Three Days Until The Ride!


Two months ago, I asked for your help and support in joining me in The Ride to Conquer Cancer, a two-day cycling journey through Pennsylvania to support Penn Medicine’s Abramson Cancer Center. It’s hard to believe, but the big day is now less than three days away.

A lot has happened during that time, and I’m incredibly grateful to have…

  • Met an amazing group of teammates, many with much more cycling experience than me who have shared their wisdom (and even some cycling gear).
  • Seen an outpouring of support from our classmates through our coffee sale and happy hour fundraisers.
  • Become more aware of the incredible work that the Abramson Cancer Center is pioneering, such as the recent FDA breakthrough therapy designation for personalized immunotherapy for acute lymphoblastic leukemia.
  • Been deeply touched by the generosity and camaraderie of another team that took us in and helped us reach our fundraising goal.

In three days, as we set off on our ride, I’ll be thinking of all of our supporters and the positive impact we’ll have made for cancer patients and their families. If you haven’t had a chance to check out our progress and would like to offer your support, please check out the link below:

Thanks again for your support, and we’ll see you on the road!

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How My PCP Alerted Me to the Potential for Abuse in Telehealth

This post was originally published on Project Millennial.

I recently called my primary care physician (PCP) for the first time in years to get my immunization records, and encountered a strange message saying he was not currently seeing patients. My mom had apparently encountered the same message weeks ago. “Maybe he retired,” she suggested.

I did a quick google search of my PCP’s name to find an alternate contact number, and instead found a shocking article from the local newspaper. Apparently my PCP has been indicted for falsifying tax returns and participating in an online pharmacy organization that provided prescription drugs without an in-person physician examination.

Remote Prescribing: Lucrative, Pervasive, and Very Illegal

I did a quick search online and confirmed that the practice of offering prescription drugs through a “cyber doctor” prescription, relying only on a questionnaire is indeed very illegal.

It is also very pervasive. The National Association of Boards of Pharmacy (NABP) reviewed 10,700 websites selling prescription drugs and found that 97% of them were “Not Recommended”. Of these, 88% do not require a valid prescription and 60% issue prescriptions per online consultation or questionnaire only.

What struck me was how this appeared to be a case where the market came together to produce a “triple win” for profit-seeking internet pharmacies, shady physicians (such as my own), and a subset of patients willing to pay a premium to access drugs (most commonly weight loss drugs, erectile dysfunction drugs, and commonly-abused antidepressants and painkillers).

According to one analysis, one such website offering prescriptions from its own doctors listed prices for fluoxetine (brand name Prozac) and alprazolam (brand name Xanax) that were roughly 400% to 1800% higher than prices from a more traditional Internet pharmacy not offering prescriptions. The fact that such “remote prescription” websites remain in business despite the huge price differential suggests that they are attracting patients willing to pay that premium to avoid seeing their regular doctor. And as for where that money is going—well, my doctor was alleged to have received roughly $2.5 million over six years.

Similar Incentives Could Exist for Telehealth Writ Large

Given the clear business case driving abuse in this model of “remote prescribing”, I wondered about the risks of overuse and abuse in the rapidly burgeoning field of telehealth more broadly. After all, one of the promises of telehealth is its ability to make the delivery of services more convenient for both patients and providers. A physician could vastly expand the number of patients he/she sees without leaving the office—which has been identified as a potent way to alleviate the physician shortage problem.

But that would only hold true if the proliferation of telehealth does not generate additional, potentially unnecessary demand. And substantial evidence points to the presence of physician-induced demand under a fee-for-service system. Currently, Medicare pays for a limited set of telehealth services under the same fee-for-service payment model used for in-person visits. Within Medicaid, while select states are experimenting with bundled or capitated payments that include telehealth, others are retaining their fee-for-service model.

In a testimony before the House Energy and Commerce Committee last month, Dr. Ateev Mehrotra, an expert on telehealth, noted, “To reduce health care costs, telehealth options must replace in-person visits.” I’m not convinced this is the case—especially when there is a clear financial incentive to provide more care.

“The very advantage of telehealth, its ability to make care convenient, is also potentially its Achilles’ heel. Telehealth may be ‘too convenient.’” — Ateev Mehrotra

In some cases, fee-for-service payments for telehealth may result in outright fraud, as my physician may have done. In others, it may simply encourage providers to err on the side of providing more care given uncertainties in a practice environment. In fact, a study led by Dr. Mehrotra found that PCPs were more likely to prescribe antibiotics during e-visits than in-person visits.

As various constituencies continue to debate the best approach for paying for telehealth, it is imperative for us to better understand how the incentives and convenience of telehealth interact to affect overall utilization. Blindly carrying our existing fee-for-service system into the new world of telehealth options may produce some unintended consequences.

Health care spending, Medicaid expansion, preventable deaths…

It’s been awhile since I’ve posted on this blog, but I wanted to keep any (remaining) readers updated with two posts I’ve published on separate blogs over the last two months. Links and descriptions are below:

  1. The ACA did not cause the slowdown in spending–but it may be contributing to the recent uptick (The Incidental Economist, April): After four years of historically low growth, health care spending is exhibiting an uptick again (a trend that has accelerated since this post was published in April). It appears the ACA’s value-based payment models are not kicking in quite yet. However, it may be contributing to the upsurge in spending–although not quite through the exchanges/Medicaid expansion as one might expect.
  2. Not having health insurance: a top cause of preventable death? (Daily Briefing Blog, May): Starting with the landmark Annals of Internal Medicine study that found insurance expansion significantly reduces risk of mortality, I look at how this would translate to the 15.1 million uninsured adults that could gain coverage if every state expanded Medicaid. The answer is concerning, especially in light of a recent CDC report on the top causes of potentially preventable deaths.

I hope to start writing again soon, so keep an eye out for new posts!

PCMHs Don’t Work–Or Do They? Insights From Two Recent Studies (Of the Same Program)

This post was originally published on Project Millennial.

A month ago, a JAMA study rocked the health wonk world by showing provocative evidence that Patient-Centered Medical Homes do not work. Evaluating 32 practices in the PA Chronic Care Initiative over a three-year period (2008-2011), the authors found that achieving NCQA PCMH recognition did not statistically reduce utilization or costs, and only improved one of 11 quality measures (nephropathy screening for diabetes). Aaron Carroll summarized the study and accompanying editorial over at The Incidental EconomistMainstream media and health wonk blogs alike declared the death of the “touted medical homes model”.

That’s why I was surprised to read this headline last week:

Study: Medical homes cut costs for chronically ill members

The punch line: these two studies evaluated the same PA pilot project over the same time period (albeit with different practices and patient populations).

Medical Homes Work—But Only for High-Risk Patients

A close read of the studies reveals that their conclusions are not incongruous. Indeed, the more recent AJMC study found no significant decrease in utilization or costs across all patients, just as the JAMA study did. However, when the authors limited their analysis to the top 10% highest risk patients (defined by DxCG risk scores), they found significant decreases in inpatient utilization in all three program years, and significant decreases in costs in the first two.

We can’t discern if the JAMA study would’ve found the same significant effects if they did a sub-analysis of the highest risk patients. (Interestingly, they state in the Methods section, “we repeated our utilization and cost models among only patients with diabetes,” but the results of that analysis are nowhere to be found.)

These results underscore an insight that’s becoming increasingly clear: cost savings from care management are concentrated in the highest risk individuals.

But we can go one step further.

Cost Savings Came ONLY From High-Risk Patients

Among the 654 high-risk patients, the PCMH produced adjusted savings of $107 PMPM in the first year. That roughly comes out to an estimated $69,978 in overall savings. Almost all of this (and then some) came from an estimated 40 avoided hospitalizations (654 patients x 61 adjusted avoided hospitalizations / 1000 patients).

Among 6940 patients overall, the PCMH produced (statistically insignificant) adjusted savings of $10 PMPM in the first year—an estimated $69,400 in overall savings. Across this entire patient group there were an estimated 41-42 avoided hospitalizations.

In other words, this study didn’t just find that savings are concentrated among high-risk patients. Essentially all of the cost savings and avoided hospitalizations came from the top 10% high-risk patient cohort.

This doesn’t mean that other PCMH models couldn’t squeeze savings out of lower risk patients. It just means that this and many existing models haven’t found out how to.

How to Achieve “Risk-Targeted Population Health”?

That finding raises a broader question that these studies can’t answer: What prevented the hospitalizations among the high-risk patients, and more importantly, were those key interventions limited to only the high-risk patients?

For example, were the crucial interventions ones that were only used for high-risk patients, such as a dedicated care manager, targeted outreach messages, and special appointments for high-risk patients?

Or were they interventions that were indiscriminately used on all patients, such as standard patient education or practice-level infrastructure that all patients enjoyed (even if only high-risk patients “benefited” in terms of reduced hospitalizations)?

This question is important because all interventions (and the infrastructure to support them) have a cost. Developing patient registries, expanding EHR capabilities, maintaining after-hours access, and investing in new training all represent substantial financial investments. Less than $70,000 in savings among high-risk patients—while extremely meaningful and significant—would be wiped out by the $20,000 “practice support” and average $92,000 bonuses paid out to each PCP by the medical home program.

If all of the benefits and savings are coming from the high-risk patients, we need to devise ways to concentrate our costs as well. Implementing such “risk-targeted population health” may be the only way to make the financials work.

Some practices are trying to do this by using dedicated care managers for high-risk patients within their existing patient panels. Others are trying to create separate clinics entirely dedicated to high-risk patients—which would allow them to limit fixed costs to high-risk patients as well. In fact, the NHS in England announced this January they are piloting this latter approach, creating “complex care practices” of 400-500 high-risk patients drawn from surrounding practices.

Whichever approach proves most effective, one thing is clear from these two studies: we need to rethink our current PCMH model.

New Year’s Resolution? Let’s Make it about Cost

This post was first published on Project Millennial.

2013 has been the year of the (botched) insurance expansion. But if the experience of other countries is any lesson, we should hope for political attention in 2014 to be devoted to another looming issue.

Over dinner with a few panelists at the Lown Conference, I learned about their involvement with the World Bank’s Universal Health Coverage (UNICO) Study Series, a comparative analysis of efforts to achieve universal coverage in 22 countries and Massachusetts (“the People’s Republic of Massachusetts”, as one panelist fondly called it).

And Then Came the Cost Issue

Befittingly, this People’s Republic was recently profiled in Health Affairs for lessons learned from its experience with cost containment, an issue it has been grappling with since achieving near-universal coverage in 2006.

Lesson number one?

“The first lesson is that the implementation of near-universal coverage triggered a new political resolve to address the difficult challenges of cost containment.”

In other words, achieving near-universal coverage subsequently made the cost issue too dire to ignore. While there is debate over whether insurance expansion accelerated cost growth (some say yes, others no), the facts are that Massachusetts’ per capita health spending is 15% higher than the national average, and that it has the highest individual market premiums in the country.

(Update: Medicaid expansion increased ER use by 40% in Oregon, so it’s not an inconceivable hypothesis.)

Interestingly, the same sequence of events is playing out halfway across the world.

Taiwan’s Looming Health Budget Challenge

After returning from Boston, I had the opportunity to grab lunch with an individual who was involved with Taiwan’s health sector for a number of years. Through that, I learned that Taiwan is facing a remarkably similar cost challenge.

Taiwan’s National Health Insurance (NHI) system has been lauded as a model for developed nations. Established in 1995, it expanded coverage from 57% to 97% within a year. But as might be expected, this coverage expansion unleashed a surge in utilization, nearly doubling outpatient visits, hospital admissions, and use of ED services among the previously uninsured. Since then, growth in outpatient visits, ED visits, and surgeries has vastly outpaced overall population growth.

To date, Taiwan has addressed the cost containment problem partly through aggressive price setting—sometimes below the cost of providing those services. Yet ironically, this has pushed providers to rely on increasing utilization as their only survival lever. This supply side-induced demand, along with low co-pays, no gatekeepers, and the political difficulty of raising premiums has created a financial situation where NHI expenditures have outpaced revenues almost every year since 1998.

Price controls will likely only work in the short term. In the long term, the NHI will need to alter its incentives to reign in over-utilization while encouraging greater provider efficiency, much as BCBS has done in Massachusetts. Shifting to a DRG-based payment system by 2015 is a good first step.

We concluded our conversation with a pronouncement that struck me: “China’s health system is about 20 years behind Taiwan’s in its evolution, so I think it can learn a lot from Taiwan’s experience.”

Marching Toward Cost Escalation in China

There’s a lot of wisdom in that statement. To date, most reform efforts in China have focused on expanding access, particularly in rural areas. As described in this UNICO report, insurance coverage in rural China had plummeted from 90% of the population to less than 10% with the collapse of the commune system in the 1980s. In the last decade, through a series of programs and reforms, China achieved 93% insurance coverage nationally, and just announced this past August that it had achieved 99% rural insurance coverage. While the accuracy of the numbers can be disputed, there’s no doubt that a lot more people now have access to health care services.

Which raises the specter of cost escalation in China’s not too distant future. Alarmingly, insurance expansion in urban China has been found to lead to such supply-side demand inducement (e.g. unnecessary treatments, expensive technology) that getting insured can actually increase one’s financial risk. Furthermore, China’s current insurance schemes have been criticized for being too narrow in scope (not enough procedures covered) and depth (not enough reimbursement). If China deepens the value of its existing benefits (as is much needed), we could expect demand to surge even higher.

Seek Truth from Facts

I’ve breezed through a lot of details to keep this post manageable, and for those who are interested, there’s a wealth of information in these papers on the reforms in Massachusetts, Taiwan, and China.

But the experience of all three shows that upon achieving near-universal coverage, cost containment issues are sure to follow. It would therefore seem like a prime opportunity to seek truth from the facts of the trial-and-error already happening in other countries. As our nation’s health care marches toward a costly ruin, perhaps the time is ripe for a UNICO-like study series on cost containment.

How to Eliminate $226 Billion of Overuse in Health Care

This post was first published on Project Millennial.

This past weekend, I had the opportunity to attend the 2013 Lown Institute conference in Boston. The Lown Institute is an organization founded by Dr. Bernard Lown in 1973 that promotes a humanistic, patient-centered practice of medicine. A major topic covered during this conference, as well as during last year’s inaugural conference, was that of overuse in health care.

Just How Much Overuse Is There in the U.S.?

Dr. Don Berwick, who gave the keynote address, estimated the cost of overuse in the U.S. to be $158-$226 billion in 2011. Interestingly, the methods of the four studies cited for the $158-226 billion figure were primarily based on macro-level economic approximations (e.g. comparison of DRG intensity between U.S. and Canada)—not on micro-level analyses of overuse in violation of widely-accepted standards.

Which makes sense, given that for many questions of what constitutes overuse, the science may simply not be clear. In 2012, Dr. Deborah Korenstein and colleagues published a review of the literature on overuse in the U.S. The study’s subtitle (“An Understudied Problem”) reveals the punch line. While they were able to document overuse rates for specific treatments that have clear standards (e.g. antibiotics for upper respiratory infection), they concluded that “the overuse literature includes relatively few procedures and diagnostic tests.” And they attributed that to the uncertainty of our science:

“The limited overuse literature is understandable given the challenges of developing standards to measure overuse. […] the process of defining appropriateness for many services remains incomplete owing to both gaps in the evidence and failure to translate evidence into appropriateness criteria.” –Korenstein et al., 2012

Which puts us as (aspiring) providers in a bit of a quandary.

Eliminating Overuse May Take More Than a Checklist

For providers (especially in a profession susceptible to paternalism), the most straightforward solution might seem to be to direct patients away from those wasteful, inappropriate treatments, shaving 7-8% off of our $2.7 trillion and growing health care expenditures. But for the majority of cases, there may not be enough evidence to clearly support a treat or don’t treat decision. And even when evidence-based recommendations exist, they are likely based on population-level analyses, which may conflict with the desires of the individual patient.

As Jessie Gruman argues, there is a coming conflict between clinicians pressured to adhere to a burgeoning number of quality measures and patients who are becoming increasingly engaged in their treatment decisions. Dr. Gruman was at the Lown conference, and she described her experience choosing a new doctor when her old one refused to give her a treatment that he deemed was “not worth it” (despite a 20% success rate). Dr. Gruman belongs to a growing chorus of advocates calling for increased patient engagement in their care decisions. Have the provider lay out the treatment options, with each option’s risk and chance of success, and let the patient decide.

At the same time, I kept hearing my former Swarthmore professor Dr. Barry Schwartz whispering three words in my ear: “paradox of choice”. Presenting people with 24 varieties of jam was enough to confuse them into inaction. Present patients with too many treatment options under actual life-or-death situations, and you could create a lot of (unwarranted?) stress and anxiety.

It seems to me that while some patients may be ready to be empowered consumers choosing from among a menu of options their provider lays out, others may not be there (yet). As Dr. Ranjana Srivastava, another panelist, aptly described, “Even with a menu of options, patients expect their doctor to take charge of their treatment.”

Overcoming the Culture of Overuse

Therefore, I believe the role of providers in reducing overuse will be much more complex than simply adhering to evidence-based recommendations to root out overuse. It will require engaging with each individual patient, intuiting that patient’s preferences for autonomy vs. provider advice, and having a conversation about the value of each treatment option (see Teaching Value Project)—including being willing to argue that the most aggressive option may not always be the best (though it certainly might be for that patient).

These are all skills that are often overlooked in our current medical education system. But the alternative—sticking with an ingrained culture to overtreat—may soon become unsustainable.

P.S. If you are interested in the issue of overuse, I strongly encourage you to check out the Lown Institute’s Right Care Declaration and sign if you so choose.

What the Stock Market Crash Reveals About Medical Errors

This post was first published on Project Millennial.

Proponents of high-deductible health plans want to give patients more skin in the game, to solve our system’s problem of escalating costs. Should our system have more skin in the game to do right by our patients?

The Best Risk-Management Rule Ever?

It is not only economically efficient, but morally imperative, to have “skin in the game”. That’s what Nassim Taleb, author of Fooled by Randomness (2001) and The Black Swan (2007), argues in a recent paper and interview on EconTalk.

Dr. Taleb opens by recounting the “eye for an eye” philosophy of Hammurabi’s code—or, in his opinion, “the best risk-management rule ever.” Three thousand years later, Immanuel Kant posed it in a slightly less morbid way through his notion of a “categorical imperative”: “Act only according to that maxim whereby you can, at the same time, will that it should become a universal law.” Or put more simply, do unto others as you would have them do unto you.

Corporate managers, academics, predictors, warmongers, and politicians, Dr. Taleb argues, are exempt from this moral imperative. They take risks and stand to benefit from the upside of those risks, but are shielded from the downside.

The Moral Hazard of “Fat-Tailed” Phenomenon

In fact, this problem is particularly severe for phenomena Dr. Taleb defines as “fat tailed domains”. A fat-tailed phenomenon is one in which an extremely rare but high-impact event dominates the effect of all other events. Repeated instances of a fat-tailed phenomenon (such as stock market outcomes every year) might look like this (source):

fat tail

A problem exists in that the reputation of market forecasters is based on how often they correctly predict the direction of the market movement, and not by how accurately they predict the final value of the market. (More technically, they are judged by a “binary metric” for what is actually a very skewed distribution.) A forecaster who is frequently right wins widespread admiration, even as people who follow that forecaster’s predictions ultimately see their savings wiped out by that rare, “blow-up” event. The forecaster, meanwhile, is insulated from the full pain of the investment loss.

The more skewed the phenomenon, the easier it is to hide the true impact of a mistake behind a façade of “pretty good performance”.

“Forecasters with steady strings of successes become gods.” –Taleb and Sandis, 2013

Skin in the Game for Patient Safety

Medical errors are a prime example of a fat-tailed phenomenon. For 98.6-99.4% of hospitalizations in the U.S., the patient is discharged without a lethal adverse event. But for the family of the patient who falls into that 0.6-1.4% of hospitalizations, getting killed due to medical error is an extremely “high-impact event”. I would imagine that the physician and care team—if they were aware that their error had caused the patient’s death—would feel very terrible. I’m sure even the hospital administrator would feel pretty bad as he/she looks over their adverse event reports. But will their suffering come close to what the patient’s family feels from the loss?

I’ve previously written about why we haven’t eliminated medical errorsNews flash: hospital errors don’t cause 44,000-98,000 deaths each year, as we previously thought. They cause 210,000-440,000 deaths per year. That makes hospital error the number three killer in the U.S., after heart disease and cancer.

Slow innovation is arguably one of the most effective ways to spur adoption of safer practices. But it is, by its very nature—well, slow. The nation’s third leading cause of death may warrant a bit more urgency. And that brings us back to the moral imperative of skin in the game. Today, individual hospitals and clinicians are rarely judged by the impact of their medical errors. When they are, they are evaluated based on the frequency of their medical errors—a binary metric (error vs. no error) for a very skewed phenomenon (the magnitude of suffering to the patient and family). Given the immense suffering caused by medical errors, it would seem that providers should share the burden in some way—perhaps not literally by Hammurabi’s standards, but, as Dr. Roberts suggests, “substitut[ing] the physical eye for the economic value of the eye”. And yet, the vast majority of public and private payers today are still paying hospitals (even rewarding them) for medical errors.

Dr. Ashish Jha recently wrote an article arguing that incentivizing hospitals for patient satisfaction more than patient safety has led them to invest in lavish amenities over patient safety improvements. To be fair, Medicare finalized a rule this August that will penalize hospitals in the lowest quartile for medical errors or hospital-acquired infections by withholding 1% of their overall payment. But that may not be a strong enough incentive to catch hospital executives’ attention, as Dr. Jha points out in this blog post. If we believe that market forecasters should invest in the same stocks they predict, and that warmongers should be subject to the draft themselves, then why shouldn’t health care providers have some skin in the game when it comes to patient safety?

What If We’ve Been Wrong? Implications of an Imperfect Science

Watch this. If you are pre-med, in medical school, in the health care field, or have ever felt a twinge of disdain for someone who was obese, watch this:

Back in April, as thousands of thinkers, innovators, and TED junkies descended upon DC for the 2013 TEDMED conference, I tweeted about my excitement to see the full talk by Dr. Peter Attia, a surgeon, researcher, and co-founder of the Nutrition Science Initiative (NuSI).

The full talk is finally out. And, man, is it powerful.

A Medical Establishment of Treating Bruises

Dr. Attia raises a provocative suggestion: What if we’ve been wrong about the causation behind obesity and diabetes? What if instead of obesity causing diabetes, obesity is actually a symptom of insulin resistance and other metabolic malfunctions?

Dr. Attia gives the somewhat comical yet strikingly apropos analogy of bruises and banging into coffee tables: Imagine a world in which we thought bruises were the problem. We would evolve “a giant medical establishment and a culture around treating bruises: masking creams, painkillers, you name it, all the while ignoring the fact that people are still banging their shins into coffee tables.” (emphasis mine)

It’s funny until you remember that less than two months ago, the AMA officially classified obesity as a disease. While the decision is unlikely to have much impact on currently research or public health efforts against obesity, I see one area where it could have a marked impact: spurring insurance payments for medical treatment of obesity, i.e. weight loss drugs and bariatric surgery.

  1. Weight loss drugs: Two new weight loss drugs, Qsymia and Belviq, entered the market last year. A day after the AMA decision, a group of lawmakers introduced bills in the Senate and House to require Medicare Part D to pay for weight loss drugs. Qsymia costs about $160/month, or $1920/year. Belviq costs about $200/month, or $2400/year. Our history with weight-loss drugs has not been that great, with many being ultimately pulled due to side effects or leading to rapid weight regain after discontinuation.
  2. Bariatric surgery: Currently, bariatric surgery coverage by Medicare or private payers is limited–something that may change given the new classification. However, the cost-effectiveness of bariatric surgery was called into question by a JAMA article published this February. The surgery and 30-day postoperative care cost $29,517. There was no significant reduction in health care costs for surgery patients compared to non-surgery patients in the 6 years following surgery.

Alarmingly, the vote went against the conclusions of the AMA’s Council on Science and Public Health, which studied the issue for a year and concluded that the main measure used to define obesity (BMI) is simplistic and flawed. More specifically: “Some people with a B.M.I. above the level that usually defines obesity are perfectly healthy while others below it can have dangerous levels of body fat and metabolic problems associated with obesity.” Which brings us back to Dr. Attia’s provocative suggestion: maybe we have cause and effect backwards.

“So what I’m suggesting is maybe we have the cause and effect wrong on obesity and insulin resistance. […] What if being obese is just a metabolic response to something much more threatening, an underlying epidemic, the one we ought to be worried about?” – Dr. Peter Attia

Revisiting an Imperfect Science

Intrigued, I went onto NuSI’s website to see what they’re actually doing. After overcoming my initial reaction that I had tripped upon another miracle weight-loss scam company, I dug deeper and realized that NuSI is actually gathering researchers to tackle significant shortcomings in the literature on the cause and effect of obesity.

Dr. Attia goes hard after the idea that carbohydrate restriction, independent of caloric restriction, is the way to reduce incidence of metabolic disorder, which then causes obesity. NuSI provides a great review of nearly 100 studies on diet and obesity and summarizes key points on why the existing research approach is insufficient. For those who want a summary of the summary, here are some key points:

  • The vast majority of dietary trials are “free-living studies“, in which researchers tell ordinary people to stick to a dietary regimen and then evaluate their adherence through questionnaires or food diaries. If people actually listened, we probably would have solved the obesity crisis by now.
  • The more stringent option is to isolate people in metabolic wards to monitor their intake. Any volunteers?
  • Even studies with sufficient sample size and decent adherence fail to parse out the effects of caloric restriction and carbohydrate restriction. This is a crucial nuance, as it precludes us from testing the hypothesis whether restricting a particular micronutrient (e.g. carbs) is more important for preventing obesity than reducing caloric intake overall.
  • A few studies have managed to make this distinction, but they relied on lean individuals. “Similar results might not have been obtained in a group of obese individuals or lean individuals susceptible to obesity.”

This last point is particularly provocative, as it suggests that obesity may be much less a result of “poor self-control” than we are inclined to assume. I had the opportunity to do a little research in the glorious field of gut bacteria research, and there’s rapidly growing evidence that of two perfectly identical people (at least from the outside) who consume the same foods, one may become strikingly more obese because of the gut bacteria in his/her gut. I recently began reading Dr. Eric Topol’s The Creative Destruction of Medicine, and I can easily foresee a world in which individuals have their gut bacteria characterized (which you can do for $80), and then have metabolic therapies tailored for them. But seeing as I’m already pushing 1000 words, that’s a story for another time.

What If We’ve Been Wrong?

Have you watched the video yet? If not, take two minutes and click to 13:30.

I think the power of Dr. Attia’s speech doesn’t come primarily from the provocative nature of his hypothesis, or from the facts he musters to support it. It comes from his tangible humility, and the painful implication that by staking our belief in an imperfect science, we may be letting our patients down.

“We can’t keep blaming our overweight and diabetic patients like I did. Most of them actually want to do the right thing, but they have to know what that is, and it’s got to work. […] If obesity is nothing more than a proxy for metabolic illness, what good does it do us to punish those with the proxy?” -Dr. Peter Attia

Blog Post Published on Project Millennial!

A big thanks to the folks at Project Millennial for publishing my newest blog post: “Why Haven’t We Eliminated Medical Errors?“. Project Millennial is a health care blog seeking to get the allegedly apathetic millennial generation excited about health care. There are some fantastic regular contributors to the blog, and I’m honored to have a post included by them.

I’ve included the opening of my post here:

To Err is (Still) Human

Last week, Consumer Reports released its new “surgery ratings”, encompassing 2,463 hospitals in all 50 states and the District of Columbia. The ratings measure the rate of hospital deaths and unexpected discharge delays for 27 common surgeries, including hip and knee replacements, back surgery, and angioplasty. Scrolling through the publicly available ratings list, I was struck by the number of dark shaded circles filling my screen—each one representing a hospital that performed poorly by their standards.

It’s been 14 years since the Institute of Medicine published its landmark “To Err Is Human” study, which estimated that a shocking 44,000-98,000 Americans die each year as a result of medical errors (0.2%-0.5% of hospitalizations). The study called for a 50% reduction in errors by 2004, asking, “Must we wait another decade to be safe in our health system?” It appears so; a 2010 NEJM study measured the rate of errors in ten North Carolina hospitals from 2002 to 2007. North Carolina was specifically chosen for having a “high level of engagement in efforts to improve patient safety.” Yet out of 2341 admission records reviewed, 588 harms were identified for a rate of 25.1 harms per 100 admissions. 14 involved harms that caused or contributed to a patient’s death, leading to a 0.6% hospitalization death rate due to medical error.

The dial hadn’t moved.

(Continue reading here.)

Prevention Isn’t the (Only) Answer: Two Sobering Findings

True or false:

  1. The best way to reduce health care costs is to prevent costly admissions for the sickest patients.
  2. Diet and exercise will help accomplish this goal for patients with diabetes.

Have you guessed?

Two studies out this week, which have flown under the radar of many news stations, suggests the answer may not be what we intuitively thought.

Almost 90% of Hospitalization Costs are Unavoidable

A new study from Dr. Karen E. Joynt and colleagues, published concurrently with their presentation of the results at the Academy Health conference, suggests that only about 12% of total acute care costs are actually “preventable”. They looked at 2009-2010 spending data for over 1 million Medicare beneficiaries and categorized their ED and hospitalization costs as “preventable” and “non-preventable” based on validated algorithms. This is what they found:

  • The top 10% most costly patients (“high-cost”) were responsible for 73.0% of total acute care costs. No surprise there, for anyone who’s read Atul Gawande’s “The Hot Spotters“, or heard of the 80-20 rule.
  • For both high-cost and non-high-cost  patients, slightly more than 40% of ED visits and costs were deemed preventable. Sounds promising…
  • However, total ED costs (~$124 million) paled in comparison to total hospitalization costs (~$3.0 billion). For these costs, “preventable” costs constituted only 9.6% of costs for high-cost patients and 16.8% for everyone else.

I’ve thrown together a graphic to illustrate this. Preventable costs are shaded darker:

Data source: Joynt KE, Gawande AA, Orav EJ, Jha AK. 2013. Contribution of preventable acute care spending to total spending for high-cost Medicare patients. JAMA. 2013 June 24;309(24):doi:10.1001/jama.2013.7103.

Data source: Joynt KE, Gawande AA, Orav EJ, Jha AK. 2013. Contribution of preventable acute care spending to total spending for high-cost Medicare patients. JAMA. 2013 June 24;309(24):doi:10.1001/jama.2013.7103.

This means that even if a health system succeeds in preventing 100% of its unnecessary ED visits, and in preventing all hospitalizations related to congestive heart failure, bacterial penumonia, COPD, urinary tract infections, etc. (diseases deemed to be “preventable” given effective outpatient management), total costs would only drop by 12.5%. That’s not very enticing, especially considering the formidable investment required to develop such care management capacity.

The main issue appears to be that the most costly hospitalizations, such as orthopedic conditions and cancer, can’t be prevented simply with better outpatient care management. You can do as much health coaching as possible, but short of confining granny to solitary confinement, she might eventually need that hip replacement.

The authors offer some cautious takeaways:

“These findings suggest that strategies focused on enhanced outpatient management of chronic disease, while critically important, may not be focused on the biggest and most expensive problems plaguing Medicare’s high-cost patients. Indeed, while a proportion of these very expensive inpatient episodes may be potentially preventable (such as acute myocardial infarction or degenerative joint disease leading to orthopedic procedures), their prevention would likely require a long time horizon and substantial investments in population wellness.” (emphasis mine)

Of course, hit those expensive diseases early with the one-two punch of diet and exercise! But just how long of a time horizon is needed? If you’re trying to prevent heart attacks in diabetics, it appears the answer is: longer than 9.6 years.

Diet and Exercise Failed to Reduce Heart Attack/Stroke Among Diabetics

It was not a heartening day for proponents of care management and prevention. Another study, from the Look AHEAD (Action for Health in Diabetes) Research Group, implemented a randomized intervention of diet and exercise for overweight diabetic patients and set out to track them for 13.5 years, hoping to prove some health benefits. They stopped it at a median of 9.6 years based on a futility analysis; it didn’t work.

Most strikingly, the diet and exercise intervention seemed to work just fine. When the study ended, intervention patients had achieved significantly greater weight loss (-6.0% vs -3.5%), reduction in waist circumference (-1.8% vs -0.9%), and improvement in fitness score (+3.7% vs -2.0%). Not only that, they had significantly greater improvements in almost all measured cardiovascular risk factors (including glycated hemoglobin, blood pressure, and triglycerides).

So it’s puzzling that the incidence of cardiovascular-related hospitalizations or death was statistically indistinguishable between the two groups (P=0.51).

Don’t Put All Your Eggs in the Care Management/Prevention Basket

Despite these sobering findings, I’m still of the opinion that care management and prevention are worthy efforts, as Drs. Carroll and Frakt point out in an accompanying editorial. After all, 12.5% of a very large number is still a very large number. And the diabetes study did show many indicators between the intervention and control groups converging after the first year; if we could figure out a way to sustain the benefits of exercise and diet, perhaps the longitudinal effects on outcomes would be more pronounced.

That said, maybe we should focus on two additional sources of cost savings:

  1. Streamline care processes and avoid costly errors during (non-preventable) hospitalizations. In fact, one of the study authors wrote the book on how a 5-step checklist prevented 43 central line infections and saved ~$2 million over 2 years. He’s now bringing the approach to birth and end-of-life care. And he’s not the only one achieving breakthrough results with simple solutions.
  2. Take a closer look at post-acute care. A provocative article last month pointed the finger at post-acute care for variations in Medicare spending. Since post-acute care made up 13.4% of national health expenditures in 2012 (compared to hospital care’s 31.5%), there could definitely be substantial opportunity to reduce costs there.

But perhaps the most important takeaway is: Never assume a strategy will work, even if it sounds logical, until you’ve looked at the data.