulates the value of his drug
Allos Therapeutics is a small biopharmaceutical company located in Westminster, Colorado, with a single agent on the market. Pralatrexate, brand name Folotyn, is a small molecule therapy for a rare and aggressive hematologic malignancy peripheral T-cell lymphoma. The recently FDA-approved drug is stirring controversy by, you guessed it, pricing itself out of the market. The pricing giants at the company decided that a fair price for the drug, achieving parity with other compounds in the space and helping them recoup their investment, would be $30,000 per month.
In the area of cancer, such a price tag is certainly nothing unusual. Here, fancy and expensive-to-produce biologic therapies can run as high as $100,000 annually to treat such common cancers as those of the lung, breast and colon, even while only prolonging the patient's life by an average of 2 months. But here is the kicker: pralatrexate is not a biologic, but a small molecule, and not even first in class! So, essentially, it is a me-too drug that is not particularly expensive to manufacture.
But let's give the company the benefit of the doubt -- after all, clinical development, especially in a rare cancer, is prolonged, costly and generally resource-intensive. A few more pieces of the puzzle are in order before we can make the final judgment. The drug was approved based on a trial of 115 patients with recurrent PTCL refractory to, on average, 3 prior therapies. The outcome evaluated was a combined endpoint of complete response or complete response unconfirmed or partial response (each indicating degrees of tumor shrinkage). Among the 111 evaluable patients the response rate was 27%, and the median duration of response was 9 months (meaning that one-half of the 29 responders progressed by 9 months). And the median duration of use of the product in the trial was 70 days.
So, let's do the math here: if 100 patients are prescribed this drug for 70 days (this is being conservative, as the median is usually lower than the mean value in similar distributions) at a cost of $30K per month, we have spent $7,000,000 to get a response in 27 patients that lasts under 1 year, or about $333,000 per year of life saved. So, this may be less reasonable in some books than others. Hmmm...
Well, in case you you have any shred of doubt remaining, look at what James Caruso, the Chief Commercial Officer for the company, is quoted as saying:
In the area of cancer, such a price tag is certainly nothing unusual. Here, fancy and expensive-to-produce biologic therapies can run as high as $100,000 annually to treat such common cancers as those of the lung, breast and colon, even while only prolonging the patient's life by an average of 2 months. But here is the kicker: pralatrexate is not a biologic, but a small molecule, and not even first in class! So, essentially, it is a me-too drug that is not particularly expensive to manufacture.
But let's give the company the benefit of the doubt -- after all, clinical development, especially in a rare cancer, is prolonged, costly and generally resource-intensive. A few more pieces of the puzzle are in order before we can make the final judgment. The drug was approved based on a trial of 115 patients with recurrent PTCL refractory to, on average, 3 prior therapies. The outcome evaluated was a combined endpoint of complete response or complete response unconfirmed or partial response (each indicating degrees of tumor shrinkage). Among the 111 evaluable patients the response rate was 27%, and the median duration of response was 9 months (meaning that one-half of the 29 responders progressed by 9 months). And the median duration of use of the product in the trial was 70 days.
So, let's do the math here: if 100 patients are prescribed this drug for 70 days (this is being conservative, as the median is usually lower than the mean value in similar distributions) at a cost of $30K per month, we have spent $7,000,000 to get a response in 27 patients that lasts under 1 year, or about $333,000 per year of life saved. So, this may be less reasonable in some books than others. Hmmm...
Well, in case you you have any shred of doubt remaining, look at what James Caruso, the Chief Commercial Officer for the company, is quoted as saying:
Patients, moreover, are likely to use the drug for only a couple of months because the tumor worsens so quickly, he said. So the total cost of using Folotyn will be less than for many other drugs with lower monthly prices.So, the message is that our expensive drug should be used not because it saves lives, not because it improves quality of life, but because the expense will be limited by the its uselessness? Isn't is a little bit like saying that a gas-guzzling HUM-V is worth the expense because it will break down in two months anyway? I have to admire the manufacturer in speaking the truth this way: if more manufacturers do this, comparative effectiveness concept will become obsolete before it is even legislatively approved.
Tuesday, September 8, 2009
Healthcare reform: so much more than costs
The opposition to healthcare reform is fanning fears of rationing as a way of controlling escalating costs and broadening access. A better use of our collective energies would be to identify and eliminate the waste and harm produced by the current healthcare system. We are well aware of over 1 million hospitalizations and the nearly 100,000 deaths associated with hospital-acquired infections, at a cost of approximately $40 billion annually. We have this information partly because the data infrastructure within hospitals exists to track these events. Many more potential adverse consequences of healthcare remain unrecognized and unquantified, particularly those that occur outside of hospitals, where the majority of healthcare takes place. We hear distant echoes of alarms sounded by researchers, but because there is no integrated picture of the full magnitude of the harm, we remain complacent. We must admit that harm exists, develop systems to track and quantify it, and finally eliminate it. The time for this is now; here is why.
Why would harm be pervasive in what has been called by some “the best healthcare system in the world”? Theoretically, risk aversion, by promoting a culture of over-testing, over-diagnosis and over-treatment, is fraught with paradoxically noxious potential. Beyond theory, an example of how the “better-safe-than-sorry” approach can be detrimental plays out in screening mammography. Despite the fact that the US Preventive Services Task Force says to commence breast cancer screening at the age of 40, the risk and benefit balance of this recommendation is unclear. A recent study from Europe, for example, noted that for every 2,000 women screened over a 10-year period, one cancer death is averted. The trade-off is that 2-10 other women receive invasive and toxic treatments for a cancer that would not have become life-threatening. What is even more disturbing is that we do not know how many of these 10 women are consigned to life-long illnesses or an early death due to the complications of this unnecessary treatment. Similar concerns exist for prostate cancer screening and for many other diagnostic and therapeutic modalities we employ every day: the drug and device approval process focuses more on short-term benefits and less on long-term risks. Therefore, the full extent of the harm remains unknown.
Until recently, the heretical talk of harmful effects of healthcare was relegated to the fringes of the medical world. Fortunately, this conversation has now penetrated into the mainstream, with a paper in a recent issue of The New England Journal of Medicine laying out the research framework to explore this problem. Unfortunately, the research agenda, which still requires much clarification and planning, though harnessing the will to fill this knowledge gap, will be hard-pressed to find a way. This is because of the heterogeneous and still mostly paper-based medical record-keeping by the majority of US practitioners, precluding meaningful recognition and aggregation of relevant data.
This lack of knowledge gives the public a rosy view of healthcare: high benefits with virtually no harms. Getting a handle on the full picture of the balance of benefits with harms and waste, however, is critical for optimizing outcomes. Although data to quantify it are presently scant, the timing of the emergence of this research agenda, coincident with the major push to adopt electronic health data platforms along with the recent allocation of funding to the comparative effectiveness research (CER), is fortuitous. Electronic data, if captured accurately and uniformly, are a robust source of complete information on long-term outcomes, and the well-established framework for CER is a natural fit for incorporating these data to inform policy decisions.
If healthcare reform is to achieve its goals of universal access to good quality healthcare at a reasonable price, we must not miss this opportunity to align the research into harm with our ability to generate useful data from electronic sources to feed smart policy decisions through CER. Although historically our healthcare system has enjoyed very little integrated planning, this is an opportunity to draw up a sensible blueprint for a successful future. If we do it right, we will be not only saving money, but also addressing such an important societal concern as human lives.
Thursday, August 13, 2009
On "10 Steps to Better Health Care" in NYT
Here is a comment I posted on the NYT site in response to Gawande, Berwick, Fisher and McClellan's article "10 Steps to Better Health Care". Thought it was worth posting here because it brings together many of the arguments that I have been making piece-meal in multiple posts.
The two central problems that are driving the situation are 1). the profit motive and 2). fear of malpractice. The profit motive for all involved in the system has created an incentive to diagnose more disease to treat with therapies developed based on the market size rather than on real needs, to perform more procedures and to build a giant bureaucracy ostensibly for the sake of checks and balances, but in reality to increase the bottom line. As for malpractice fears, because our society simplistically thinks that more care is better care, doing more is the default.
In my mind, the Mayo system works well precisely because the profit motive has been removed for those on the ground. Take the market out of healthcare, and you have a model for success. Unfortunately, single payer has very little chance of passing, as it is a political third rail in the US. And tort reform? Well, we know that no one wants to tackle that either, for so many reasons. So, best we can do is to come up with a way of defining and promoting value-based healthcare (think comparative effectiveness). Within that we really have to quantify the harms inherent in overuse and misuse, so that we can start changing this culture of more is better.
For more on these issues see the following posts on this blog: http://evimedgroup.blogspot.com/2009/08/putting-sticker-price-on-healthcare.html http://evimedgroup.blogspot.com/2009/07/convenient-failure.html http://evimedgroup.blogspot.com/2009/07/is-useless-harmful.html
Tuesday, August 4, 2009
Putting a sticker price on healthcare
Let's say you are in the market for a car. You have settled on a GM model (unlikely, I know), and now you are shopping around for the best price. There are 2 dealerships that are equidistant from your home, and they both have the model you want in the color and with all the accessories that your heart is set on. The only difference is that the dealer on your right is insisting on charging you $5,000 more than the one to your left. What do you do? Absurd, you say? OK, how about if the dealer to the right is throwing in a rust-proofing treatment that the dealer on the left is refusing to give you. Are you willing to spend an extra $5,000 on rust-proofing? Still absurd?
Well, when it comes to cars, these decisions are pretty straight-forward. Not so in healthcare, right? Healthcare is full of arcane and esoteric concepts that only experts can unravel, and the public has to be vigilant not to get duped into getting less than they deserve. The reality is that we have not asked for the same accountability from our healthcare "dealers" as we expect from car vendors. By trusting the experts to know what they are doing, and because of our love affair with new stuff, we have been willing to pay a Rolls Royce price for a used Oldsmobile. Just because a technology is new and touts itself as being better, it is not necessarily so. Would you buy a car only because of its manufacturer's claims of safety? Would you not want this confirmed by independent reviewers, such as Consumer Reports, before investing your trust and dollars?
Admittedly, healthcare is more complicated. But the real monkey wrench is not what you think it is, the complexity of each individual human organism's interaction with its world. Rather, the monkey wrench is the matrix of interconnectedness within the healthcare industry, running from the manufacturers to healthcare providers, to the Food and Drug Administration, to the Insurance companies. This complex web is based on money in the way of profits, reimbursements, salaries and executive bonuses, and it does not explicitly include patient interests. It is this very complexity that keeps us on guard.
So, into this swamp of special interests, the administration is introducing the concept of comparative effectiveness research (CER) for sensible policy development. The reality of CER is much less sinister that what the Republicans will have you believe: its intent is in essence to compare values of interventions aimed at the same outcome. Going back to the analogy of cars, the CER is meant to quantify explicitly how much the rust-proofing is worth, and to tell us whether $5,000 is too much to pay for it. The idea is that, once this comparative value has been determined, we can start making our healthcare decisions more rationally than we are forced to do today.
Americans have a legacy of being sensible consumers. While the administration is proposing demystifying the value proposition of healthcare decisions, the Republicans are promoting further obfuscation. They do not want us to be able to make value-based choices in healthcare, and we absolutely must ask why. It is that simple: the choice is not between whether grandma lives or dies (after all, there is still 100% risk of death for all of us eventually), the choice is essentially between giving away the $5,000 to the car dealer for a useless rust-proofing or whether we can use that money for something of real value to ourselves and our community.
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