The Evolution of Precision Health

Health 2.0 sat down with Linda Molnar to discuss the evolution of Precision Health, the imperatives at stake in a fast-paced field, and empowerment through big data. Linda has over 20 years in the field of Life Sciences and is responsible for a number of initiatives that further the field with start-ups, the feds, and for investors.

Her current endeavor is leading the upcoming Technology for Precision Health Summit in San Francisco alongside Health 2.0. “We’re never going to pull together all of this disparate data from disparate sources in a meaningful (i.e. clinically actionable) way, unless we talk about it” she says. “The Summit is an attempt to bring together the worlds of Precision Medicine and Digital Healthcare to realize the full potential of a predictive and proactive approach to maintaining health”.

Check out the full interview here.

As a bonus, save 25% off the standard admission to the Technology for Precision Health Summit by using discount code TPH25Register here!

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How the Republican Tax Cuts Will Impact the Health Care System

The U.S. tax system and health care are deeply intertwined. The Republican tax bills hurtling through Congress would make significant changes in this relationship.

The proposed changes, primarily a large cut in the corporate tax rate from 35 to 20 percent, would benefit health care (and most other) companies.

But none of the changes would, in the long run, benefit consumers, the public good, or public health. The major components of the proposed legislation are dangerously ill-conceived and ill-timed in the context of the overall economy and in particular health care policy and spending, which is projected to comprise 20 percent of the nation’s economy in 2025, up from 18.3 percent today.

That’s a difference and increase that reflects several trillion dollars of “additional” health care spending over the next decade. Amid this projected rise, the Trump administration and congressional Republicans propose to reduce the rate of growth of overall federal government spending and shift a sizable portion of health spending to other government entities and programs. These include the Pentagon, national security, homeland security, infrastructure projects, and—most notable in the context of the tax bills—a tax cut for corporations and upper income Americans.

It doesn’t and won’t add up—unless two (unlikely) things happen: (1) the economy grows at twice to three times the rate most economists predict and (2) the rate of growth in health spending is dramatically constrained.

Absent both, the Republican tax bills will cause the annual federal budget deficit and the nation’s long-term debt to balloon even more than already forecast.

This outcome is highly irresponsible and could lead to adverse economic consequences, especially in the event of a major war, terrorist attack, natural disaster (such as a pandemic) or weather/climate-change-related event.

In addition, over the long run, excessive deficits and debt are a tax and dangerous burden on our children and grandchildren during a period in which fewer workers will be subsidizing a growing number of senior citizens.

What follows is Part 1 of a series of pieces on the Republican tax legislation’s health care components. This piece deals with the federal budget deficit, national debt, and projected health spending. Part 2 will discuss the proposed elimination of the individual mandate to purchase insurance under the ACA—if that proposal remains in the proposed legislation. As of this writing, it’s in the Senate tax bill but not the House bill. The White House and Republicans have sent mixed signals on the fate of the mandate over the past two weeks.

Deficit spending, debt and health care

As has been widely reported, the tax legislation would add an estimated $1.5 trillion to the national debt over the next decade (some analyses show it closer to $2.2 trillion). Republicans dismiss this as a trivial amount and say economic growth, spurred primarily by the corporate tax cut, will more than make up for it.

Many independent and Democratic-affiliated analysts disagree. They argue that (1) there’s no proof the corporate tax cut will add more than a fraction (less than 1%) to economic growth over the next decade, and (2) that an increase in deficit spending, and thus the national debt, at this point is greatly at odds with the current debt load, national priorities, overall projected government expenditures, and the government’s commitments to provide health insurance and care to upwards of 120 million Americans.

The national debt is currently $20 trillion. That’s more than triple the debt in 2000, when it was $6 trillion. Federal government spending (our tax dollars!) to pay the interest on that debt varies from year to year. It mostly trends between 5 and 10 percent of the federal budget each year. In 2017 it was 6.5 percent. In 2018 it will be 8 percent—$315 billion of a $4 trillion federal budget.

For a host of reasons, interest on the national debt is projected to be the fastest growing federal expense over the next decade. It’s projected to rise every year over the next decade to $787 billion by 2026 and in that year comprise 12.2 percent of the federal budget.

Economists track the national debt in another way, too: as a percentage of overall GDP. That’s considered a more robust and meaningful measure for technical reasons. The table below indicates what many observers believe is a very precarious situation: our nation’s debt is now at around 100 percent of GDP. The GDP was $19.4 trillion in 2017.

The only other time it was that high was during and right after World War II.

And deficits and the debt are poised to rise further. If current tax receipts and federal expenditures, including for health care, were to continue at a pace equal to the past few years (often referred to as the baseline), CBO forecasts a federal budget deficit of close to $1 trillion in 2024.

In CBO’s words: “The projected rise in deficits would be the result of rapid growth in spending for federal retirement and health care programs targeted to older people and to rising interest payments on the government’s debt, accompanied by only moderate growth in revenue collections. Those accumulating deficits would drive up debt held by the public from its already high level to its highest percentage of gross domestic product (GDP) since shortly after World War II.

As is well known by health policy folks, Medicare and Medicaid account for bulk of health spending by the federal government (again, our tax dollars). And there’s no let up in sight.

As most recently projected by CMS’s Office of the Actuary, Medicare spending will increase between 7.1 and 7.6 percent annually from 2019 to 2025. That program alone will make up 18.8 percent of the federal budget in 2019 rising to 21.4 percent in 2025.

Medicaid spending is projected to increase around 6 percent a year between 2019 and 2025, and make up 9.2 percent of federal government spending in 2019 and 9.5 percent in 2025.

Other tax-supported health care spending is also forecast to rise, including for the VA and active military, CHIP, tax dollars devoted to federal employees’ health insurance, and subsidies/tax credits for people who enroll in the ACA exchanges.

CBO issued a report in September taking stock of several of these avenues of expenditures over the next decade—again, as a baseline assessment not factoring in any change in law.

The tax exclusion for employer-based coverage (which, by the way, is among the largest tax breaks in the current tax code) will “cost” the government $297 billion in 2018, rising to $475 billion in 2027. Cumulative cost 2018-2027: $3.8 trillion.

As an aside, the idea of limiting this exclusion has been debated for years since it is regressive; it benefits upper-income people far more than lower-income because upper-income people have, on average, richer benefits. It benefits people who buy coverage on their own and the uninsured not at all. A cap on the exclusion at a certain actuarial level could save between $50 and $100 billion a year. But that is politically unlikely to happen for the foreseeable future.

Tax credits for those who purchase coverage in the ACA exchanges will cost the government $41 billion in 2018, rising to $62 billion in 2027. Cumulative cost 2018-2027: $531 billion.

Medicaid coverage triggered by the ACA’s Medicaid expansion will cost the government $76 billion in 2018, rising to $143 billion in 2027. Cumulative cost 2018-2027: $1 trillion

There are other commitments and costs as well, such as for CHIP (cumulative 2018-2027 spending: $64 billion) and the cost-sharing reduction payments (cumulative over the next decade: $99 billion, if those payments get restored in Congress as expected).

All totaled, the tax benefits in existing law plus commitments and expenditures under the ACA for added coverage for the non-Medicare population add up to $654 billion in 2018. That is forecast to rise to just over $1 trillion in 2027. The cumulative cost 2018 to 2027: $8.2 trillion.

And don’t forget: 30 million Americans under age 65 are expected to remain uninsured each year over the next decade. If we choose as a society to extend coverage further—via Medicaid, the ACA exchanges or new programs—where does the money come from?

Conclusion

The U.S. spends a whooping amount on health care—across the board. Its’ commitments in this area of the economy are huge. Health spending is forecast to rise at a rapid clip over the next decade. Without substantial changes in the way we pay for health care that leads to a reduction in the rate of increase, rising expenditures are unavoidable and become unsustainable if other societal needs are to be met.

Tax cuts and reforms that add to deficit spending and seek to reduce health care spending growth by (a) slashing budgets, (b) eliminating coverage or benefits and/or (c) shifting costs to consumers—without making major structural changes in the way we pay for care and how much we pay—are a dereliction of fiduciary duty.

In this context, the Republican tax bills and the Trump administration’s 2018 budget are bad law and bad policy and should be rejected.

 

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A New Gimish Model of Complex Disease?

The appeal of precision medicine is the promise that we can understand disease with greater specificity and fashion treatments that are more individualized and more effective.

A core tenet (or “central dogma,” as I wrote in 2015) of precision medicine is the idea that large disease categories – like type 2 diabetes – actually consist of multiple discernable subtypes, each with its own distinct characteristics and genetic drivers. As genetic and phenotypic research advances, the argument goes, diseases like “type 2 diabetes” will go the way of quaint descriptive diagnoses like “dropsy” (edema) and be replaced by more precisely defined subgroups, each ideally associated with a distinct therapy developed for that population.

In 2015, this represented an intuitively appealing idea in search of robust supporting data (at least outside oncology).

In 2017, this represents an intuitively appealing idea in search of robust supporting data (at least outside oncology).

The gap between theory and data has troubled many researchers, and earlier this year, a pair of cardiologists from the Massachusetts General Hospital (MGH) and the Broad Institute, Sek Kathiresan and Amit V. Khera, wrote an important – and I’d suggest underappreciated – commentary in the journal Circulation that examined this very disconnect, through the lens of coronary artery disease (CAD).

(Disclosures: I’m Chief Medical Officer of DNAnexus, a cloud genomics company, and my training at MGH overlapped with Kathiresan’s.)

Do CAD patients “actually have one of many disease subtypes,” each with its own “distinct driving pathway” and ideal therapeutic approach, they asked, or is CAD a “quantitative blend of causal risk pathways” – essentially, a gimish, with slight defects in multiple pathways collectively contributing to disease manifestation? (See Figure 1.) Kathiresan and Khera tell me they’ve often described these as the “fruit salad” and “smoothie” models of disease, respectively.

Figure courtesy S. Kathiresan, used with permission.

Figure 1: “Fruit Salad” vs “Smoothie” Model Of Complex Disease.

Reviewing the CAD literature, Kathiresan and Khera conclude that while “precision medicine will in fact identify a small subset of individuals in whom an identifiable driving pathways accounts for much of their risk of CAD,” for “the vast majority of CAD patients, it is a quantitative blend of causal processes that underlies disease” – i.e. a smoothie.

In other words, for the vast majority of CAD patients, the disease reflects a collection of contributing genetic (as well as, of course, environmental) factors, a mix of slight defects that collectively nudge the body towards disease in what Kathiresan and Khera call “a probabilistic fashion.”

The implications of this, Kathiresan and Khera argue, is that there probably will not be a “taxonomic revolution in complex disease” – a clarifying subdivision of a complex disease into distinct subtypes – although there will be an opportunity to identify the “causal-risk pathways that contribute, albeit to varying degrees,” to complex diseases like CAD.

The authors point out that both rare and common genetic variants associated with CAD seem to involve the same molecular pathways, and the “vast majority of cardiovascular therapeutics in use today have demonstrated benefits across subgroups.” Perhaps the most visible example of this are the statins, which initially were demonstrated to improve the LDL (“bad cholesterol”) levels in patients heterozygous for familiar hypercholesterolemia (an example of a mutation in one gene profoundly impacting CAD risk); yet statins also proved to reduce CAD risk in the vast majority of patients whose disease was not caused by a single powerful mutation — sophisticated subtyping of disease was not required.

If Kathiresan and Khera are correct, and if their hypothesis extends beyond CAD, it means we should think carefully about reflexively assuming diligent data collection will permit us to subdivide complex disease – like multiple sclerosis and Alzheimer’s – into biologically and therapeutically distinct subcategories. At the same time, their argument suggests we should aggressively pursue robust molecular clues that have been identified, as pharmacologically targeting the products of genes with seemingly small effect could still have major impact, as the statin experience demonstrates. Individual rare variants with clear phenotypic effect might be particularly helpful in pointing out high value targets. Furthermore, based on Kathiresan and Khera’s hypothesis, drugs developed based on these targets might be expected to impact most patients, not just a select few. In short, while comprehensive genetic and phenotypic evaluation might be required to identify patients with high impact rare variants, once identified, these variants can lead to the development of a new medicine that has generalized utility. In other words: precision research, blockbuster development.

How far beyond CAD does Kathiresan and Khera’s hypothesis extend? For many conditions, after all, existing drugs seem to work for only a minority of patients; it was this very dilemma that motivated much of precision medicine. Surely, for many psychiatric conditions, some kind of improved “digital phenotyping” must be helpful, as Mindstrong Health President and Co-founder (and former NIMH Director) Tom Insel recently argued. And where do immune-related conditions like rheumatoid arthritis, inflammatory bowel disease, and multiple sclerosis fit in – are there biologically distinct, therapeutically relevant subtypes here?

For rare genetic diseases, precise, molecular-defined treatments will likely remain the goal, although here, too, we might be surprised. At this year’s JP Morgan meeting, for example, Vertex CEO Jeff Leiden expressed his hope that most patients with cystic fibrosis – a disease which can be caused by any of a staggering number of different mutations in a single large gene – could eventually be treated with a single (combination) drug regimen, a view reinforced by promising data that arrived this summer, as FORBES colleague Matthew Herper reported. This is similar to what’s occurred for hepatitis C, with the advent of single (combination) regimens that are effective across all viral genotypes, obviating the need for obtaining such molecular assessment in most patients.

At first blush, oncology looks like the most obvious exception to this hypothesis, and seems like a discipline where precise subtyping and identification of unique drivers has truly revolutionized the field, leading to relatively specific treatments that target specific “driver” genes and pathways. Yet, Kathiresan and Khera suggest that immune-oncology research may prove “potentially even more influential.” Immunotherapy, they argue, was born from “recognition of a key causal pathway,” and is “largely agnostic to the specific driver mutation of an individual cancer cell,” and thus may “have utility across a broad range of cancer subtypes.”

Bottom Line

Until now, precision medicine has been animated by the belief that detailed genetic and phenotypic analysis will dissect complex diseases into distinct and more tractable subtypes. Kathiresan and Khera’s recent commentary asks whether we should question this assumption, and suggest that most complex conditions are likely to be caused by a collection of core causal factors, which blend in any given individual to cumulatively cause the disease. The authors endorse the detailed molecular identification of these factors, and anticipate that targeting some of these factors could result in generally effective treatments for many suffering from the condition – though how to pick which of the many potential factors to target seems less obvious; perhaps loss-of-function variants with a clear phenotype could represent a useful starting point.

If Kathiresan and Khera are correct, precision medicine may not produce customized cures for each patient – but instead offer the hope that in elucidating the complex genetic architecture of disease, we will be able to identify and develop novel therapeutics each offering benefit to large numbers of suffering patients.

It’s an interesting theory – but so was the one it hopes to replace. As always in science, we’ll enjoy the conversation, but look for the data.

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Practicing Medicine While Black (Part II)

Managed care advocates see quality problems everywhere and resource shortages nowhere. If the Leapfrog Group, the Medicare Payment Advisory Commission, or some other managed care advocate were in charge of explaining why a high school football team lost to the New England Patriots, their explanation would be “poor quality.”

If a man armed with a knife lost a fight to a man with a gun, ditto: “Poor quality.” And their solution would be more measurement of the “quality,” followed by punishment of the losers for getting low grades on the “quality” report card and rewards for the winners. The obvious problem – a mismatch in resources – and the damage done to the losers by punishing them would be studiously ignored.

This widespread, willful blindness to the role that resource disparities play in creating ethnic and income disparities and other problems, and the concomitant widespread belief that all defects in the US health care system are due to insufficient “quality,” is difficult to explain. I will attempt to lay out the rudiments of an explanation in this essay.

In my first article in this two-part series, I presented evidence demonstrating that “pay-for-performance” (P4P) and “value-base purchasing” (VBP) (rewarding and punishing providers based on crude measures of cost and quality) punish providers who treat a disproportionate share of the poor and the sick.

I begin this second installment by presenting some of the evidence indicating that providers who treat a disproportionate share of the poor and the sick suffer from fewer resources, and that P4P and VBP (hereafter just VBP) worsen resource disparities. I will then argue that this outcome is the direct result of a habit, now deeply ingrained among health policy experts and managed care advocates, of calling resource-related problems “quality” problems. I will describe instances of this behavior that occurred early in the history of the “quality” crusade that began in 1999 with the publication of To Err is Human by the Institute of Medicine (IOM).

First do some harm

I closed my last article noting that CMS’s Hospital Readmission Reduction Program (HRRP) may be killing up to 5,000 seniors with congestive heart failure (CHF) every year. Under the HRRP, CMS tracks readmissions for CHF, heart attack, pneumonia and a few other diagnoses. Readmission rates above the national average for patients with these diagnoses are punished by reductions in all Medicare payments to the “bad” hospitals. The savings from the penalties inflicted on the “bad” hospitals are redistributed to the “good” hospitals.

We have no research yet on the mechanism that might cause the HRRP program to harm CHF patients, but we know this much:

  • Research has established that the HRRP as well as other VBP programs punish providers who serve a high proportion of the nation’s sickest and poorest patients, and
  • this terrible outcome is caused by CMS’s inability to adjust readmission rates and other “quality” measures, as well as cost measures, to reflect accurately factors outside clinic and hospital control, the most important of which are the health status and incomes of their patients, and the resources available to the clinics and hospitals.

The result of these and other VBP programs (ACOs, “medical homes,” MACRA, and possibly bundled payments) is that hospitals and clinics serving the nation’s poorest and sickest people are being drained of vital resources.

Harming the poor to improve “value”

The November 2017 edition of Medical Care contains a paper demonstrating that two Medicare VBP programs, the HRRP and the Hospital Value-Based Purchasing (HVBP) program, both implemented in 2013, have hammered hospitals in poorer communities. [1] The authors noted at the beginning of the paper, “Previous studies showed that the … HRRP and the … HVBP disproportionately penalized hospitals caring for the poor.” They stated their goal was to determine how this difference in penalties affected the financial performance of poor hospitals compared with better-off hospitals.

The paper compared the financial performance of hospitals serving the eight-state Mississippi Delta Region with non-Delta-region hospitals over the period 2008 through 2013. The authors, Hsueh-Fen Chen et al., used two measures of financial performance: Operating margin (income from hospital services minus the cost of those services) and total margin (income from all sources minus all costs). They selected the Delta Region because it is much poorer than other regions in the country. [2]

The authors found that Delta hospitals started and ended the study period (2008-2013) with fewer resources than the non-Delta hospitals – they served more Medicare and Medicaid patients, and had lower operating and total margins over the entire study period. Figures 1 and 2 show the changes in operating and total margins for the two groups of hospitals.

 

Figure 1 Delta hospitals and other hospitals in the United States 2008-2014

 

Figure 2 Delta hospitals and other hospitals in the United States 2008-2014

Figure 1 is the more revealing of the two. It shows that both groups of hospitals suffered negative or zero-percent operating margins over the study period, but Delta hospitals suffered substantially worse margins. The Delta hospital margins plummeted after 2010 to a low of minus 10 percent by 2013, while the non-Delta hospitals’ margins declined only slightly after 2011to a low of minus 1 to 2 percent. The authors surmise that margins plummeted in the two or three years prior to the implementation of the HRRP and HVBP programs in 2013 because hospitals, knowing those programs were coming, spent money on infrastructure and staff to attempt to score well on the measures used by those programs. Here is how the authors put it: “The growing gap in financial performance between the two hospital groups is likely a result of both the amount of penalties incurred from HRRP and HVBP, and the expenditure from increased investments in infrastructure for reducing readmissions and improving quality of care and the patient experience.” [3]

The authors concluded: “Policy makers should modify these two programs to ensure that resources are not moved from the communities that need them most.” “Modifying” these programs is not the solution. They should be terminated, and more resources should be funneled into to the Delta hospitals and others serving a disproportionate share of the sick and the poor.

To err is human, especially if you’re a managed care advocate

The managed care movement has exhibited a disdain for evidence since its birth in the early 1970s (see my discussion of the culture of managed care here) One manifestation of this cavalier attitude toward evidence is the problem I’m discussing in this series – the willful blindness to insufficient resources that has characterized managed-care-think since at least the publication of To Err is Human by the IOM (since renamed) in 1999. That report offered four explanations for why somewhere between 44,000 and 98,000 “preventable deaths” occur annually in US hospitals. Not one of those four explanations dealt with resource shortages. Here is how the Commonwealth Fund, a zealous proponent of managed care, articulated those four explanations in a 2005 review  of the impact of To Err is Human:

The IOM report provided a blueprint for reducing medical errors, naming four key factors that contribute to the epidemic of errors. First, fragmentation and decentralization of the health care system may create unsafe conditions for patients and impede patient safety efforts. Second, licensing and accreditation processes give insufficient attention to preventing errors. Third the medical liability system, which discourages physicians from admitting mistakes, impedes systematic efforts to uncover and learn from errors. Fourth, third-party purchasers of health care offer little incentive for health care organizations and providers to improve safety and quality.

Not one word about whether insufficient resources, or misallocation of resources, might contribute to some of those “preventable deaths.” Not surprisingly, given this one-eyed diagnosis of the problem, the IOM did not recommend more resources or better distribution of resources (for example, more spending on nurses and less spending on administrative costs generated by managed care schemes). Instead, they called for “transformation” of our “delivery system” via greater consolidation of our already highly concentrated system, more pay-for-performance schemes, installation of electronic medical records across the country to facilitate the implementation of P4P, and studies of alternatives to malpractice suits.

Many studies have documented a correlation between insufficient resources and negative health outcomes. An inverse correlation between nurse-to-patient ratios and adverse events in hospitals, for example, is well established (see Aiken et al. and a review of the literature by the Agency for Healthcare Research and Quality ). Just as our commonsense tells us a high school football team will lose to a professional football team, so it tells us that inequitable resource distribution will profoundly affect performance by health care institutions and professionals. What possible excuse did the IOM have for ignoring the resource issue in To Err Is Human and their 2001 sequel, Crossing the Quality Chasm? If I could rewrite those books, their titles would be Conflating Quality with Insufficient Resources is To Err Big Time, and Crossing the Resource Chasm.

Misuse of the RAND paper

The IOM is to the managed care movement what St. Paul was to Christians: An indisputable authority on the religion. (I have no idea what the IOM did to warrant that status, but they unquestionably have it among the faithful.) The IOM’s willingness to deny the role that insufficient resources plays in “preventable” adverse outcomes in hospitals has deeply influenced the faithful ever since. An influential paper published by Elizabeth McGlynn and colleagues at RAND four years after the publication of To Err reinforced the principle, first clearly established by the IOM, that blindness to resource disparities is acceptable, perhaps even admirable.

The paper by McGlynn et al. was very useful. It analyzed the relative prevalence of over- and underuse. They reported that underuse occurred four times as often as overuse. “[W]e found greater problems with underuse (46.3 percent of participants did not receive recommended care …) than with overuse (11.3 percent of participants received care that was not recommended …),” the authors concluded. McGlynn et al. found, for example, that only 58 percent of stroke patients were “on daily antiplatelet treatment”; only 29 percent of suicidal patients with “psychosis or addiction or specific plans to carry out suicide” had been hospitalized; and only 5 percent of alcoholics had been “referred for further treatment.”

You might think this paper, having documented so much underuse, would have been seen by managed care advocates as a repudiation of the assumption most fundamental to their creed – that US health care costs are high because the fee-for-service system induces overuse. Nope. Instead managed care buffs seized on the paper’s underuse data as proof that “quality” is bad and doctors are to blame. The title of the paper, “The quality of health care delivered to adults in the United States,” and the authors’ interpretation of their findings (they recommended closer supervision of doctors) bear some of the blame.

Managed care buffs wasted no time distorting the meaning of this paper. Shortly after it was published, I heard a lobbyist for the Buyers Health Care Action Group, a coalition of large employers in Minnesota, characterize this paper before a committee of the Minnesota legislature as follows: “Your odds of getting correct treatment when you walk into your doctor’s office are 50-50, no better than a coin flip.”

Today the deceptive practice of measuring the consequences of resource disparities but not the resource disparities themselves, and blaming the consequences on doctors and hospitals, is at epidemic levels. [4] This practice generates above-average blame and financial punishment for minority providers and the hospitals and clinics that serve a high-proportion of minority patients. This deceptive practice in turn leads to another deceptive practice: Recommending better “quality” measures to catch all those bad doctors, especially the black and brown doctors. And ‘round and ‘round the cycle goes.

A paper by Andreea Creeanga et al. illustrates both practices – blaming high-minority providers for problems caused in whole or in part by insufficient resources, and declaring that the solution is more measurement, not a better allocation of resources. Creeanga et al. sought to determine the rate of complications associated with childbirth in hospitals serving primarily black, Hispanic and white patients. The authors found higher rates of adverse events in the black hospitals. “Twelve of 15 indicator rates were highest in black-serving hospitals,” the authors wrote, “with rates of puerperal infection, urinary tract infection, obstetric embolism, puerperal cerebrovascular disorders, blood transfusion, and in-hospital mortality being considerably higher than in either white- or Hispanic-serving hospitals.” And what did the authors propose to do about this? Why, measure more, of course! “[B]etter obstetric quality-of-care measures are needed,” they intoned. What about resource disparities, you ask? Silly you. The authors made no recommendation. This is a “quality” problem, don’t you know.

Leapfrog should take the Hippocratic Oath

A decade ago, as the evidence-free P4P fad was taking off, a few intrepid observers warned that P4P could have destructive consequences, including worsening of racial disparities. Lawrence Casalino et al. were among those who issued a clear warning early on. In the title of a 2007 paper  for Health Affairs they asked, “Will pay-for-performance and quality reporting affect health care disparities?” Their answer was yes: “P4P and public reporting can have serious unintended consequences, and one of these consequences may be to increase health care disparities.” Those “unintended” consequences arrived quickly. The damage done by VBP to the providers who take care of more than their share of the sickest and the poorest is now obvious.

And yet the Leapfrog Group, MedPAC and other groups and individuals who led the P4P bandwagon in the early 2000s and who flog VBP now refuse to acknowledge the damage their arrogance has caused. Some even go to the trouble of accusing their critics of stupidity or greed. In a recent article for the Harvard Business Review, posted on THCB , Leah Binder, CEO of the Leapfrog Group, and three other business representatives were willing only to concede that VBP measures “may have rough edges.” They criticized “some doctors” for daring to criticize VBP, and argued we “can’t wait for quality measures to be perfect.” And why is that? Because of the “widely acknowledged … quality-of-care issues,” said Binder et al.

Binder et al.’s “logic” is circular and faith-based:

  • Quality is deemed to be terrible based on studies that use crude measures of cost and quality and which, therefore, cannot distinguish the influence of resource shortages from the influence of poor quality;
  • because quality is so terrible, something must be done, even if it might harm the sickest and the poorest;
  • so P4P/VBP is unleashed, it worsens resource disparities, which in turn worsen disparities in health outcomes such as infections following childbirth;
  • these outcome disparities are then used to start the circular reasoning all over again: These worse outcomes are deemed to be evidence of inferior “quality,” and, in the strange world manage care advocates live in, this calls for even more crude measurement, not more resources.

What is the solution to this irrational groupthink? This is grist for a much longer essay. My short answer is that health policy researchers, and managed care proponents in particular, should mentally take the Hippocratic Oath every day. That might induce them to recognize that demanding that doctors honor the Hippocratic Oath while they themselves honor no similar principle is hypocritical. Daily recitation of the Hippocratic Oath might induce managed care zealots to recognize that demanding that doctors and patients practice evidence-based medicine while they practice evidence-free health policy is hypocritical, leads to intellectual laziness, and, most importantly, inflicts harm.

[1] The Hospital Value-based Purchasing program purports to measure both quality and cost. Quality is measured by process and outcome measures and patient “satisfaction” surveys. The surveys have been shown to discriminate against hospitals that serve a disproportionate share of minority patients. According to a RAND study ,”Hospitals with a high proportion of minority patients received lower overall HCAHPS [Hospital Consumer Assessment of Healthcare Providers and Systems] VBP scores than hospitals with predominately White patients.”

[2] Hsueh-Fen Chen et al. described the Delta Region in grim terms. “The Census Bureau has noted that the Delta Region is among the most socioeconomically disadvantaged areas in the United States. This region is largely rural with a large proportion of minority and underserved individuals, and has high rates of poverty, unemployment, chronic diseases, obesity, physical inactivity, food insecurity, and mortality, and low-birth weights.” As if this weren’t bad enough, the authors continued: “The Delta Region already has a significant physician shortage and poor access to care.  Given the limited health care infrastructure and poor population health, Delta hospitals are … more likely to be financially vulnerable under HRRP and HVBP.”

[3] This paper on the Delta hospitals is a rare example of research into the costs doctors and hospitals had to incur to participate in managed care schemes. Since the birth of the managed care movement a half-century ago, the American health policy community has shown virtually no interest in measuring the additional costs imposed upon our health care system by managed care fads. Proponents of HMOs, gate-keeping, report cards, ACOs, “medical homes,” electronic medical records, P4P, and VBP demanded that lawmakers and payers adopt these programs without the faintest idea what these fads would cost payers (taxpayers, premium payers and out-of-pocket payers) and providers.

Unfortunately, even this paper on the Delta hospitals is insufficiently precise. The authors did not attempt to determine what portion of the financial losses suffered by hospitals since the enactment of the ACA can be attributed to penalties imposed by the HRRP and HVBP programs and what portion can be attributed to the extra costs hospitals incur to improve their HRRP and HVBP scores.

[4] Here are other examples of “experts” who claimed, either explicitly or by referring to the McGlynn paper in an endnote, that the paper measured physician quality:

·       “Studies have documented that nearly one-half of physician care in the United States is not based on best practices.” (Robert Smoldt and Denis Cortese, “Pay-for-performance or pay for value,” Mayo Clinic Proceedings, 2007;82:210-213)

·       “Physicians deliver recommended care only about half of the time….” (Richard Hillestad et al., “Can electronic medical record systems transform health care?….” Health Affairs 2005;24:1113-1117)

·      “[D]espite the extensive investment in developing clinical guidelines, most clinicians do not routinely integrate them into their practices.” (Dan Mendelson and Tanisha Carino, “Evidence-based medicine in the United States.,…” Health Affairs 2005; 24:133-136).

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Who Owns Your FitBit Data? Biometric Data Privacy Problems

By JASON CHUNG

The following blog post is adapted from a talk the author gave at the “Data Privacy in the Digital Age” symposium on October 26th sponsored by the U.S. Department Health and Health and Human Services.

Today, I’ll be focusing on the data privacy issues posed by sports wearables, which I define to include both elite systems such as WHOOP or Catapult and more consumer-oriented products such as Fitbits, and why the U.S. needs an integrated federal regulatory framework to address the privacy challenges posed by private entities commercializing biometric data.

Sports wearables have evolved from mere pedometers to devices that monitor heart rate and sleep, tell athletes how to maximize recovery, and even track food intake and sexual activity – all uploaded to the cloud.

These technologies are now ubiquitous and have wide appeal to consumers – in fact, I’m wearing a Fitbit right now.

But these devices raise several key problems for consumers that are not yet being adequately addressed by the U.S. legal and regulatory system.

Employee Freedom

I begin with what will likely be the biggest impending battle over sports wearables: how employers may use them as a potential economic weapon and restrict employee freedom.

Biometric data isn’t just for personal, voluntary use anymore but is being increasingly used for employee monitoring and evaluation.

In the sports domain, companies such as WHOOP are using wearables to track individual athletes’ data – and sharing it with not only athletes but coaches and other team officials as well.

At the professional level, this has already raised some eyebrows. But professional athletes have high visibility and bargaining power through sports unions, making their use of wearables subject to collective bargaining agreement negotiations.

For example:

  • The NFL Players’ Association signed a deal with WHOOP to make it the Officially Licensed Recovery Wearable of the NFLPA and allow players to commoditize their own data
  • In their new CBA, the NBA and NBA Players’ Association agreed to terms protecting the right of individual players to decline the use of wearables at any time

This is great for pro athletes but most U.S. workers don’t have robust unions protecting them. Employers increasingly offer sports wearables as part of efforts to promote employee wellness and bring down health insurance costs. BP uses data provided by sports wearables to adjust premiums for their employees, and CVS has actually fined employees for failing to disclose health vitals such as weight and body fat percentage.[1]

And this is encouraged to a certain extent by the federal government.  In June 2013, the Department of Labor and HHS shared Affordable Care Act wellness program regulations which allowed, and in some cases mandated, that employees share personal information with their employers in the form of a health risk assessment (HRA).[2] Sports wearables have the potential to help in such efforts. Thus, employers are being incentivized to address major public health concerns – like tackling obesity among the workforce – by creating major privacy concerns.

All of this amounts to an ethical quandary.  What will happen in the future if an employee doesn’t want to share his health data or refuses to wear a Fitbit? Even athletes, whose entire jobs depend on peak physical performance, regularly balk at excessive invasions into their physicality and privacy that render them feeling like “lab rats.” How can, say, Oracle justify similar biometric data tracking for a systems analyst?

Furthermore, what happens when the person in question isn’t an employee at all?  For instance, while the NCAA constantly reiterates that collegiate players aren’t “employees” but “student-athletes,” advanced sports wearables are already being used by at least two collegiate athletic departments. The purported goal is to get athletes to make “positive” behavioral changes with “strong” encouragement to wear WHOOP.[3]  But the data could also be used to monitor every aspect of athletes’ lives and penalize them for “poor” choices such as drinking the night before a game or failing to get enough sleep.

Without better rules on who can access and handle biometric data, fears of Big Brother may shift from the government to Big Business – all under the guise of companies trying to promote “wellness”. 

Security

These issues are further compounded by questions regarding security and technological accuracy.

With sports wearables comprising 24/7 products and often paired with technologies like GPS, collected biometric data can paint a very accurate picture of a person’s habits and proclivities.

And reports show that this data is often easily hacked. This opens wearables users up to unintended access and even falsification of data, threatening harm to economic – and possibly even legal – interests.

Furthermore, we don’t know if the data produced by these devices is even accurate, or capable of being meaningfully used. No universal standard exists regarding how sports wearables operate, and a class action lawsuit has already risen alleging inaccuracies in Fitbit’s measurements. Expect questions about the viability and reliability of sports wearable technology to grow as they become more pervasive – and more central to determining, in dollars and cents, how to evaluate the health of an individual for economic purposes.

The Legal Landscape

Now that I’ve outlined some of the privacy problems surrounding sports wearables – what can Americans do?  Well, currently not much, as the United States remains an outlier by failing to have a comprehensive national policy regarding data privacy.

States appear to be leading the charge on data privacy thus far. All but two have laws in place requiring notice from private and public bodies when security breaches have leaked personally identifiable information[4].  Three states have gone further, defining what is meant by “biometric identifier”, specifying data security requirements, and limiting the period that biometric identifiers can be retained. Illinois goes furthest by creating a private right of action by which consumers can directly sue companies that misuse their biometric data.

From a consumer perspective, it’s good that the states seem to have a plan – because the federal landscape is, frankly speaking, a mess.  I’ll illustrate this by speaking about three major pieces of legislation that demonstrate how the U.S. government and its agencies are ill-equipped to deal with the future of commercialized biometric data. 

HIPAA

HIPAA’s exemption of “mHealth technologies” that include sports wearables leaves current federal regulation largely to the FTC and FCC.  While HIPAA does cover privacy and security of health information, what is covered by HIPAA can get quite parsimonious.  For instance, health information captured under a workplace wellness program isn’t protected unless it is specifically part of a group health plan.

The FTC regulates through its application of Section 5 of the FTC Act which prohibits “unfair or deceptive practices” including failure to properly disclose privacy policies or obtain authorization to disclose personal data. The FTC has also issued a general guidance regarding collection and use of biometric information, though that was primarily based on facial recognition technologies.[5]

The FCC is also getting into the regulatory space through its Connect2HealthFCC senior task force which is designed to review how broadband-enabled health solutions – including sports wearables – should be regulated. There is also growing recognition that the FCC is expanding its regulatory footprint in data privacy more generally by joining such groups as the Global Privacy Enforcement Network.[6]

In short, the FTC and FCC are using their competency in their respective domains – consumer protection and communications, respectively – to attempt to regulate in the brand-new sector of sports wearables. While their commitment may be admirable, the problem is that the U.S. is betraying a siloed history and sectoral approach to a pervasive modern problem.

What I propose is that the U.S. government take an issue-based view of the matter at hand – namely, how do we protect the American citizen from the privacy threats posed by potentially increased access to health data? – and clearly assign policy and enforcement competence and jurisdiction in this field to a single regulator at the federal level.  Not only would this eliminate the inefficiency of having multiple departments and agencies examining the same issue but it would grant the citizen a clear resource for information and for lodging complaints. 

The Rest of the World

Doing what I just mentioned isn’t crazy.  In fact, in much of the rest of the world, data privacy – a broad concept that includes biometric data – is viewed as a right.  The EU has specified that “everyone has the right to the protection of personal data concerning him or her” and that “data must be… for specified purposes”.  The EU Parliament has also outlined regulations and directives confirming the importance of consumer data privacy and clearly defining and regulating biometric data. To safeguard these principles and laws, most EU states also charge a single government agency with federal responsibility over data privacy matters, specifically including biometric data.

To the north, Canada’s Personal Information Protection and Electronics Act and Privacy Act govern how private and public sector organizations must, respectively, handle Canadians’ personal information.  A single federal regulator, the Office of the Privacy Commissioner, is tasked with investigating possible violations of both Acts.

Even Hong Kong, often called the “freest economy in the world”, has an Office of the Privacy Commissioner and a dedicated Personal Data (Privacy) Ordinance.

So What Should Happen Here?

While the U.S. doesn’t necessarily need to precisely emulate the Privacy Commissioner models of other countries, there is a need for better coordination and greater transparency – and at the very least a single body tasked with initially interfacing with the public on such matters.  This pro-citizen approach will alleviate confusion as questions about biometric data collection grows.

As is often the case, the sports world will be the testing ground for the initial stages of this battle over ownership of individual biometric data.

In the near future, expect companies who claim “anonymized” data belongs to them – and experts who contend that true anonymization is impossible. Insurers and employers will provide incentives to leagues and players to offer up their data for “research” purposes. Privacy advocates will contend that such data is personal patrimony and that athletes are giving up such information much too cheaply.

These are all worthy debates to track.  But here, at HHS and in Washington DC, I’d like to point out that such battles will soon pass from the sports and technology pages to the front page – and Americans will begin asking, as they rightly should – what is the plan and who is responsible?  It only makes sense, then, to band together and present a singular voice/agency to speak to Americans about this critical subject.

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The P.A. Problem: Who You See and What You Get

Recently, the New York Times published an article on excessive costs incurred by mid-level providers over-treating benign skin lesions. According to the piece, more than 15% of biopsies billed to Medicare in 2015 were done by unsupervised PA’s or Nurse Practitioners. Physicians across the country are becoming concerned mid-levels working independently without proper specialty training. Dr. Coldiron, a dermatologist, was interviewed by the Times and said, “What’s really going on is these practices…hire a bunch of P.A.’s and nurses and stick them out in clinics on their own. And they’re acting like doctors.”

They are working “like” doctors, yet do not have training equivalent to physicians. As a pediatrician, I have written about a missed diagnosis of an infant by an unscrupulous midlevel provider who embellished his pediatric expertise. This past summer, astute physician colleagues came across an independent physician assistant, Christie Kidd, PA-C, boldly referring to herself as a “dermatologist.” Her receptionist answers the phone by saying “Kidd Dermatology.”

The Doctors, a daytime talk show, accurately referred to Ms. Kidd on a May 7, 2015 segment as a “skin care specialist.” However, beauty magazines are not held to the same high standard; the dailymail.com, a publication in the UK, captioned a picture of “Dr. Christie Kidd”, as the “go-to MD practicing in Beverly Hills.” The article shared how Ms. Kidd treats the Kardashian-Jenner family, “helping them to look luminous in their no-make-up selfies.” While most of us cannot grasp the distress caused by not appearing luminous in no-makeup-selfies, this is significantly concerning for Kendall Jenner. At the tender age of 21, she inaccurately referred to Ms. Kidd as her “life-changing dermatologist.” Cosmopolitan continues the charade, publishing an article on the Jenner family “dermatologist.”


It astounds me how some medical professionals can contentedly live in the gray, south of brutal honesty, yet somewhere north of deceit. Until a few months ago, the Kidd Dermatology website erroneously listed her educational background as having graduated from the USC School of Medicine with honors and made no mention of her supervising physician. It was later modified to reflect she graduated from the Physician Assistant program at USC.

There are laws mandating physicians display diplomas and certifications prominently in the interest of transparency. According to Title 16, California Code of Regulations sections 1399.540 through 1399.546, a PA in “independent” practice is limited to the scope of his/her supervising physician by law. A board-certified plastic surgeon is supervising “skin specialist” Christie Kidd, PA-C, not a dermatologist. The website of the plastic surgeon states, “Trust only a Board-Certified Plastic Surgeon;” which in my opinion, seems astonishingly tongue-in-cheek. He may believe treating bullous pemphigoid disease is just another day in the life of plastic surgeons everywhere, but plastic surgery is a far cry from practicing dermatology and vice versa.

When asked about this, the Public Affairs Manager, Cassandra Hockenson, at the Medical Board of California responded “there is not a huge difference between plastic surgery and dermatology.” She suggested contacting the Physicians’ Assistant Board for the State of California instead. She kept repeating that the supervising plastic surgeon had no complaints against him. I learned two important lessons from contacting the Medical Board of California: 1) Without complaints, a physician can supervise midlevel providers in any specialty they choose, and 2) while required by law to supervise mid-level providers, the safety of patients is not a high priority for the Medical Board of California.

At a minimum, physicians complete four years of college, four of medical school, and between 3-7 years in residency. The years of education required for obtaining a PA degree are considerably fewer than that of an MD. For all intents and purposes, Christie Kidd, PA-C is running an independent dermatology practice directly under the nose of an apathetic California State Medical Board indifferent to regulations. PA’s can be fined and disciplined by their own board for misrepresentation, however, her “supervising” physician is, in fact, also out of compliance with the law.

While not all celebrities understand the difference in education between an MD or PA, mid-level providers and their supervising physicians should not be immune to the rules and regulations. Honesty, trust, and transparency are ideals essential to the medical profession. Physicians are held accountable for the health and safety of the patients we serve. Google Business modified the Kidd Dermatology listing from “Dermatologist” to “Medical Spa.” The unsinkable Christie Kidd struck a compromise, settling on the designation as a “skin care clinic.” Carpe Diem, Ms. Kidd, Carpe Diem.

Niran Al-Agba, MD is actually a physician. She practices in Washington state.

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