Category: THCB

Cancer and the Politics of Moonshots

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As many of the Republican and Democratic presidential candidates lament the high cost of healthcare and put forth how they aim to make it more cost effective, few have focused on the impact of out-of-pocket costs specifically for cancer patients. They should. One in every two men and one in every three women will get cancer at some point over their lifetime. As the U.S. population and American lifespans increase, this toll will have major financial ramification for everyone.

When fighting against the disease, cancer patients are often at the mercy of the pharmaceutical industry. Given Pfizer’s recent announcement that it plans to merge with Allergan, making it the largest pharmaceutical company in the world, many cancer patients are wondering what this will mean in terms of their cost of care. Pfizer, a giant in the cancer pharma space, already raised prices on 133 of its brand-name drugs last year, and they are not alone.  Big pharma has raised cancer drug prices up to 5000%. Recently ousted Turing CEO Martin Shkreli justified such hikes explaining, “I could have raised [prices] higher and made more profits for our shareholders, which is my primary duty.” The lack of focus on patients spawned outrage amongst patients, providers and even politicians, but the drug industry seems to be “in denial of the seriousness of its pricing problem.”

Granted, drug production takes years of research and can cost $350 million to get a single drug to market. Considering 95% of the experimental drugs will never see a pharmacy shelf, it might seem reasonable that the cost to patients is on the increase.  But contrary to the pharmaceutical industry’s claims, the cost of innovation is not the driver of drug prices. A study published in JAMA Oncology found that prices of cancer drugs are not tied to novelty nor to effectiveness, but rather set to what the market can bear. Here within lies the problem: if you’re a patient faced with a cancer diagnosis, wouldn’t you pay whatever the cost, no matter the price?

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Readmissions, Observation, and Improving Hospital Care

Ashish JhaReducing Hospital Use

Because hospitals are expensive and often cause harm, there has been a big focus on reducing hospital use.  This focus has been the underpinning for numerous policy interventions, most notable of which is the Affordable Care Act’s Hospital Readmissions Reduction Program (HRRP), which penalizes hospitals for higher than expected readmission rates. The motivation behind HRRP is simple: the readmission rate, the proportion of discharged patients who return to the hospital within 30 days, had been more or less flat for years and reducing this rate would save money and potentially improve care. So it was big news when, as the HRRP penalties kicked in, government officials started reporting that the national readmission rate for Medicare patients was declining.

Rising Use of Observation Status

But during this time, another phenomenon was coming into focus: increasing use of observation status.  When a patient needs hospital services, there are two options: that patient can be admitted for inpatient care or can be “admitted to observation”. When patients are “admitted to observation” they essentially still get inpatient care, but technically, they are outpatients.  For a variety of reasons, we’ve seen a decline in patients admitted to “inpatient” status and a rise in those going to observation status. These two phenomena – a drop in readmissions and an increase in observation – seemed related.

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Why Healthcare Costs Rise Faster Than General Inflation

Over the last few years, the latest buzz in the healthcare industry has been Accountable Care Organizations (ACOs), and the next wave will be the promotion of “value-based contracting”. These are similar approaches, different words.

Generally, an ACO is formed around a physician group or a hospital linked to physicians. The basic concept is for the provider system to be accountable for patients, and the providers are financially motivated to impact their patient population’s overall costs. Makes sense, right?

For the past 25 or so years, physicians have been linked to Independent Practice Associations, Medical Groups, and Management Services Organizations. Many of these provider organizations have had financial incentives tied to performance. Data have been available to assess physician performance. So what’s different now?

Today the Feds are re-emphasizing performance in their physician contracting under the new Medicare Access and CHIP Reauthorization (MACRA), which replaces the current reimbursement formula.

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Silicon Valley’s Healthcare Problem

flying cadeuciiSilicon Valley wants to love healthcare. The industry is enormous and full of inefficiency, which is to say, perfect for technology investment. So it comes as no surprise that venture money in healthcare technology startups has quadrupled since 2011 to $4.5BN in 2015. Moreover, the government wants to invite Silicon Valley-style innovation in healthcare. In January, CMS leaders stated that the next wave of EHR policy will focus on promoting startup innovation in healthcare by incentivizing open APIs and interoperability. Everyone agrees—so let’s just get going, right?

Here’s an important truth to recognize on the eve of what some like to call the “disruption of healthcare”: Silicon Valley and healthcare are fundamentally at odds.

In technology we fail fast, launch and iterate, proudly make mistakes and learn from them. In medicine, the first principle is “do no harm.” Entrepreneurs are obsessed with growth–exponential growth, hypergrowth, 10X growth–and the faster the better. Conversely, in healthcare organizations, progress is measured in months and years. My company is currently in Y Combinator, a three-month accelerator program. I have had phone calls with healthcare organizations that took longer than that to schedule.

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Ransomware, Interoperability, Power Outages, Natural Disasters, Oh My!

flying cadeuciiQuestion: What do ransomware, malware, the lack of medical record interoperability, power outages, floods, hurricanes and tornadoes have in common?

Answer:  They make it impossible for doctors to access their patients’ electronic medical records — which can have disastrous and costly consequences for individual patients, families and our society as a whole.

The irony is that this is an unintended consequence of one of the most successful, albeit forced, programs to quickly move an entire industry from paper records into the modern age of electronic records.  The theory was that when all providers keep electronic records and they are linked together via electronic networks, patient records will be instantly available anytime, anywhere patients require care.  Regrettably, it’s not that simple.

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Will Feeding Watson $3 Billion Worth Of Healthcare Payment Data Improve Its Decisions?

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On Feb 18, IBM announced its purchase of Truven Health Analytics for $2.6 billion. Truven collects and crunches payer data on medical costs and treatments. IBM will combine Truven’s data with recent other data acquisitions from the Cleveland Clinic’s “Explorys” and from Phytel, a software company that manages patient data. These data sets will be fed to Watson’s artificial intelligence engine in hope of helping doctors and administrators improve care and reducing costs. Truven’s data reflects more than 200 million patients’ payment records. Collectively, Watson will now have access to healthcare data on about 300 million patients.

Our question is whether healthcare payer data are so inaccurate and, worse, biased, that they are more likely to mislead than guide? Will the supercomputer’s semiconductors digestion of junk and contradictory information produce digital flatulence or digital decisiveness? On the other hand, despite our cautions, we also encourage IBM and Watson to continue their explorations with these data sets. There is much to learn and little to lose in trying, even if the incoming data are unusually messy, biased, and fragmented.

 Will Watson’s diet deliver more noise than knowledge?

First, as noted above, the best data we have—from electronic health records (EHR)–are often seriously flawed, incomplete and inaccurate. The reasons for this are known: patients are seen in many different facilities that can’t communicate with each other because of proprietary data standards and the government’s laissez faire non-insistence on interoperability. Also, patients (as Dr. House reminded us) often lie, use other people’s insurance cards, have confusing names, or have names that healthcare institutions mangle in fascinating and intricate ways. (Hospitals have up to 35 different ways of recording the same name, e.g., Ross Koppel, R Koppel, R J Koppel, Koppel R, Koppel R J, and mistyped or confused, R Koppell, Ross K etcetera) There are myriad other reasons EHR misrepresent reality, including the basic fact that we often don’t know what’s wrong with the patient until many tests are concluded (and even then), patient memories are faulty or the information is embarrassing, most elderly patients have many medications with confusing names and dosages, doctors often want to avoid diagnoses that may prematurely prohibit patients’ ability to return to work or, the opposite, allow some time off of work, etc. In addition, although discomforting for patients to realize, there’s massive ambiguity in medicine. Physicians often don’t know what the heck is going on but are forced to enter specific diagnoses in the EHRs, which can’t handle probabilities or ambiguity. They don’t accept “probably a heart attack but possibly just a muscle tear near the ribs” because the symptoms are so similar.

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Donald Trump’s Healthcare Problem

Screen Shot 2016-02-23 at 12.05.28 PMWhether you are elated, appalled, or just plain amazed that Donald Trump is the Republican primary front runner by a considerable margin, one thing should be clear: he’s not a policy guy.

So far, The Donald’s lack of policy specifics seems not to have hurt him. He’s successfully deflected the more searching debate questions, provided vague generalizations or given incomprehensible responses, and—when all else failed—insulted the debate moderators or his fellow Republican candidates.

So far, so good, for the Trump campaign. But is it time to change tactics?

As the number of competing candidates dwindles(So long, Jeb!),the focus in debates and interviews becomes sharper. With the original crowded field winnowed to just a handful,interviewers and debate moderators have time to probe a lot more deeply.And even if the questioners are relatively gentle, every other surviving candidate will be eager to pour scorn on policy statements that lack either substance or rationality.

Like Donald Trump’s healthcare proposals so far.

He’s said he wants the government to negotiate Medicare drug prices, he likes health savings accounts, he wants to be able to buy insurance across state lines, and he wouldn’t cut Medicare. And that’s pretty much it, except for one very big thing: he would “repeal and replace” Obamacare. But by what? “Something terrific” he says.

It’s easy to mock, but all of us – liberals and conservatives — should worry that we might just find ourselves with an incoming president trying to impose such an incoherent healthcare vision that our present system would look like a paragon of rationality.

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How Not to Research ACOs

flying cadeuciiIn Part I of this series I noted that we have almost no useful information on what ACOs do that affects cost and quality. I described two causes of that problem: The amorphous, aspirational “definition” of ACOs, and the happy-go-lucky attitude toward evidence exhibited by ACO proponents and many analysts. I showed how the flabby “definition” of ACO makes it impossible to operationalize this thing – to reduce it to testable components. And I asked why the health policy community let ACO proponents get away with such a vague description of the ACO. I said the answer lies in the permissive culture of the US health policy community. It is a culture that tolerates, even encourages, the promotion of vague concepts and a cavalier attitude toward evidence.

In this installment, I illustrate these problems – the vague definition of “ACO,” and loose standards of evidence – by examining a paper published last month by the Center for Health Care Strategies (CHCS) entitled, “Accountable Care Organizations: Looking back and moving forward.” In the third installment of this series I will describe the emergence of the health policy culture that tolerates intellectually flabby proposals and a devil-may-care attitude toward evidence.

I chose the CHCS paper because the organization that funded it, the Robert Wood Johnson Foundation, and the organization that wrote it are prominent advocates of managed care and its latest iteration, the ACO. The Foundation describes itself as “an early supporter of the idea that later became known as managed care” The Foundation announced last July it “has been supporting … ACOs for several years now.” CHCS was established two decades ago with support from the Foundation, and receives funding from organizations that promote managed care and ACOs.

Moreover, the paper’s authors and funders made it clear they hoped the paper would provide a useful update on what ACOs have accomplished and how they accomplished it. In its July 2015 announcement of the $20,000 grant that supported this study, the Foundation said the study would “inform stakeholders of progress to date by accountable care organizations.” CHCS’s paper claims it “identifies key lessons from ACO activities across the country to date” (p. 1).

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Apple and the 3 Kinds of Privacy Policies

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Why Are Apple’s Competitors Staying Silent On the iPhone Unlocking Fight? is the question of the day on tech blogs. The answer is hardly technical and may not be legal, it’s all about privacy policies and business strategy and it is very evident in healthcare.

There are three classes of privacy policy in healthcare and everywhere else:

Class 1 – “Apple will not see your data.” This is Apple’s privacy policy for ResearchKit and HealthKit and apparently for whatever data the FBI is hoping to read from the terrorist’s phone. Obviously, in this case the person is in complete control of the data and it can be shared only with third-parties that the person authorizes.

Class 2 – “We will see and potentially use your data but you will have first-class access to your data”. This is the kind of privacy policy we see with Apple’s calendar and many Google services. The personal data is accessible to the service provider but it is also completely accessible via an interface or API. In healthcare, the equivalent would be having the FHIR API equally and completely accessible to patients and to _any_ third-parties authorized by the patient. This is Patient Privacy Rights’ recommendation as presented to the API Task Force.

Class 3 – “We will use your data according to xyz policy and if you don’t like it, take your illness elsewhere.” This is pretty much how healthcare and much of the Web world runs today. We have limited rights to our own data. On the other hand, the services that have our data can sell it and profit in dozens of ways. This includes selling de-identified data. In Class 3, you, the subject of the data are a third-class citizen, at best. In many cases, the subject doesn’t even know that the data exists. See, for example, The Data Map.

We are so completely engulfed by Class 3 privacy policies that we have lost perspective on what could or should be. A Class 1 policy like Apple’s is widely seen as un-American. A Class 2 policy like PPR’s is indirectly attacked as “insurmountable”.

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A Reset For Workplace Wellness?

flying cadeucii“The way I see it, if you want the rainbow, you gotta put up with the rain.”– Dolly Parton

Sometimes, helpful perspective can be found in the most unexpected places. Ms. Parton may be better known for her achievements in country music, but her maxim also applies to certain aspects of the public dialogue on workplace wellness that have become a recurrent feature..

An example is a thread that has its roots in a blog invited two-part response counter-response (i.e., see the comments at the end of Part II) exchange between Al Lewis (aka whynobodybeliev) and myself that began November, 2014. The resumption of this exchange was initiated with my comments on a 12/4/15 post on this blog page from Ms. Dentzer, who noted the focus on return on investment that dominated the “debate” between Goetzel and Lewis on workplace wellness at the PHA Forum 2015.  Her post offered some questions for positioning future like-minded events in more looking forward ways. My 12/19/15 post, also on this page, offered a supplement to her formulation by urging wellness program implementers to also take stock of the empirical work that has been done to date on program impact. Indeed, it urged implementers to consider (re-) setting their sights toward the top end of what has been shown to be possible and referenced the success that Navistar achieved during the 1999-2009 period as a model. This, in turn, prompted another sharply worded response from Mr. Lewis, expressed in terms that were not only reminiscent of his counter-response noted above but have also come to typify much of his published commentary in this area, even on work that has met the test of peer-review.

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