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Mark Ganz — What’s Your Purpose in Health Care?

The Healthcare Blog - Thu, 01/18/2018 - 14:16

Last week at Health 2.0’s Wintertech, held during “health care’s money week” JP Morgan in San Francisco, Cambia CEO Mark Ganz gave a remarkable talk–one you wouldn’t expect from the leader of a big health insurer. He called out pharma for price gouging, he asked what consumers would think about the conversations we are having, but mostly he asked people to think about why they were working in health care. And he did it with a deeply personal set of stories. Everyone there found it very moving and very important, so I wanted to share it with the THCB audience. It’s well worth your time to watch. — Matthew Holt

Categories: OIG Advisory Opinions

CDI and Medical Necessity: Closing the Gap Could Prevent Denials

Medical Coding News - Thu, 01/18/2018 - 12:21

Exactly what is medical necessity? To many, it is the belief that a service or procedure is warranted or justified for a patient. Others view it as a way for health plans to deny coverage for a service. The American Medical Association (AMA) defines medical necessity as “healthcare services or products that a prudent physician […]

The post CDI and Medical Necessity: Closing the Gap Could Prevent Denials appeared first on MedicalCodingNews.Org.

Categories: Healthcare News

The Naming of Things

The Healthcare Blog - Wed, 01/17/2018 - 22:58

It’s happening across many parts of the federal government, in many sectors. Officials of the National Park Service have been reprimanded for tweeting about climate change. Scientists at the Centers for Disease Control (CDC) have been warned away from seven specific words in their budget documents, including “fetus,” “evidence-based,” and “transgender.”

It is happening in healthcare as well. In previously secret proceedings, revealed here for the first time, representatives of organizations and companies across healthcare are in negotiations with a cross-agency team at Health and Human Services to restructure the language we use around medicine and healthcare.

This is being done for the good of the industry, its regulators and payers, and ultimately of course of the American people. And the industry. In fact, this will impact the industry in multiple ways, not the least of which is clarifying and easing the labors of those of us in the pundit and panjandrum crowd.

A HHS employee delivers a list of “potentially objectionable” and/or “problematic” words to a cabinet meeting in December as part of the administration’s comprehensive new “word strategy.”

Honesty in labeling and punditry

After all, those of us who are dedicated to fixing healthcare first have to talk about it properly. We have to be sensible, straightforward, and honest in our discussions.

Take, for instance, “overtreatment,” which can now be re-labelled “medical exuberance.” “Unnecessary” tests and procedures will be called “recreational,” while the tenth MRI for the same problem will be referred to as “just dialing in the range.”

We can quit talking about the over $1 trillion per year we spend on these recreational tests and procedures as “waste,” and refer instead to healthcare as “America’s vital job creation engine.” People like jobs. The new healthcare slogan writes itself: “We turn medical exuberance into jobs.”

“Epidemics,” in which particularly popular disease states “go viral,” can be re-framed as “population-based marketing trends.”

We can trade the pejorative term “upcoding” for “opcoding,” as in “opportunity.” And “fee-for-service,” which is getting such a bad rap these days from the panjandrum crowd, can now be called “streamlined menu-based payment,” kind of like a Chinese restaurant. Instead of “one from Column A, and two each from Columns B and C,” we can just say, “This whole column. All the columns. And the desserts on the next page.”

“Overcharging” vs “Value Enhancement”

It has become blatantly obvious that slapping the label “overcharging” on the common and useful business practice of charging ten times as much as the shop down the road for, say, a new knee or a remodeled heart is a downright Orwellian twisting of language for political ends. The more reasonable and neutral replacement term is “value enhancement.”

This is eminently logical. The economist’s definition of the value of anything is “what an informed buyer will pay for it.” If you (or some payer on your behalf) paid $115,000 for a new knee while some other poor loser only got to pay $17,500 for the same implant, the same institution, the same surgical and rehab teams, clearly you have a lot more value packed into that titanium baby. And you were informed about it, at least after the fact, when the six-foot-long bill in six-point dot matrix type finally showed up in the mail.

Your $115,000 new knee is clearly something to brag about. Does anyone order a new Mercedes to be delivered without the three-pointed star in a circle on the hood? I don’t think so. The liver transplant doc who lasered his initials on the livers he put in? Forget that, you’ll want the doc’s logo tattooed right on the outside of the knee, just like those folks who leave the “Armani” or “Zegna” tag on the sleeves of their suits and keep checking the time so that you can see their Rolex. Maybe the surgeons can re-engineer one of the skin flaps you keep growing into a cute little all-natural price tag that will wave around merrily as you get back to dancing around the racquetball courts.

“Organic” vs. “primitive” medicine

As different styles of medicine emerge, we will have to think carefully about how we label them. Remember when the first hospitals to open departments of “Nuclear Medicine” were met by angry crowds of citizens bearing pitchforks and petitions—when the practice in fact had nothing to do with nuclear reactors or nuclear weapons?

What should we call diagnostics and therapies derived from big data troves and embedded sensors put through AI wringers and deep-learning algorithms popped through CRISPR machines to deliver personalized RNA medicine? We will call that style “real” medicine or “organic” medicine.

In contrast, medicine derived from intense personal consultation with a physical human doctor who actually knows you, your lifestyle, and your stage in life, and actually touches your body and subjects it to thoughtful testing, will be called “primitive” medicine.

Kilo-OODs

And life expectancy? What about the fact that for every 10 million people who drop insurance because it would cost them more than the roof over their head, we get 10,000 extra premature deaths? We can just call that “market-adjusted longevity.” People choose what they choose, and they are free to. This is America!

Even calling them “premature” deaths is overly argumentative and political. If people want to die earlier than some government actuary thinks they should, why should we give their deaths a pejorative label? Rather than “premature deaths,” we’ll call them “opt out deaths.” Ten thousand, 20,000, 40,000 “opt out deaths” (OODs) per year actually represent the ultimate consumer choice. The new metric will be KO/A, that is kilo-OODs/annum. As in, “This bill will result in a quite nominal 35 KO/A, a number the committee feels comfortable with when we consider the net present value of the ROI to the GDP resulting from consumers exercising their ultimate free choice to permanently de-access the market.”

There are of course multiple well-meaning groups working tirelessly to eliminate this precious consumer choice. We have to acknowledge that. But calling the schemes they propose “universal” or “single payer” or even “Medicare for all” is blatantly contentious. We will prefer a more neutral, descriptive term such as “forced march” healthcare.

Acronym Control (AC)

It is widely agreed that acronyms must be brought under control. This inter-agency effort will be coordinated by a new office within the CMS of HHS called the ACA Agency for Control of Acronyms (ACA), known more simply as (HHS(CMS(ACA2))). Programs and agencies that lose their own acronyms will have to fall back on the generic OWA (Other Weird Arrangements).

The communication of quantum financing

Many of the seemingly intractable problems at the core of healthcare are really problems of communication. For instance, over the past few decades, America’s hospitals and clinics have bravely pioneered the use of quantum financing. But they have not sufficiently explained this to their patients, their patients’ families, and the executors of their patients’ estates. We as an industry have to get straight with the public and clearly lay out the theory and the facts of, for instance, Schrodinger’s networks, in which the surgeon that you hired to rebuild your sterno-thoracic cavity is definitely both in your network and out of network at the same time. He or she will not collapse into one state or the other until the moment you rip open the bill they send you afterwards.

Similarly, we will see a big leap in price transparency when we understand the application of Heisenberg’s Uncertainty Principle, which has long existed as an undergirding axiom of hospital finance. For example, you might be able to discover just which items you might be charged for in a given procedure, within a reasonably narrow curve of probability. You might be able to discover exactly how much each item cost at some indeterminate point in the past. You may well be able to discover what an operation like yours cost somebody else last year, or this year in some other state. But if you were to somehow discover exactly what your operation will cost you in this institution this year, your knowing that would collapse space-time as we know it and annihilate at least the institution or possibly even the entire healthcare sector.

We can anticipate ever greater clarity as the science proceeds. The biggest, deepest cosmological conundrum has long been: How can the universe exist at all? If matter and anti-matter arise randomly and in equal amounts as theory suggests, and they continually combine and annihilate one another, why is there so much more matter than anti-matter in the universe, enough to build planets and stars and forceps and Senators?

When cosmologists and physicists resolve this core problem, I think we will find the answer to the analogous problem in healthcare: Why, with all the discounts and special prices and risk-sharing and all this maniacal curve-bending going on, does healthcare continue to cost more every year, gobbling up more and more of the economy? And the corollary question: At what point does healthcare get so large and eats up so much of the resources available to it that it begins to eat its own tail and soon disappears completely with a barely audible “pop”?

These are serious questions.

Which community?

After all, the whole healthcare system is derived from evidence-based economic science “in consideration with community standards and wishes” as the CDC directive helpfully suggests.

Which begs the question: Which community? Whose fine standards and wishes are being considered and weighed with the available evidence? A close examination of the design of the vast agglomerated healthcare sector suggests that it’s the warm and charming community of shareholders, bondholders, entrepreneurs, and executives. And of course us, the pundits, podium poohbahs, and grand panjandrums, who will not be out of work for a long long time. Whether we like it or not.

Categories: OIG Advisory Opinions

Science Fiction Coming to Life

The Healthcare Blog - Sat, 01/13/2018 - 12:17

 

Given the size and scope of the annual J.P. Morgan (JPM) Healthcare meeting (I resisted the temptation to say “diversity”), everyone in town – the minority who actually attend the formal presentations, and the many others who show up in San Francisco to meet and network – comes away with a slightly different experience.

With this caveat (and with the explicit reminder/disclosure that I now work at a life science venture fund, and as always, I’m speaking only for myself), I left the meeting with two fairly pronounced takeaways.

JPM: Two Contrasting Takeaways

First, this feels like an unbelievable, almost magical time in biopharma – a colleague described it (in a good way) as science fiction coming to life. Biological technologies, approaches, and ambitions that might have been dismissed as fantasies only a few years ago now are part of the fabric of the industry – and increasingly, it seems, clinical care. Gene therapy, gene editing, cell therapy, immune modulation – these modalities, alone and in combination, are what many in and around biopharma are contemplating, and the sorts of programs many drug development organizations are hoping to prosecute. It’s hardly surprising many JPM participants emerged with the sense of optimism my Forbes colleague Matthew Herper so accurately captured.

I was equally surprised by what I saw – or more accurately, didn’t see – through the lens of data and technology. As I’ve shared on Twitter, in addition to life science opportunities, I aspire to focus on the elusive middle-ground between tech and life science, and identify and invest in grounded, implementation-focused tech-powered startups that can improve how impactful new treatments are discovered, evaluated, and delivered. However, my overwhelming impression from this year’s JPM is that while data and tech may be embraced at the level of the C-suite, and while everyone professes an interest in AI, these emerging approaches and ways of thinking have generally not penetrated most biopharma organizations at the line/operations level, and have generally not yet impacted how these organizations actually approach their basic work of discovering and developing new therapeutics. Exploratory innovation initiatives, of course, abound, as do data wrangling and integration efforts (see here, eg), but these activities as yet seem to have had minimal impact on how most R&D is actually prosecuted within these organizations.

From what I can gather, it’s not a hostility to technology as much as a sense that it’s not immediately clear to most of those in the trenches how (or even whether) the emerging technologies will meaningfully impact the work they need to do, and many are concerned about, or at least wary of, the additional work it may create. Most acknowledge the possibility that big data and emerging analytics will likely be useful eventually, but few see these changes on the immediate horizon.

Technologist View

One biologist who does believe we’re on the threshold of profound change is Vijay Pande, a computational biologist and investor at the high-profile VC fund Andreessen-Horowitz, best known for their tech prowess, but increasingly looking towards biology; they recently raised their second biology-focused fund ($450M), and have built out their biology/healthcare team. During JPM, Pande published a (characteristically) thoughtful essay that captured what might be described as the tech view of biology.

The gist of Pande’s argument is that biology is especially complex because evolution has left cells with the equivalent of “technical debt” – essentially, old code that isn’t really used but has stuck around; cells can be “refactored,” he argues – the old code removed, the essential stuff retained and perhaps refined. Moreover, our ability to understand biology has been limited by the intellectual capabilities of the human mind; turn AI loose on biology, he argues, and we’ll be able to “go beyond the limits of the human mind and inherited tech debt.” And when “we can go beyond human hunches to really understanding biology,” Pande says, “we get far greater predictive power.” He adds,

“For the first time, the technical debt and ‘spaghetti code’ of biology can be mapped, understood, and even refactored. And given the better-than-Moore’s-Law for bio, this is happening at a time when genomics, proteomics, metabolomics, etc. have become relatively inexpensive to map. Coupled with the advances in AI (which itself are driven by similar cost reduction curves), this all opens the door to new applications of biology for healthcare with unprecedented accuracy. So the question becomes: When you finally understand the spaghetti code of bio, what can you do with it?”Pande’s perspective on our current state of understanding – the view that we’re on threshold of replacing empirical experimentation with in silico analysis — contrasts sharply with the perspective of almost everyone I met at JPM with biopharma domain expertise. Most would likely dismiss Pande’s take as incredibly naïve, disconnected from their lived reality. As outspoken entrepreneur Ethan Perlstein (an impassioned empiricist) put it, rather acidly, on Twitter, Pande’s piece “is like an AI bot imitating a first year CS grad student discovering biology for the first time on Wikipedia.” Well, then.

Tech futurists like Pande typically are not troubled by such critique; many have the view that workers in industries about to be disrupted by tech don’t see it coming, and tend to see such disruption as the sort of thing that impacts other people and other industries, often discovering too late that they were wrong. (Of course, sometimes it’s the futurists who are wrong — flying cars, anyone?)

How Will Tech Find Its Way Into Pharma?

My own view is that we’re still pretty far away from the future Pande describes, and I’ve seen little evidence to suggest technology has brought us anywhere close to meaningfully resolving the complexity of biology and of biological systems, much less turning drug discovery and development into a tidy in silico exercise.

And yet, as fantastical as this tech perspective seems, my sense is that there’s something incredibly robust and exciting behind it. Pande’s optimism, I suspect, is driven by the remarkable progress that occurring on the tech side, especially in the tools available for collecting and analyzing data, often in massive quantities. Simply stated, these tools are too powerful and too important not to impact how we think about biology and drug discovery. The question is how to bring the power of these tools to bear in developing impactful new medicines for patients. (See also this recent post on the vital, often-underappreciated importance of implementation.)

To this point, much of the interaction has taken the form of transactional engagements, what I call “Rumpelstiltskin” projects, designed essentially to spin straw into gold. For example, a pharma company has a dataset (such as from a failed trial), and the tech co is tasked with finding hidden value – a discriminatory biomarker, perhaps. Or, a pharma company is struggling with designing a small molecule against a particular target, and the tech co is tasked with using their proprietary methodology to deliver it.

In the near term, I suspect the main impact of technology on drug development will be through such transactions. A pharma is more likely to license a compound developed through fancy analytics (it knows how to evaluate compounds) than it is to license, successfully implement, and routinely incorporate the analytic methodology itself. Given that the economics for products (especially after early clinical de-risking) tend to be especially attractive, it’s perhaps not surprising that many companies that start as analytic or diagnostic companies ultimately pivot towards therapeutics.

After enough of these successes, however, pharmas will recognize that competence in modern analytics (or big data), is essential for drug development (a must-have, rather than a nice-to-have, in consultant-speak), and at that point will strive with a real sense of urgency to internalize and integrate both the technology and the expertise.

The Nature Of Medical Progress

While such tech-enabled drug discovery may seem remote today, consider how far-fetched gene editing and gene therapy seemed just a few years ago – who would have guessed they would so quickly become must-have biological technologies?

Medical advances rarely arrive as overnight successes, and powerful new approaches tend to fail before they succeed, as Anish Koka has described in this truly magnificent, must-read essay on the history of organ transplantation.

“We need the pessimists,” writes Koka, “because most attempts at progress in medicine will fail. But we also need the relentless optimists, because just maybe, one of them will break through and make the impossible possible.

The point – which Paul Kedrosky described on Twitter as “the “Triumph of the Optimists” argument, or the Enlightenment idea of progress” – is that the naive/optimistic belief that progress just around corner motivates the intense effort often required to deliver change, even though the change generally takes far longer to achieve than the initial champions imagined.

We’ve seen this in gene therapy, an effort that took more than thirty years of hard work, as biotech CEO Cyrus Harmon pointed out on Twitter. Moreover, as VC Vishal Gulati wryly observed, “at no point in those thirty years did we believe it was not imminent in the next five years.” (tweet lightly edited for clarity).

I resonate with the relentless optimism that propels medical science forward – it’s also what I love most about tech and Silicon Valley. And as out of touch as the tech view of biology seems today, I am excited about the potential of emerging technologies to radically redefine the way we approach biology and understand disease.

To be sure, it would behoove tech futurists (and particularly, I suspect, tech futurists who are investors) to have the humility to appreciate the difficulty of taming biology. But it seems equally important for contemporary drug developers to remind themselves of the need for radical improvement, of the possibility of radical change, and of the tendency for disruption, like bankruptcy, to arrive slowly at first, then all at once.

David Shaywitz is a Palo Alto-based VC. His views are his own.

 

Categories: OIG Advisory Opinions

A Surprisingly Logical Argument in Favor of Head Transplantation

The Healthcare Blog - Sat, 01/13/2018 - 04:42

Not since Rene Descartes gazed from his garret window in early 17th-century Paris and wondered whether those were men or hats and coats covering “automatic machines” he saw roaming the streets has the issue of personal identity and your cranium been of such import. Descartes feared a world that he alone occupied due to deception by the devil. Today we face a different mind-body challenge in the form of a devil we know: Italian neuroscientist Sergio Canavero. He recently announced that the first human head transplant is imminent.

For bioethicists, the moral critiques of this surgery practically write themselves: Are we merely our bodies? How can a person so ill as to wish to trade in his lifelong corporeal companion be considered competent to consent to such a drastic procedure? How can family members consent to donate a body that they could very well run into — and recognize — at the beach or gym? What if a left-handed person received a right-handed body? What if a lifelong Chicago Bears fan woke to find himself attached to the green-and-gold-tattooed torso of a former Packers fan? Would transplant recipients need to buy whole new wardrobes? Who will pay?

We were among those early to carry ethical torches and morally indignant pitchforks at this transplant ahead of its time. Caplan not long ago called Canavero’s work “crackpot science,” writing that “everything about Canavero’s activity is ethically wrong” while incisively reminding all that “[m]oving a head is not akin to moving a light bulb to a new socket.” Ever at the forefront of translational bioethics, Caplan was, as is his wont, quick to integrate electrical engineering with bioethics on the frontier of the emerging field he wittily dubbed, cephalogy.

More recently we decided to hole up in our own, 21st-century garrets: putty-colored, fluorescent-lit boxes seven stories above a lower midtown Manhattan block. It was here that we took the time over a lunch of offal things to explore the real risks and benefits of head transitioning, and it was here that we realized that we had been coming at the problem completely the wrong way. Remember good facts make for good ethics. What Canavero is planning isn’t really a head transplant, but a body transplant. “Heads up!” he cried, when he should have threatened, “Bodies down!”

His first patient was to have been a Russian man with a spinal muscular atrophy disease called Werdnig-Hoffman. No one applauded; many recoiled. Then Canavero’s fancy turned to China, where he planned to do the first transplant at the end of 2017. (As Newsweek reported, “He’s vague on details.”)

As bioethicists are always happy to note, neuroethics is incredibly boring unless engaged in hypothetical debates about the morality of downloading your neural persona into a Roomba. If Canavero wants the PR he craves — and, importantly for our concerns, he must stay within the parameters of morally permissible omnicorpectomies — he should reframe his quest. He should tell his new Chinese associates that the body swap is not intended to replace a sound albeit average mind with a better body but instead to offer a glorious body an even more glorious head.  Enhancement is the heading Dr. C should be tacking.

Who would oppose such a plan? Only prudes, religious crackpots, and small-minded, knuckle-dragging, natural law devotees could possibly object to such an enormous stride forward in corporeal correction. But, as our president would say, who cares about them? We must make Americans greater in the head. We have already taken ourselves, as Nietzsche said, from the worm to man. Progress must continue.

Once again, ethical questions abound — but for once there are answers. As we are nothing if not principled, let’s start there. Can there be a better safeguard of autonomy—not to mention can-do rugged America individualism—than letting someone choose to install his or her own head, and all the attendant features and contents, on a perfect body? In fact, it would be maleficent to stand in the way.

An overarching concern, as it so often is in bioethics, is justice. Obviously, the caput-corpus transfer will be quite costly, and since insurance rarely pays for anything anyway, there’s no reason to believe it would cover this. Since not all can have access to one, how do we decide who should? As Men at Work once plaintively and rhythmically asked, “Who can it be now?”

We selflessly suggest ourselves and our bioethics ilk. We offer up our/their magnificent heads.  We would be honored, not to mention noble and magnanimous, to join Jonas Salk, Barry Marshall, and Werner Forssmann in the hallowed halls of self-experimentation. Whether you are ASBH-certified or just an old bioethics maven, future generations need your noggin.

Look at the bodies of your peers in bioethics — great minds borne upon obtunded, scrawny, paunchy, frail, decrepit, myopic, doughy, brittle, often crapulent, corpulent, corporeal carriers — and you’ll understand the source of our inspired proposal. Would not all future generations benefit from many, many more years of inchoate prolixity from today’s deepest applied moral thinkers?

When looking at head transitioning from this more enlightened perspective, moral opposition to Canavero’s dome replacement surgery is not just mindless, it is stupid. Bioethicists unite! We belong at the head of the queue. Let this surgery go to our heads.

This blog post first appeared in the Hastings Center’s Bioethics Forum.

Categories: OIG Advisory Opinions

New Evidence in JAMA Shows Insurance Gaps Leave Some Cancer Patients Without Treatment

The Healthcare Blog - Fri, 01/12/2018 - 14:53

“How long do I have?”

The man was just diagnosed with lung cancer.

“That depends,” his doctor says. “What insurance do you have?”

New research suggests that conversations like these may be actually taking place across the country. Todd Pezzi and colleagues analyzed a national database for treatment outcomes for patients with limited stage non-small cell lung cancer, a diagnosis with high rates of response to treatment. The results, reported in JAMA Oncology last week were astounding: patients with Medicare, Medicaid, or no health insurance received different, and often worse, care than those patients with other types of health insurance. These patients were less likely to receive radiation therapy in addition to chemotherapy, part of the standard of care treatment. And they found that patients with Medicare or Medicaid were significantly less likely to survive their cancer than their counterparts with private insurance.

Clearly, the health insurance system is broken if different insurances determine what treatment a patient will get, even when there is a proven standard of care. Forcing patients and doctors to continue under what has been famously referred to as the patchwork quilt of our healthcare system is leaving people out in the cold.

These findings should alarm anyone who may be a patient one day – which, of course, is everyone. For me, a resident in internal medicine, the findings are also disquieting and discouraging. It’s frustrating to think that the best and most evidence-based treatments I spend many hours per week learning about may not even be available for some of my patients. I worry about being a part of a healthcare system where science and ethics take a backseat to billing groups.

To be sure, many will use this data to argue that the government shouldn’t be involved with health insurance at all. Although public insurance is often criticized as denying expensive or experimental drugs to patients, that’s not the issue here: radiation therapy in combination with chemotherapy is standard care for this cancer. Still, even if this hole is patched, it does nothing to address the underlying health insurance system with its persistent gaps and disparities in care.

After a tumultuous year of fighting over the Affordable Care Act, many progressives in Congress seem relieved to leave the fight over healthcare in 2017. The President and his team met at Camp David this week to begin to set the legislative agenda for 2018, where their focus has moved to infrastructure. While the health of our roads is important, the health of our citizens is critical. But health care must not be left by the side of the road.

“Standard of Care” therapies are as close as we come as a profession to black and white. A healthcare system which doesn’t guarantee at a minimum these standard therapies is not a system at all.

Categories: OIG Advisory Opinions

The Individual Mandate’s Dead. What Happens Next?

The Healthcare Blog - Thu, 01/11/2018 - 14:59

The demise of the ACA individual mandate, along with Trump’s and Republicans’ efforts to repeal Obamacare in 2017, will trigger in election year 2018 a new phase of the long-running, bitter battle over the fate of ACA, the insurance marketplaces, and the direction of health reform in general.

Surprisingly, the Democrats appear to have the upper hand for the moment.   Republican efforts to repeal the ACA in 2017 were deeply unpopular—only about 20 percent of the U.S. population supported them. Independents and moderate Republicans, in Congress and among voters, were notably opposed. And in the Senate, moderates killed the various ACA repeal bills (albeit by narrow margins).

The Republican tax bill is also unpopular.

Recent special election results in Virginia and Alabama—put Republicans off-balance and on-notice as well. In particular, the Alabama result bends the vote math in the Senate against any repeat ACA repeal efforts in 2018, and very likely beyond.

But, perhaps most surprising, the resurgence of interest in “coverage for all,” universal coverage, and “health care as a right” that started with Bernie Sander’s campaign in 2016 has continued to gain traction, even among some conservatives.

Are we seeing the turn of the screw?   If so, will this be a fast-moving cultural shift, like the acceptance of gay marriage? Or is it on the slow track, like the civil rights movement and equal pay for women? Given the fractious health care debate, the money involved, and philosophical divide, I tend to think it’s the latter. But there’s no doubt a shift is going on.

In this context, revelations in the new book Fire and Fury are notable: Trump apparently has been secretly skeptical of ACA repeal and has asked staff “Why can’t we just do Medicare for all.” Not incidentally, Trump put the kibosh on Republican plans to reform Medicare and Medicaid in 2018. (After a Camp David strategy retreat over the Jan 6-7 weekend, Republicans leaders appeared to agree.)

At the end of this blog is a brief list of emerging Democratic ideas and proposed legislation that would take us in the expanded- and universal-coverage direction, largely via Medicare and/or Medicaid expansions. These proposals won’t go anywhere in 2018 but they will be a part of election-year discussions, and possibly lay the foundation for a quite different kind of health care debate come 2019-20-21.

But first, there are immediate structural and policy issues that have been triggered by the mandate’s demise.   While that new law doesn’t kill the mandate until 2019, the effects will begin to show up in early summer 2018 when insurers must start making decisions about participating in the exchanges next year.

As of this writing, it’s not clear if Congress is going to pass two bills that aim to shore-up the exchanges in the wake of the mandate’s end.   One bill would restore for two years the CSR (cost-sharing reduction) payments that Trump killed last fall, and allow states more flexibility in regulating the operation of exchanges.

The other bill would create a 2-year $10 billion reinsurance fund to help states cover high-cost patients—via the exchanges or in newly created high-risk pools.

Both bills are being debated as possible components of the federal budget deal that must be reached over the next month or so. Senate Leader McConnell says he plans to honor his pledge to Susan Collins (R-Maine) to adopt the measures. (Her vote for the tax bill was contingent on this.)   But House Republicans have signaled loud and clear that they don’t want to “bail out the insurance companies” or “fix” the exchanges in any way.

This is a scorched earth position—shocking given the harm that will cause in their states.  If they hold to this line, the Dems will mercilessly beat up on them about it during the campaigns this year.

But, oddly, for now, the Dems are sanguine about the two bills (and even the mandate’s fate). That’s largely because of the counterintuitive result that occurred during exchange enrollment for 2018.   Namely, Trump’s nix of the CSR payments backfired. Increased premiums due to Trump’s action of the CSR payments triggered increased subsidies (and more government spending!), and exchanges manipulated the system to allow (and even push) more people into better, higher tier coverage.

That’s one reason exchange enrollment stayed on course for 2018, with minor fall-off.

Authorizing the CSR payments would, thus, reduce subsidies in 2019 and force plan switches by millions of people in 2019. The whole complex issue surrounding CSRs arguably needs more thought.

As for the fate of individual mandate, Kaiser Family Foundation’s Larry Levitt—ever insightful—told The Washington Post recently that the mandate’s termination at the federal level could end up being a “blessing in disguise.”

Why? (1) It removes this much-hated (by Rs) provision from public scorn and the general debate around the ACA  (2) It will allow a real-world test of whether the mandate made all much difference in getting people to enroll in the exchanges, or whether the subsidies are the chief motivator.

Related, the CBO is scheduled to release a new examination of that issue in coming weeks. Analysts expect the agency to (perhaps significantly) revise downward their previous estimate that 4 million people in 2019 and 13 million over the next decade would lose coverage absent the mandate.

States can enact an individual mandate

Meanwhile, it looks like a bunch of blue states are going to enact (or try anyway) their own individual mandates during the 2018 state legislative season (January to June for most states.) The policy would be the same: sign-up during an annual open enrollment period each fall or incur some kind of a penalty.

Massachusetts has an individual mandate that predates the ACA; it’s credited with contributing to that state’s successful achievement of near universal coverage. Maryland lawmakers became the first to unveil an individual mandate plan this week (Jan 9). It’s paired with an automatic enrollment plan and is innovative: the plan would charge a fee to people in the state who do not have insurance.   But it would allow people to choose to use that fee instead as a down-payment on exchange coverage. Alternatively, they could pay the fee. All without coverage could receive counseling on their options.

Several private groups and reform advocates are working on model state individual mandate laws.

If insurers start to signal in June and July that they intend to drop out of more exchanges for 2019—participation is already way down compared to 2016 and 2017—Congress will likely be forced into a conversation about alternatives to the mandate, to preserve access to coverage.

One major mandate alternative idea Republicans have pitched in the past is to impose a premium surcharge on those who enroll late. Medicare has such a surcharge and it’s nasty. Failing to sign-up upon eligibility for Part B results in a 10 percent premium surcharge for each full 12-month period the beneficiary was not enrolled—for the entire length of an individual’s enrollment in Medicare!

Other proposals:

  • A requirement to have continuous coverage, or pay a premium surcharge
  • Limited plan choice—restricting the number or types of plans available to an individual enrolling late or after a gap in coverage.
  • Reduced subsidies—for late enrollment or after a period of time without insurance that exceeds 60 days.
  • Auto-enrollment—of everyone who does not sign up but then allowing them to opt-out. (Otherwise, it’s essentially the same as a mandate and raises significant government “big brother” issues). This presumably would be combined with a penalty for late enrollment.

Reinsurance

My own strong view is that Congress should enact the $10 billion reinsurance program as part of the FY 2018 budget bill.   This should be a no-brainer.   It’s a bipartisan concept; Medicare Part D operates with a permanent reinsurance program.   And reinsurance is a well-known insurance industry tool.

The Trump administration in March invited states to apply for a waiver to set up reinsurance pools, under an existing ACA program (Section 1332). In fact, a reinsurance pool existed under the ACA from 2014 to 2016 and helped constrain premium increases by 5 to 10 percent. The program then lapsed per a provision in the ACA. It’s time to bring it back.

Three states have enacted their own reinsurance programs: Alaska, Minnesota, and Oregon.   All have federal waivers for the programs. Alaska’s program lowered premium increases from 42 percent to just 7 percent in 2017.

The $10 billion needs to be combined with measures that make it easier for states to apply for and win the waivers. Such measures are a part of the proposed legislation.

Stop Trump’s sabotage

The Trump administration is moving ahead with plans to expand association health plans and prolong the period—possible up to a year—in which people can buy so-called “short-term” health insurance that do not meet ACA requirements.

Both these initiatives—part of Trump executive orders—will undermine the exchanges and are bad policy. Draft rules on association health plans were issued this month. It’s not clear how they will look after the 60-day comment period; opposition is strong.  Blue states may seek to block association health plans. The draft rules on short-term plans are due any day.

Alex Azar will soon take the reins at HHS. Trump and the Republicans are hoping he’ll be an effective promoter and enforcer of conservative health policy—and use the power of HHS to undermine the ACA.   But I’ll bet he won’t support policies that reduce coverage and lead to more government spending at the same time (as Trump’s CSR action did).

What the Dems are proposing

Here, in brief, are some of the ideas percolating up from newly emboldened Democrats (courtesy of a nice list in a Jan. 8 AP story). The left-leaning Century Foundation has also recently published a series of thoughtful analyses by leading health policy experts—all casting an eye toward the future. You can find them here.   https://tcf.org/publications/

Medicare for All.   Bernie Sanders is still pushing for this but state-level attempts to enact single-payer have foundered or stalled, primarily because of the large tax increases needed.   Still, one-third of Sanders’ Democratic colleagues in the Senate are co-sponsoring his latest bill.

Medicare-X. Senators Tim Kaine and Michael Bennet (Colo) would allow individuals in states and communities lacking insurer competition to buy into a new public plan. Medicare-X would be available as an option through HealthCare.gov and state health insurance markets. Enrollees could receive financial assistance for premiums and copays through the ACA.

Medicare Part E. Yale University political scientist Jacob Hacker has proposed this. People of all ages who don’t have access to job-based coverage would be able to enroll. It would be financed partly with taxes on companies that don’t provide insurance. Consumers would pay income-based premiums. Hospitals and doctors would be reimbursed based on Medicare rates.

Medicare at 55: Sen. Debbie Stabenow (Mich.) has introduced a bill that would let older adults buy into Medicare (including Med Advantage plans) starting at age 55. Enrollees would be eligible for subsidies under Obama’s law.

Medicaid Buy-In. Sen. Brian Schatz (Hawaii) and Rep. Ben Ray Lujan (N.M.) have introduced legislation to allow states to open their Medicaid programs up to people willing to pay premiums.

 

 

Categories: OIG Advisory Opinions

Separating the Art of Medicine From Artificial Intelligence

The Healthcare Blog - Tue, 01/09/2018 - 22:20

Artificial intelligence requires data. Ideally that data should be clean, trustworthy and above all, accurate. Unfortunately, medical data is far from it. In fact medical data is sometimes so far removed from being clean, it’s positively dirty.

Consider the simple chest X-ray, the good old-fashioned posterior-anterior radiograph of the thorax. One of the longest standing radiological techniques in the medical diagnostic armoury, performed across the world by the billions. So many in fact, that radiologists struggle to keep up with the sheer volume, and sometimes forget to read the odd 23,000 of them. Oops.

Surely, such a popular, tried and tested medical test should provide great data for training AI? There’s clearly more than enough data to have a decent attempt, and the technique is so well standardised and robust that surely it’s just crying out for automation?

A random anonymised chest X-ray taken from the NIH dataset. Take a look, and make a note of what you think you can see… there’s a test later.

Unfortunately, there is one small and inconvenient problem — humans.

Human radiologists are so bad interpreting chest X-rays and/or agreeing what findings they can see, that the ‘report’ that comes with the digital image is often either entirely wrong, partially wrong, or omits information. It’s not the humans’ fault… they are trying their best! When your job is to process thousands of black and white pixels into a few words of natural language text in approximately 30 seconds, it’s understandable that information gets lost and errors are made. Writing a radiology report is an extreme form of data compression — you are converting around 2 megabytes of data into a few bytes, in effect performing lossy compression with a huge compressive ratio. It’s like trying to stream a movie through a 16K modem by getting someone to tap out what’s happening in Morse code. Not to mention the subjectivity of it all.

Don’t believe me that radiologists are bad?

Let’s look at the literature…

Swingler et al showed that radiologists’ overall sensitivity was 67% and specificity 59% for finding lymphadenopathy on children’s x-rays clinically suspected for having tuberculosis. (That’s means they only found something about 2/3rds of the time even though they knew there was something wrong, but were only correctly finding a lymph node just over half the time.)

Taghizadieh et al showed that radiologists’ sensitivity was 67% and specificity 78% for finding a pleural effusion (fluid around the lung — solid white on an X-ray, you’d think quite hard to miss…).

Quekel et al found that lung cancer was missed in one-fifth of cases, even though in retrospect the lesions were entirely visible! In nearly half of these cases, cancers had again been missed at least twice on subsequent X-rays.

Thankfully, research does show that medical training makes one slightly better than the average student or lay person…

Satia et al showed that 35% of non-radiologist junior doctors were unable to differentiate between heart failure and pneumonia, 18% were unable to diagnose a normal CXR, 17% were unable to spot a 3 cm right apical mass and 55% were unable to recognise features of chronic emphysema. Senior clinicians performed better in all categories.

At first, this might seem quite alarming! You’d probably expect modern medicine to be a bit better than getting things sort of right up to 2/3rds of the time at best. Well, actually it’s worse than that…

Not only are radiologists really quite bad at writing accurate reports on chest X-rays, they also write entirely different reports to each other given the same chest X-rays. The inter-observer agreement is so low, it’s laughable — one study showed a kappa value of 0.2 (0 is awful, 1 is perfect). Another study just gave up and concluded that “in patients with pneumonia, the interpretation of the chest X-ray, especially the smallest of details, depends solely on the reader.” Subjectivity is as subjectivity does, I suppose.

A few days ago, I took to Twitter to conduct a simple (totally unscientific) experiment to prove this.

Twitter science. RT for exposure.

Hypothesis: No two radiologists will create the same report.

Method: Radiologists, please look at this CXR and type a report as a reply WITHOUT looking to see what others have said.

Hx: 54 yrs Male, non-smoker, 2 weeks SOB, outpatient pic.twitter.com/4lJhzEuRnq

— Hugh Harvey (@DrHughHarvey) December 18, 2017

I asked radiologists to look at a chest X-ray (taken from the anonymised NIH dataset) and tweet their report in response. I gave a brief fabricated history that wasn’t specific for any particular disease (54 years old, non-smoker, two weeks shortness of breath, outpatient) so as not to bias them towards any findings.

So how did everyone do? Here’s a few sample replies:

https://twitter.com/bati9val/status/943229525651001349

1/2: Spiculated-appearing Xmm LUL nodule, & additional bilat apical nodules. Prob L hilar & poss R hilar adenopathy. Lingular consol. Lungs

— Alan (@GammaCounter) December 19, 2017

Individually, people performed as expected. They made some correct and some probably incorrect observations, and some suggested further imaging with CT. But did people agree? No two suggestions were identical. Some were close, but no two reports mentioned exactly the same findings or came to the exact same conclusion. Reported findings ranged from infection, to adenopathy, to hypertension, to emphysema, to cancer to tuberculosis.

However, there was an overall trend that emerged. If you trawl through all the replies, there are certain findings that were picked out more often than others, and these included a left apical nodule, hyper-expanded lungs and an indistinguishable left heart border. I don’t know what the correct ‘read’ is for this chest X-ray (I even had a go myself, and wrote a different report to everyone else), but I would tend to agree with these three main findings. The labels from the NIH dataset were ‘nodule’ and ‘pneumonia’ as mined from the original report. Sadly, there is no follow up CT or further clinical information, so we shall never know the truth.

(Incidentally, the thread rather took a different turn, with medics from other professions joining in, offering rather humorous opinions of their own. I recommend you have a read if you want a laugh! And, yes, the radiologists did better as a group. Phew!)

What I find fascinating is how the reports could have changed by simply altering some of the surrounding metadata. If I had, for instance, given a history of smoking 40 cigarettes a day, would the reports have been far more concerned with emphysema and a lung cancer than the possible pneumonia? What if I had said the patient was 24 not 54? What if I had said they had alpha-1 anti-trypsin deficiency? What if this chest X-ray had come from sub-saharan Africa? Would tuberculosis then be the most common reported finding?

The interpretation of the image is subject to all sorts of external factors, including patient demographics, history and geography. The problem is even worse for more complex imaging modalities such as MRI or operator dependent modalities like ultrasound, where observer error is even higher.

Why does all this matter? So what if a chest X-ray report isn’t very accurate? The image is still there, so no data is really lost, is it?

The problem quickly becomes apparent when you start using the written report to train an AI to learn how to interpret the image. The machine learning team at Stanford have done exactly this, using 108,948 labelled chest X-rays freely available from the NIH. They proudly announced their results as outperforming a radiologist at finding pneumonia. Now, I’m all for cutting edge research, and I think it’s great that datasets like this are released to the public for exactly this reason…BUT we have to be extremely careful about how we interpret the results of any algorithms built on this data, because, as I have shown, the data is dirty. (I’m not the only one — please read Dr Luke Oakden-Rayner’s blog examining the dataset in detail.)

How is it possible to train an AI to be better than a human, if the data you give it is of the same low quality as produced by humans? I don’t think it is…

It boils down to a simple fact — chest X-ray reports were never intended to be used for the development of AI. They were only ever supposed to be an opinion, an interpretation, a creative educated guess. Reading a chest X-ray is more equivalent to an art than a science. A chest X-ray is neither the final diagnostic test nor the first, it is just one part of a suite of diagnostic steps in order to get to a clinical end-point. The chest X-ray itself is not a substitute for a ground truth. In fact, it’s only real purpose is to act as a form of ‘triage’ — with the universal clinical question being “is there something here that I need to worry about?”. That’s where the value in a chest X-ray lies — answering “should I worry?”, rather than “what is the diagnosis?”. Perhaps the researchers at Stanford have been trying to answer the wrong question…

If we are to develop an AI that can actually ‘read’ chest X-rays, then future research should be concentrated on three things:

  1. The surrounding metadata and finding a ground truth, rather than relying on a human-derived report that wasn’t produced with data-mining in mind. An ideal dataset would include all the patient’s details, epidemiology, history, blood tests, follow up CT results, biopsy results, genetics and more. Sadly, this level of verified anonymised data doesn’t exist, at least not in the format required for machine reading. Infrastructure should therefore be put into collating and verifying this metadata, at a bare minimum, preferably at scale.
  2. Meticulous labelling of the dataset. And I do mean absolutely painstakingly thoroughly annotating images using domain experts trained specifically to do so for the purposes of providing machine-learning ready data. Expert consensus opinion, alongside accurate metadata, will be demonstrably better than using random single-reader reports. Thankfully, this is what some of the more reputable AI companies are doing. Yes, it’s expensive and time consuming, but it’s a necessity if the end-goal is to be attained. This is what I have termed as the data-refinement process, specifically the level B to level A stage. Skip this, and you’ll never beat human performance.
  3. Standardising radiological language. Many of the replies I got to my simple Twitter experiment used differing language to describe roughly similar things. For instance ‘consolidation’ is largely interchangeable with ‘pneumonia’. Or is it? How do we define these terms, and when should one be used instead of the other? There is huge uncertainty in human language, and this extends to radiological language. (Radiologists are renowned in medical circles for their skill at practicing uncertainty, known as ‘the hedge’). Until this uncertainty is removed, and terminology agreed upon for every single possible use case, it is hard to see how we can progress towards a digital nirvana. Efforts are underway to introduce a standardised language (RadLex), however uptake by practicing radiologists has been slow and rather patchy. I don’t know what the answer is to this, but I know the problem is language!

Until we have done all of this, the only really useful value of AI in chest radiography is, at best, to provide triage support — tell us what is normal and what is not, and highlight where it could possibly be abnormal. Just don’t try and claim that AI can definitively tell us what the abnormality is, because it cant do so any more accurately than we can because the data is dirty because we made it thus.

For now, let’s leave the fuzzy thinking and creative interpretation up to us humans, separate the ‘art’ of medicine from ‘artificial intelligence’, and start focusing on producing oodles of clean data.

If you are as excited as I am about the future of AI in medical imaging, and want to discuss these ideas, please do get in touch. I’m on Twitter @drhughharvey

If you enjoyed this article, it would really help if you hit recommend and shared it.

About the author:

Dr Harvey is a board certified radiologist and clinical academic, trained in the NHS and Europe’s leading cancer research institute, the ICR, where he was twice awarded Science Writer of the Year. He has worked at Babylon Health, heading up the regulatory affairs team, gaining world-first CE marking for an AI-supported triage service, and is now a consultant radiologist, Royal College of Radiologists informatics committee member, and advisor to AI start-up companies, including Kheiron Medical.

Categories: OIG Advisory Opinions

Is Marital Status in a Febrile 5-year-old Child Important?

The Healthcare Blog - Tue, 01/09/2018 - 16:20

My pediatric practice is one which harkens back to days long ago when physicians knew their patients and pertinent medical histories by heart. My 81-year-old father and I were in practice together for the past 16 years; he still used the very sophisticated “hunt and peck” to compose emails. The task of transitioning to an electronic record system seemed insurmountable, so we remain on paper. Our medical record system has not changed in almost five decades. I would not have it any other way.

This past spring, he walked into my office shaking his head in disbelief after thumbing through a stack of faxes. “Can you believe this 16-page emergency room note has no helpful information about the patient?”

This was not a shock to me. The future of medicine will include robots who are paid to collect reams of useless data to provide nothing in the way of health or care. Regardless, the government and third-party payors will extoll upon the virtues of their inept system as life expectancy falls.

Fifty years ago, there was a close relationship between a physician and their patient grounded in years of familiarity. Physicians took a history, performed a physical exam, and developed an assessment and plan. Diagnosis in a child with fever would be descriptive, like Bacterial Infection, Otitis Media, Fever of Unknown Cause, or Viral Illness. Parents were advised to provide supportive care, involving clear liquids, fever medication, and follow up precautions if the child worsened.

At the dawn of the technological age, the effortless simplicity previously existing between physicians and patients has all but evaporated. It was traded away without our consent, relegating the role of physician to that of a data-entry clerk. Physicians are discouraged from synthesizing information and utilizing it to guide our decision making. Today, a 16-page document “appears” to contain crucial elements such as chief complaint, past medical and surgical history, medication list, and allergies, however, the information is then followed by more than a dozen pages of waste.

The particular case to which my father was referring involved a 5-year-old child with fever. The provider documented the sexual history of this child, whether he was single or married, and whether or not he had children of his own. My dad and I started chuckling as we contemplated collecting this kind of extraneous information from a child who had not even entered puberty. As one would suspect, our young patient was single, as in not married; he had no children (which is physiologically impossible), and his years of formal education were noted “not pertinent to his medical situation.” Interestingly enough, I volunteer at the school where this young boy attended kindergarten; his classroom was next door to the one with my second oldest child. Three of his classmates were out with febrile illnesses, however technology cannot incorporate this kind of alternative data.

We kept reading and laughing. Occupational history was recorded as not on file; running a bustling lemonade stand in his neighborhood apparently was not clinically relevant. It came as quite a relief that at the tender and impressionable age of five, this boy had managed to steer clear of regularly smoking cigarettes. It was comforting to discover he had never used smokeless tobacco either; and for some reason, I never thought to inquire about such things before (insert eye roll.) He also denied alcohol use, restoring my faith in the fact that not every youngster was consuming alcohol during their formative childhood years.

Just when I thought things could not get more absurd, I came upon the sexual history; contemplating whether or not a five-year-old child was engaging in consensual intercourse was nauseating. I reminded myself that data entry clerks were devoid of emotion and instead were tasked with collecting “critical” details to practice by protocol. Sexual history: Not on file.

The final summary and diagnosis section was the most entertaining part, which read: “primary diagnosis: none.”  Seriously, are you kidding me? No diagnosis? This is the future, technology will seal the fate of our profession as one entirely devoid of the need for any cognitive skills. This earth-shattering conclusion after sixteen (16!) pages of documentation was utterly astonishing. Despite the considerable time and effort invested asking a febrile five-year-old whether he was married or having consensual sexual intercourse in his spare time, little to nothing was provided in regard to healthcare.

At this point, my father and I laughed so hard that tears were running down our cheeks. There is no other reasonable response to the sheer waste of time, resources, and education invested in becoming a physician. Doctors have spent decades honing their clinical skills and should be entitled to choose the documentation method they find most effective and efficient. Some physicians find electronic records helpful and should be encouraged to use them. My pediatric practice will keep surviving on a shoestring, a prayer, and good old-fashioned paper. It warms my heart to know each chart note contains helpful information and not one human being leaves with NONE as their diagnosis.

Footnote: Page 16 states: “This chart is intended to document the majority of the information from this patient’s visit today. Other items, such as the patient’s care timeline, are reported elsewhere and should be reviewed to better understand this encounter.” (More eye rolling.)

By all means, if 16 pages did not cut it, twenty more should make sense of arriving at no diagnosis. Forgive me for not running out and requesting those records immediately.

Niran al-Agba is a pediatrician practicing in Washington state.

Categories: OIG Advisory Opinions

What Is An Abnormal Test Result?

The Healthcare Blog - Tue, 01/09/2018 - 12:09

Most teachers of evidence-based-medicine talk about tests as “positive, or negative”. A positive test is one in which the result of the test is abnormal; a negative test is one in which the test’s result is normal. A problem with this way of teaching about the value of test results is that often physicians and patients think there are only two possible test results, normal or not. However, test results are never just, “normal or abnormal”; test results may take on many values, not just two. ,

Researchers distinguish normal test results by performing the test in people who are well. For example, 100s of normal people will have blood tests done and the test results will vary over a narrow range. A serum potassium test result may be as low as 3.0 and as high as 4.0 in normal people, for example. An abnormal test result for potassium, then, is one whose value is greater than the highest in the range of values in normal people. But, the greater the potassium level, the more the diagnostic and treatment decisions may vary. In tesing, the magnitude of the result matters.

A key concept in testing is that the value of any test result may vary. The more abnormal it is, the more information it “contains” in terms of making a diagnosis. This may seem self evident, but failing to consider the absolute value of a test result is a common cause of missing the correct diagnosis in my experience.

The best way to understand this is to see an example. In the table below, I present a single test’s possible results. The test is PSA, or prostate specific antigen. It is a test used to find prostate cancer, but it is imperfect as the PSA test can be abnormal in diseases other than cancer.

If a physician, or you, just considers the test result as normal or abnormal, you will lose information about the value of the test. In the table, a high value (30, for example, in the first column of the table) means something different to you than a value of 20, or 10, or 5, even though all of those values are abnormal (any value greater than 2 in this example would be abnormal).

In the table, also, note that a test result value as high as 30 only occurs in people with cancer 1% of the time, which is a small percent chance. However, that level of the test never occurs in other diagnoses (in this example). Hence, a value of 30 means you have cancer. It is, in fact, a gold-standard test result at that level.

Any other abnormal test result value less than 30 in this example may increase the likelihood of having cancer, but those values do not mean for certain that you have cancer.

You can see from this table, then, that the actual test result value will have different meanings in terms of making a diagnosis. When we get to actual case examples in future blogs, you will see situations where a diagnosis was uncovered just by considering the information contained in the actual test result value.

My main point to you as a diagnostic decision maker is that you must know everything about your test results, including the exact value of every test’s results. Do not think of tests as just abnormal or not, know your test result values backward and foreword.

Categories: OIG Advisory Opinions

Are you in SF? What are you doing on Wednesday?

The Healthcare Blog - Mon, 01/08/2018 - 14:51

If you’re in San Francisco for JP Morgan Week, you can’t miss the hottest event focusing on new investment trends in health tech and the revolution in choice within the consumer landscape.

Check out the full agenda of our 4th annual WinterTech conference! Here is what’s happening during our WinterTech conference that makes it unique from every other event happening during JP Morgan Week:

  • Mark Ganz, CEO of Cambia Health giving a keynote presentation on how to create seamless health care experiences to meet the needs of consumers.
  • Bakul Patel, Associate Director for Digital Health at the FDA in a panel discussion on the opportunities, roadblocks, and regulations within the field of digital therapeutics.
  • Investment Strategies Past and Present: a look into 2017 trends, surprises, and flops. Plus predictions for 2018 by VC firms GE VenturesCanaanFifty YearsNEA, and B Capital Group.
  • Four chats between 4 VCs and their CEOs on their relationships, how they work together, and where their companies are going next.
  • Live demos from some of the most innovative companies in the digital healthcare space includingParsley HealthNeurotrackHabit, and much more!
  • Access to the Investor Breakfast where start-ups and investors discuss business models and explore portfolios.
  • Launch winners from previous years – hear what Healthvana, a patient engagement platform that delivers interpreted lab results; and Cardinal Analytx Solutions, which identifies next year’s new high cost members before a high cost event occurs, are up too since they appeared at Health 2.0.
Register today to get the latest on new heath tech investments, see live tech demos, and network with hundreds of health tech VCs, CEOs, and thought leaders.

Categories: OIG Advisory Opinions

A New Non-Partisan Panel to Monitor the President’s Health

The Healthcare Blog - Sun, 01/07/2018 - 11:36

The White House has announced that President Trump has scheduled an annual physical exam for Jan. 12. The President will go to Walter Reed National Military Medical Center in Bethesda, Md., the largest military hospital in the nation. White House press secretary Sarah Huckabee Sanders says Dr. Ronny Jackson, a rear admiral in the U.S. Navy who has served as physician to the President since 2013, “will give a readout of the exam after it’s completed.”

Some may have greeted this announcement with relief. Finally, concerns about the President’s slurred speech, overall mental health, crummy diet and obesity will be publicly addressed. Don’t get your hopes up.

A physical tends to be just that—an assessment of the physical not the mental. The evaluation of mental health in a standard physical is, to be polite, very cursory.

And while it is good that Trump at 71 will get a physical, he is under no obligation to reveal anything concerning that the exam turns up. When you are Commander-in-Chief and an Admiral reports on your exam, it is very clear that the Admiral had better be prudent about what gets said about the boss. Same goes for those on active duty at Walter Reed who perform the exam. Moreover, Trump has the same right to privacy that you or I do when we choose to get a physical or undergo any other medical procedure. It is up to him what he reveals to the rest of us.

The White House is well aware that they control what we will learn about the President’s health. And control the results they will.

There is a long history of prevarication, distortion, withholding and pretending when it comes to medical information generated when doctors examine a President. Enough to raise enormous doubt about what this White House will permit to be released. Those who served previous Presidents whose livelihood depended on keeping him in office never really let us know what they knew about the health troubles and risks of Wilson (serious stroke), Roosevelt (polio, cardiac disease), Eisenhower (massive heart attack), Kennedy (Addison’s disease), Johnson (bipolar disorder, heart attacks), Nixon (alcohol abuse, depression), Reagan (early signs of Alzheimer’s) or Clinton (hypertension, lipidemia).

We don’t see any easy solution to this problem, which applies to every modern leader who wields immense power. If health information is vetted by the President’s closest allies and confidantes, truth is likely to be hard to find.

Former President Jimmy Carter tried to address the issue in 1994. Under his Carter Center he helped form a Working Group on Presidential Disability, which proposed a non-partisan panel of doctors to monitor the president’s health. It was never created.

President Carter’s proposal needs to be revisited. A physical is not an assessment of someone’s ability to do the job of President. Presidents choose to get physicals for their own benefit. Congress needs to insist on a mandatory, independent, thorough, transparent annual medical assessment of Presidents and Vice Presidents for our benefit.

Arthur Caplan is the head of the Division of Bioethics at New York University Langone Medical Center.  Jonathan D. Moreno is a professor of medical ethics and health policy at the Perelman School of Medicine, University of Pennsylvania.

This blog post originally appeared in Forbes.

Categories: OIG Advisory Opinions