Evidence Based Medicine
[This post is the third and final part of a commentary on “Medicine in Denial,”(2011) by Dr. Lawrence Weed and Lincoln Weed. You can read Part 1 here and Part 2 here.]
It seems that Dr. Larry Weed is commonly referred to as the father of the SOAP note and of the problem list.
Having read his book, I’d say he should also be known as the father of orderly patient-centered care, and I’d encourage all those interested in patient empowerment and personalized care to learn more about his ideas. (Digital health enthusiasts, this means you too.)
Skeptical of this paternity claim? Consider this:
“The patient must have a copy of his own record. He must be involved with organizing and recording the variables so that the course of his own data on disease and treatment will slowly reveal to him what the best care for him should be.”
“Our job is to give the patient the tools and responsibility to organize the knowledge and slowly learn to integrate it. This can be done with modern guidance tools.”
These quotes of Dr. Weed’s were published in 1975, in a book titled “Your Health Care and How to Manage It.” The introduction to this older book is conveniently included as an appendix within “Medicine in Denial.” I highlighted it this section intensely, astounded at how forward-thinking and pragmatically patient-centered Dr. Weed’s ideas were back in 1975.
Thirty-eight years ago, Dr. Weed was encouraging patients to self-track and to participate in identifying the best course of medical management for themselves. Plus he thought they should have access to their records.
Continue reading “Medicine in Denial: What Larry Weed Can Teach Us About Patient Empowerment”
Filed Under: Physicians
Tagged: Diagnosis, Disease Management, e-patients, Evidence Based Medicine, Larry Weed, Lawrence Weed, Leslie Kernisan, Medicine in Denial, patient engagement, Physicians, SOAP
May 22, 2013
A useful and well-written summary of open access to publications in the medical field triggered some thoughts I’d like to share. The thrust of the article was that doctors need more access to a wide range of journal publications in order to make better decisions. The article also praises NIH’s open access policy, which has inspired the NSF and many journals.
My additional points are:
- Open publication adds to the flood of information already available to most doctors, placing a burden on them to search and filter it. IBM’s Watson is one famous attempt to approach the ideal where the doctor would be presented right at the point of care with exactly the information he or she needs to make a better decision. Elsewhere, I have reported on a proposal to help experts doctors filter and select the important information and provide it to their peers upon demand–a social networking approach to evidence-based medicine.
- Not only published papers, but the data that led to those research results should be published online, to help researchers reproduce the results and build on them to make new discoveries. I report in an earlier article on this site about the work of Sage Bionetworks to get researchers to open their data. Of course, putting up raw data leaves many challenges: one has to be careful to deidentify it according to accepted standards. One has to explain the provenance of the data carefully: how it was collected and massaged (because data sets always require some culling and error-correction) so it can be understood and properly reused. Finally, combining different data sets is always difficult because they are collected under different conditions and with different assumptions.
Filed Under: THCB
Tagged: Andy Oram, Data, Evidence Based Medicine, NIH, NSF, open access
May 7, 2013
We are asking doctors to help us study what access to all medical research would mean for their practice. To study the value of such access, we are providing physicians who participate in this Stanford University Public Access Study with eleven (11) months of complete access to virtually all medical journals, as well as to an evidence-based clinical decision-support service.
Participating physicians will have free, one-click access to this vast body of research on their computer or tablet, whenever and wherever they are online. The study is intended to inform current discussions and legislation on the state of public and professional access to federally funded medical research.
Demands on Participant: Participants must be a physician licensed to practice in the United States. Data will be collected on participants’ use of research, with selected participants asked to participate in a 30-minute confidential interview. As a control measure, participants are given an extra month of the evidence-based clinical decision-support service, either prior or following the eleven months of access to the research literature.
To learn more and/or to begin immediate participation (after providing informed consent) in the Public Access Study, follow this link: http://nihpublic.stanford.edu/.
The principal investigator of the Public Access Study is John Willinsky, Khosla Family Professor, Stanford University, Stanford CA; email@example.com.
Filed Under: THCB
Tagged: Clinical Decision Support, Clinical Trials, Evidence Based Medicine, John Willinsky, Physicians, Public Access Study, Stanford School of Medicine
Apr 7, 2013
Sir Muir Gray, of evidence-based medicine fame, is a man who speaks his mind – often in 140 characters or fewer. “Show me a paper by a management academic,” he Tweeted, “that has changed the way we deliver health services” [and, implicitly, improved patient outcomes].
Part of me agreed with him, but I’m married to a management academic (“Oops sorry, better man than me,” Muir backpedalled), who helped me rise to Muir’s challenge.
We kicked off with a paper almost every clinician has heard of:
Kaplan and Norton’s ‘balanced scorecard’, published in Harvard Business Review in 1992 and cited over 8000 times since . The scorecard was aimed at company directors who wanted some quick (and, one is tempted to suggest, dirty) metrics to monitor what their customers thought of them and where they should direct their efforts for the future. It has certainly changed practice (many healthcare organisations use it), but we were not overly sold on its transferability to the healthcare setting.
Continue reading “Have Management Papers Ever Changed Practice in Healthcare?”
Filed Under: THCB
Tagged: balanced scorecard, Evidence Based Medicine, management papers, Outcomes, Sir Muir Gray, Social Media, Trish Greenhalgh, Twitter
May 25, 2012
This week, The New York Times gave heart-wrenching accounts of newborn babies enduring opiate drug withdrawal because of their mothers’ addictions. The story provided only one cause for optimism: Both babies and their painkiller-dependent mothers can benefit dramatically from being maintained on medications such as methadone or buprenorphine.
Unless, of course, these mothers were members of a military family, in which case such essential, life-saving care would be denied to them.
The most effective treatment for opiate addiction — long-term buprenorphine or methadone maintenance — is not covered by the Department of Defense’s TRICARE insurance program. The program limits methadone and buprenorphine prescriptions to short-term detoxification, and its regulations state, “Drug maintenance programs when one addictive drug is substituted for another on a maintenance basis (such as methadone substituting for heroin) are not covered.” The premise that prescribing opiate substitutes is no different from uncontrolled opiate abuse goes back to the anti-methadone hysteria of the 1970s. Since then, opiate-substitution treatment has become a staple of modern addiction medicine, particularly with the addition of buprenorphine in 2002. Unlike methadone, burenorphine can be prescribed for maintenance by patients’ regular primary physicians, outside traditional venues of addiction treatment, which had long posed forbidding barriers for many patients.
In fact, many of the best clinical trials of methadone and buprenorphine were conducted in Veterans Health Administration studies with former military personnel as patients. The treatment is so established that in 1997, the National Institutes of Health called for an end to the unnecessary regulation of these medications and for these medications to be included in public and private insurance coverage. These treatments are now standard within the addiction field, are FDA-approved and have been used to treat opiate dependence disorders for several decades. Long-term methadone and buprenorphine maintenance are now available to patients through Medicaid, through many state-funded programs, and, increasingly, through private insurance. Continue reading “Bad Medicine: TriCare’s Noncoverage of Evidence-based Opiate Maintenance Therapy”
Filed Under: THCB
Tagged: Evidence Based Medicine, Harold Pollack, Keith Humphreys, TRICARE
Apr 15, 2011
One of the founders of the evidence-based medicine movement, Muir Gray
Fascinating, how in the same week two giants of evidence-based medicine have given such divergent views on the future of quality improvement. Here (free subscription required), Donald Berwick, the CMS administrator and founder and former head of the Institute for Healthcare Improvement, emphasizes the need for quality as the strategy for success in our healthcare system. But here, one of the fathers of EBM, Muir Gray, states that quality is so 20th century, and we need instead to shine the light on value. So, who is right?
Well, let’s define the terms. The Merriam-Webster dictionary defines quality as “the degree of excellence.” The same source tells us that value is “a fair return or equivalent in goods, services or money for something exchanged.” To me “value” is a holistic measure of cost for quality, painting a fuller picture of the investment vis-a-vis the returns on this investment. What do I mean by that?
Simply put, the idea behind value is to establish what is a reasonable amount to pay for a unit of quality. Let’s take my used 1999 VW Passat as an example. If my mechanic tells me that it needs to have some hoses replaced, and it will cost me under $100, and the car will run perfectly, I will consider that to be a good value. However, if my transmission has fallen out in the middle of Brookline Ave. in Boston (really happened to me once, many years ago and with a different car), and it will cost me $5,000 to fix, I may say that the value proposition is just not there, particularly given that the car itself is worth much less than $5,000. Given that my budget is not unlimited, I have to make trade-off decisions about where to put my money, so I may instead spend the money on another used Passat that has good prospects.
But in medicine, we routinely avoid thinking about value. There seems to be an overall impression that if it out there on the market, and especially if it is new, it is good and I am worth all of it. This impression is further enabled by the fact that CMS has no statutory power to make decisions based on value of interventions — they are legislatively mandated to turn a blind eye to the costs. Does this make sense? How toothless is our comparative effectiveness effort likely to be if it has to ignore half of the story? Continue reading “Quality or value? A Measure for the 21st Century”
Filed Under: THCB, The DC
Tagged: Evidence Based Medicine, Marya Zilberberg, Quality, Value
Mar 11, 2011
We entered the 21st century awash in “evidence” and determined to anchor the practice of medicine on the evidentiary basis for benefit. There is the sense of triumph; in one generation we had displaced the dominance of theory, conviction and hubris at the bedside. The task now is to make certain that evidence serves no agenda other than the best interests of the patient.
Evidence-based medicine is the conscientious and judicious use of current best evidence from clinical care research in the management of individual patients”. [1,2]
But, what does “judicious” mean? What does “current best” mean? If the evidence is tenuous, should it hold sway because it is currently the best we have? Or should we consider the result “negative” pending a more robust demonstration of benefit? Ambiguity is intolerable when defining evidence because of the propensity of people to decide to do something rather than nothing.  Can we and our patients make “informed” medical decisions on thin evidentiary ice? How thin? Does tenuous evidence mean that no one is benefited or that the occasional individual may be benefited or that many may be benefited but only a very little bit? Continue reading “The Evidentiary Basis for a Clinically Meaningful Benefit”
Filed Under: Uncategorized
Tagged: Clinical medicine, Clinical Trials, Comparative Effectiveness Research, Evidence Based Medicine, Nortin Hadler, Robert McNutt
Jun 1, 2010
The Massachusetts Massacre has everyone stepping back a bit. The President says that we should “coalesce around those elements of the package that people agree on,” but it is unclear just which elements those might be, given the extreme polarization that has defined the debate. He suggests that points of agreement might center on insurance reform and cost containment, which are both important goals. I’m skeptical that a sudden flowering of bipartisanship will allow such agreement, however. Ezra Klein, on the other hand, has a paring proposal that goes in another direction, and reminds us of why we got into this in the first place: to extend coverage to the uninsured. If we must narrow our focus, Klein says we should extend Medicare to those over 50, and expand Medicaid to those under 200% of poverty. This would get lots of people insured, and could well be accomplished through budget reconciliation if no Congressional coalescing is to be had.
However the parsing, paring, and palavering goes, cost control is and will be at or near the health reform debate for years to come. Two recent articles are worth a look for those interested in analysis of cost-containment strategies.
Continue reading “Cost, Choice, and Value”
Filed Under: OP-ED
Tagged: Costs, Evidence Based Medicine, John Jacobi
Jan 29, 2010
By NORTIN HADLER, MD
Last summer President Obama signed the American Recovery and Reinvestment Act into law. Tucked into the legislation was $1.1 billion to support comparative effectiveness research (CER). The legislation charged the Institute of Medicine with defining CER. Its Committee on Comparative Effectiveness Research Prioritization rapidly came up with,
…the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat and monitor a clinical condition, or to improve the delivery of care. The purpose of CER is to assist consumers, clinicians, purchasers, and policy makers to make informed decisions that will improve health care at both the individual and population levels.
The Committee then elicited over 2500 opinions from 1500 stakeholders and produced a list of the 100 highest-ranked topics for CER (www.iom.edu/cerpriorities). Proposals to undertake CER are pouring forth from investigators across the land. There is no doubt that an enormous amount of data will be generated by 2015. But there is every reason to doubt whether many inferences can be teased out of these data that will actually advantage patients, consumers, or the health of the nation.
I am no Luddite. For me “evidence based medicine” is not a shibboleth; it’s an axiom. Furthermore, having trained as a physical biochemist, I am comfortable with the most rigorous of the quantitative sciences let alone biostatistics. However, you can’t compare treatments for effectiveness unless you are quite certain that one of the comparators is truly efficacious. There must be a group of patients for whom one treatment has unequivocal and important efficacy. Otherwise, the comparison might discern differences in relative ineffectiveness.
The academic epidemiologists who spearheaded the CER agenda are aware of the analytic challenges but are convinced these can be overcome. I would argue that CER can never succeed as the primary mechanism to assure the provision of rational health care. It has a role as a secondary mechanism, a surveillance method to fine tune the provision of rational health care, once such is established.
The difference between efficacy and effectiveness
My assertion may seem counter-intuitive. After all, we hear every day about pharmaceuticals that are licensed by the FDA because of a science that supports the assertion of benefit. In epidemiology-speak, the science that the FDA reviews does not speak to the effectiveness of the drug, but to its efficacy. The science of efficacy tests the hypothesis that a particular drug or other intervention works in a particular group of similar patients. CER asks whether an intervention works better than other interventions in practice where the patients and the doctors are heterogeneous. The rational for the CER movement is the perceived limitations of efficacy research. I argue that the limitations of efficacy research are much more readily overcome than the limitations on CER.
The gold standard of efficacy research is the randomized controlled trial (RCT). In a RCT, patients with a particular disease are randomly assigned to receive either a study intervention or a comparator (often a placebo). After a pre-determined interval, the previously defined clinical outcome is compared in the active and control limbs of the trial. If there is no difference, one can argue that the intervention offers no demonstrable clinical benefit to patients such as those in the study. If there is a difference, the contrary argument is tenable.
This elegant approach to establishing clinical utility has its roots in antiquity, at least as far back as Avicenna. The modern era commences after World War II and escalates dramatically after 1962 when the Kefauver-Harris Amendment to the laws regulating the US Food and Drug Administration mandated demonstration of efficacy before pharmaceuticals could be licensed. Modern biostatistics has probed every nuance of the RCT paradigm. The result is a highly sophisticated understanding of the limitations of the RCT, an understanding that has fueled the call for CER:
- The more homogeneous the study population, the more likely any efficacy will be demonstrated and the more compelling any assertion as to its lacking. However, the homogeneity compromises the ability to assume the result generalizes to different kinds of patients.
- Many important clinical outcomes are either infrequent or occur late in the course of disease. It is difficult to maintain and fund RCTs that require years or decades before one can hope to see a difference between the active and control limbs. The compromise is to study “surrogate” outcomes, measures that in theory reflect the disease process, but are not themselves clinically important outcomes. Thus we have thousands of studies of blood pressure, cholesterol, blood sugar, PSA and the like but comparatively few studies that use heart attacks, death from prostate cancer, or other untoward clinical outcomes as the end-point.
- How big a difference between the active and control limbs is important? Biostatistics has dictated that we should pay attention to any difference that is unlikely to happen by chance too often. “Too often” traditionally is considered no more than 5% of the time, but that’s a matter risk-taking philosophy. What are we to make of a difference that is clinically very small, even if it is unlikely to happen by chance more than 5% of the time? Is it possible that the small effect will be important, perhaps less small, when the constraints of homogeneity are removed in practice? In practice, drugs licensed for one disease are even tried for other “off label” indications where effectiveness may emerge.
- The corollary limitation relates to the negative trial. If there is no demonstrable difference, does that mean that there is no effect? Or could the effect have been too small to detect because of the duration of the trial or the size or homogeneity of the population studied? Even a very small effect, advantaging only the occasional patient, can translate into many benefited people when tens of thousands are treated.
- Devices and surgical procedures are used practice; rigorous testing as to efficacy is not a statutory requirement. Maybe in the “real world” a treatment that was never studied or studied in a limited fashion turns out to really advantage patients in practice, or advantage some patients – or not.
CER to the rescue?
The methodology employed for CER is not the RCT. CER is an exercise in “observational research”. CER examines real world data sets to deduce benefit or lack thereof. This entails the development of large-scale, clinical and administrative networks to provide the observational data. Then biostatistics must come to grips with issues that make defining the heterogeneity of populations recruited into RCTs seem trivial. In the RCT, the volunteers can be examined and questioned individually and in detail and the criteria for admission into the trial defined a priori. Nothing about the validity of diagnosis, clinical course, interventions, coincident diseases, personal characteristics or outcomes can be assumed in observational data sets. There must be efforts at validating all such crucial variables. No matter how compulsively this is done, CER demands judgments about the importance of each of these variables. It is argued that some of these limitations are overcome because CER is not attempting to ask whether a particular intervention works in practice, but whether it works better than another option also in practice. It is even suggested that encouraging or introducing particular interventions or practice styles into some practice communities and not others would facilitate CER. Perhaps.
The object lesson of interventional cardiology
Interventional cardiology for coronary artery disease is the engine of the American “health care” enterprise. Angioplasties, stents of various kinds, and coronary artery bypass grafting (CABG) have attained “entitlement” status. There are thousands of RCTs comparing one with another, generally leading to much ado about very small differences, usually in surrogate measures such as costliness or patency of the stent. But there are very few RCTs comparing the invasive intervention with non-invasive best medical care of the day: 3 for CABG and 4 for angioplasty with or without stenting. In these large and largely elegant RCTs, the likelihood of death or a heart attack if treated invasively is no different from the likelihood if treated medically. Whether anyone might be spared some degree of chest pain by submitting to an invasive treatment is arguable since the results are neither compelling nor consistent. Yet, interventional cardiology remains the engine of the American “health care” enterprise. It carries on despite the RCTs because its advocates launch such arguments as “We do it differently” or “The RCTs were keenly focused on particular populations of patients and we reserve these interventions for others we deem appropriate.” These arguments walk a fine line between hubris and quackery.
So many invasive procedures are done to the coronary arteries of the young and the elderly that interventional cardiology has long lent itself to CER. We know from observational studies that that it does not seem to matter much if the heart attack patient has an invasive intervention quickly or it is delayed or not at all. We know from observational studies, and even trials rewarding some but not all hospitals for getting doctors to adhere to the “guidelines” for managing heart disease, that adherence does not make much of a difference. Do the results of this CER mean that we need to further improve the efficiency and quality of the performance of invasive treatments as many would argue? Or can we hope that more exacting CER can parse out some meaningful indication from large data sets, some compelling inference that only particular people with particular conditions are advantaged and therefore are the only candidates for interventional cardiology?
Or are we using the promise of CER to postpone calling a halt to the ineffective and inefficacious engine of American “health care”. The available science is consistent with the argument that interventional cardiology is not contributing to the health of the patient. I would argue that interventional cardiology should be halted until someone can demonstrate substantial efficacy and a meaningful benefit-to-risk ratio in some subset. Then CER can ask whether the benefit demonstrated in the efficacy trial translates to benefit in common practice.
Efficacy research is the horse; CER is the cart
Interventional cardiology for coronary artery disease is but one of many object lessons. There is much in common practice that has never been shown to be efficacious in any subset of patients. Some practices take up residence in the common sense despite having never been studied. Some practices, like interventional cardiology, persist because intellectual and fiscal interests are vested in the entrenchment despite the results of efficacy trials. CER can not inform efficacy, and CER can not inform effectiveness unless there is an example of efficacious therapy against which practices are compared. Otherwise, CER can be comparing degrees of ineffectiveness.
The way forward is to design efficacy trials that are more efficient in providing gold standards for comparison and as efficient in defining false starts that are not allowed into common practice until the approach is superseded by one of demonstrated efficacy. This is not all that difficult to do. Let’s return to the limitations of efficacy trials listed above:
- Homogeneity of study populations is not a limitation for the quest for a meaningful standard of efficacy. At least we will know the intervention is good for someone.
- Surrogate measures are useful to bolster the hypothesis that something might work. They have a dismal track record for testing the hypothesis that something does work. Clinically important outcomes must be invoked for such a test. If it is not feasible because the clinical outcome is too slow to develop or too infrequent, compromise is not an option. The intervention can not be studied at all, or it can not be studied until an appropriate subpopulation can be identified, or one must bite the bullet and undertake a lengthy RCT.
- Surrogate outcomes are not the only way that RCT results can lead to spurious clinical assumptions. “Composite outcomes” are even worse. RCTs in cardiology are notorious for an outcome such as “death from heart disease or heart attack or the need for another procedure.” When these studies are closely read, one learns that any difference detected is almost exclusively in “the need for another procedure” which is a highly subjective and interactive outcome that can speak to preconceptions on the part of the doctor or the patient rather than the efficacy of the intervention.
- Modern epidemiology is so wedded to the notion of statistical significance that concern about the statistical significance of “What?” is overwhelmed. “What?” is the clinical significance? Just because the difference observed between the active and control limbs of the RCT wouldn’t have happened by chance too often does not mean that the difference is clinically important even in the occasional patient. I’ll illustrate this by touching the Third Rail that the debate over the clinical utility of mammography has become. Malmö is a city in Sweden where women were invited to volunteer for a RCT; half would be offered routine screening mammography for a decade and the other half encouraged see their physicians whenever they had concern about the health of their breasts. That’s the difference between screening and diagnostic protocols; in screening one is agreeing to a test simply as a matter of course, in diagnostics one agrees to the testing in response to a clinical complaint. Back to the Malmö RCT. Over 40,000 women between age 40 and 60 volunteered for the RCT. Invasive cancer was detected in statistically significantly more women who were in the screened group than in the diagnostic group. Impressed? How about if I told you that 7 of 2000 women screened for a year were found to have invasive breast cancer and 5 of 2000 women in the diagnostic group for a year were found to have invasive breast cancer. Was all the screening worth this difference in absolute number of additional cancers detected? I could have told you that screening detected 40% more cancers but you won’t be swayed by the relative increase now that you know the absolute increase was 0.1%, will you? Would you consider the screening valuable if I told you that for every woman whose invasive breast cancer was treated so that they lived long enough to die from something else at a ripe old age, another two were treated unnecessarily since they died from something else before their breast cancer could be their reaper? How about all the false positive mammograms and false positive biopsies? There is a debate about mammography because it is a very marginal test that clearly is not doing as well as the common sense assumes.
- How small an effect can we detect in a RCT? Theoretically we can detect a very small effect. Theoretically we can detect an effect even smaller than the Malmö result. In order to do so, you need to randomize a large, homogeneous population whose size is determined by the level of statistical significance you choose and the nature of the health effect you seek. Death is the least equivocal outcome, for example. The quest for the small effect is the mantra of modern epidemiology. However, I consider such “small effectology” a sophism. No human population is homogeneous; we differ one from another in obvious, often measurable ways but also in less obvious, immeasurable ways. When we randomize individuals in any homogeneous population into a treatment group and a control group we assume that all the immeasurable differences randomize 50:50 or if not the randomization errors counterbalance. The smaller effect we are seeking, the more likely we are to be fooled by randomization errors that account for the difference rather than the treatment. That’s why so many small effects that emerge from RCTs do not reproduce.
Evidence Based Medicine can be more than a Shibboleth
The philosophical challenge in the design of efficacy trials relates to the notion of “clinically significant.” How high should we set the bar for the absolute difference in outcome between the treated and control groups in the RCT to be considered compelling? One way to get one’s mind around this question is to convert the absolute difference into a more intuitively appealing measure, the Number Needed to Treat (NNT). If the outcome is readily measured and unequivocal, such as death or stroke or heart attack, I would find the intervention valuable if I had to treat 20 patients to spare 1. Few students of efficacy would be persuaded if we had to treat more than 50 to spare 1. Between 20 and 50 delineates the communitarian ethic; smaller effects are ephemeral. For an outcome that is more difficult to measure than death or the like, an outcome that relates to symptoms or quality of life, I would argue for a more stringent bar.
If we applied this logic to RCTs, the trials would be far more efficient (in investigator/volunteer time, materiel, and cost) and the results far more reliable. If we applied this logic to RCTs, we would eliminate trials designed only to license agents no better than those already licensed (“me too” trials) and trials designed only for marketing purposes (“seed” trials). If we only licensed clinically efficacious interventions going forward, we could turn to CER to understand their effectiveness in practice. If we applied this logic retrospectively, to the trials that have already accumulated, we would soon realize how much of what is common practice is on the thinnest of evidentiary ice, how much has fallen through and how much supports an enterprise that is known to be inefficacious. It would take great transparency and political will to apply this razor retrospectively. We, the people, deserve no less.
Nortin M. Hadler, MD, MACP, FACR, FACOEM (AB Yale University, MD Harvard Medical School) trained at the Massachusetts General Hospital, the National Institutes of Health in Bethesda, and the Clinical Research Centre in London. He joined the faculty of the University of North Carolina in 1973 and was promoted to Professor of Medicine and Microbiology/Immunology in 1985. He serves as Attending Rheumatologist at the University of North Carolina Hospitals.
For 30 years he has been a student of “the illness of work incapacity”; over 200 papers and 12 books bear witness to this interest. He has lectured widely, garnered multiple awards, and served lengthy Visiting Professorships in England, France, Israel and Japan. He has been elected to membership in the American Society for Clinical Investigation and the National Academy of Social Insurance. He is a student of the approach taken by many nations to the challenges of applying disability and compensation insurance schemes to such predicaments as back pain and arm pain in the workplace. He has dissected the fashion in which medicine turns disputative and thereby iatrogenic in the process of disability determination, whether for back or arm pain or a more global illness narrative such as is labeled fibromyalgia. He is widely regarded for his critical assessment of the limitations of certainty regarding medical and surgical management of the regional musculoskeletal disorders. Furthermore, he has applied his critical razor to much that is considered contemporary medicine at its finest.
Filed Under: Uncategorized
Tagged: CER, Comparative Effectiveness Research, Effectiveness, Efficacy, Evidence Based Medicine, Nortin Hadler
Jan 11, 2010