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Tag: David Shaywitz

2012 Digital Health Investment Activity: The View From the Valley

Rock Health recently released a decidedly mixed report on the current state of Digital Health investing, as the data suggest many investors continue to tentatively explore the sector, but most have yet to make a serious commitment.

Overall, VC funding for digital health increased significantly over the past year, from just under $1B in 2011 to about $1.4B in 2012; 20% of this total was associated with just five deals: two raises for transparency companies, Castlight (targeting employees with high deductible plans looking to manage their costs) and GoHealth (targeting consumers contemplating purchase of health insurance); two raises for referral companies, Care.com (helps consumers find the right caregiver – defined broadly, as needs addressed include eldercare, child tutoring, babysitting, and pet care) and BestDoctors (helps employees find the right doctor), and one deal for 23andMe (a pioneering consumer genetics company).

Not surprisingly, the largest thematic area of investment ($237M) was “health consumer engagement,” comprised of companies that – like the first four above – help consumers or employees with healthcare purchases.   “Personal health tools and tracking,” the second leading category, captured $143M in funding last year.  “EMR/EHR” ($108M) and “hospital administration” ($78M) rounded out the list; the last two numbers seem shockingly low given the apparent size of these markets, and suggest both areas may be perceived as  firmly owned by incumbent players, and prohibitively difficult for new participants to enter.

Athenahealth’s just-announced acquisition of Epocrates highlights the competitive pressures even existing EMR companies face as they struggle for traction in an environment that seems to be increasingly dominated by a few large players, most notably Epic. “Our biggest obstacle,” Athenahealth CEO Jonathan Bush told Bloomberg Businessweek, “is that 70% of doctors don’t even know we exist.”  In contrast, I’ve suggested that a category I’d broadly define as EMR adjacencies may be primed for growth, as VC’s Stephen Kraus and Ambar Bhattacharyya have also discussed recently in this intelligent post.  The related area of care transitions is also attracting considerable entrepreneurial interest, including current Rock Health portfolio companies WellFrame and OpenPlacement, and TechStars alum Careport; it remains to be seen whether a robust business model will emerge here.

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Why Getting to a Digital Health Care System Is Going to Be Harder Than We Thought Ten Years Ago

A leading scientist once claimed that, with the relevant data and a large enough computer, he could “compute the organism” – meaning completely describe its anatomy, physiology, and behavior. Another legendary researcher asserted that, following capture of the relevant data, “we will know what it is to be human.” The breathless excitement of Sydney Brenner and Walter Gilbert —voiced more than a decade ago and captured by the skeptical Harvard geneticist Richard Lewontin [1]– was sparked by the sequencing of the human genome. Its echoes can be heard in the bold promises made for digital health today.

The human genome project, while an extraordinary technological accomplishment, has not translated easily into improved medicine nor unleashed a torrent of new cures. Perhaps the most successful “genomics” company, Millennium Pharmaceuticals, achieved lasting success not by virtue of the molecular cures they organically discovered, but by the more traditional pipeline they shrewdly acquired (notably via the purchase of LeukoSite, which ultimately yielded Campath and Velcade).

The enduring lesson of the genomics frenzy was succinctly captured by Brown and Goldstein, when they observed, “a gene sequence is not a drug.”

Flash forward to today: technologists, investors, providers, and policy makers all exalt the potential of digital health [2]. Like genomics, the big idea – or leap of faith — is that through the more complete collection and analysis of data, we’ll be able to essentially “compute” healthcare – to the point, some envision, where computers will become the care providers, and doctors will at best be customer service personnel, like the attendants at PepBoys, interfacing with libraries of software driven algorithms.

A measure of humility is in order. Just as a gene sequence is not a drug, information is not a cure. Getting there will take patience, persistence, money and aligned interests. The most successful innovators in digital health will see the promise of the technology, but also accept, embrace, and ideally leverage the ambiguity of disease, the variability of patients, and the complexities of clinical care.
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Through a Scanner Darkly: Three Health Care Trends for 2013

As we anticipate a new year characterized by unprecedented interest in healthcare innovation, pay particular attention to the following three emerging tensions in the space.

Tension 1: Preventive Health vs Excessive Medicalization

A core tenet of medicine is that it’s better to prevent a disease (or at least catch it early) than to treat it after it has firmly taken hold.   This is the rationale for both our interest in screening exams (such as mammography) as well as the focus on risk factor reduction (e.g. treating high blood pressure and high cholesterol to prevent heart attacks).

The problem, however, is that intervention itself carries a risk, which is sometimes well-characterized (e.g. in the case of a low-dose aspirin for some patients with a history of heart disease) but more often incompletely understood.

As both Eric Topol and Nassim Taleb have argued, there’s a powerful tendency to underestimate the risk associated with interventions.  Topol, for example, has highlighted the potential risk of using statins to treat patients who have never had heart disease (i.e. primary prevention), a danger he worries may exceed the “relatively small benefit that can be derived.”  (Other cardiologists disagree – see this piece by colleague Matt Herper).

In his new book Antifragile, Taleb focuses extensively on iatrogenics, arguing “we should not take risks with near-healthy people” though he adds “we should take a lot, a lot more, with those deemed in danger.”

Both Topol and Taleb are right that we tend to underestimate iatrogenicity in general, and often fail to factor in the small but real possibility of potential harm.

At the same time, I also worry about external experts deciding categorically what sort of risk is or isn’t “worth it” for an individual patient – a particular problem in oncology, where it now seems  fashionable to declare the possibility of a few more months of life a marginal or insignificant benefit.

Even less dramatically, a treatment benefit that some might view as trivial (for hemorrhoids, say) might be life-altering for others.  For these sufferers, a theoretical risk that some (like Taleb) find prohibitive might be worth the likelihood of symptom relief.  Ideally, this decision would ultimately belong to patients, not experts asserting to act on patients’ behalf.

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The Mentalists

Obama’s most significant healthcare-related accomplishment this year may well have been his campaign’s demonstration of the effective use of analytics and behavioral insight – strategies that also offer exceptional promise for the delivery of care and the maintenance of health.

For starters, of course, there’s the widely-reported “big data” success of the Obama campaign.  In unprecedented fashioned, they collected, mined, analyzed, and actioned information, microtargeting voters in a remarkably individualized fashion.

Imagine if healthcare interventions could be personalized as effectively (or pursued as passionately).

Another example:  according to the NYT, the Obama campaign hired a “dream team” of behavioral psychologists to burnish their message and bring out the vote, using a range of techniques the field has developed over the years.

According to the article, the behavioral experts “said they knew of no such informal advisory committee on the Republican side.”

This idea of focusing intensively on behavior change is without question an idea whose time has come.

Earlier this year, for instance, a colleague (with similar training in medicine, molecular biology, and business) and I were surveying the biopharma landscape, and were struck by the extent to which classic biology hasn’t (yet) delivered the cures for which we had hoped; physiology turns out to be extremely complicated, and people, and communities, even more so.

We were also struck by the remarkably low adherence rates for many drugs, abysmal whether you look at this from the perspective of clinical care or commercial opportunity (imagine if Toyota lost half their cars on the way to the dealership).
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Nate Silver Is King, Long Live Nate Silver

My twitter stream is awash in math this morning, cheering Nate Silver’s exceptional forecasting (“Triumph of the Nerds: Nate Silver Wins In Fifty States”, Chris Taylor wrote), and celebrating the victory of math and big data over pompous punditry.  Jeff Greenfield tweeted, “I, for one, welcome our new Algorithmic Overlord.”

At some level, I thrill to the ascendancy of math, and of math nerds – and I write this as a proud former math team captain (and math team T-shirt designer), and as someone whose very best summers as a teenager were spent in math (and writing) camp at Duke University.  It’s also one of the reasons I love Silicon Valley so much – it’s where nerds rule, and where even emerging VCs promote themselves as “Geeks.”

However, before we turn all of life over to algorithms, as some are suggesting, it’s important to place the election prediction in context.

The accomplishment of Silver’s splendid forecasting was to intelligently aggregate existing data, to accurately summarize the current, expressed intentions of the national electorate.  And we’ve learned that careful analysis is far more useful than blustery experts – something Philip Tetlock has been trying to tell us for years.

At the same time, all forecasting challenges are not created equal, and summarizing current public opinion is a much lower bar than predicting events far into the future – and Silver has been clear about this; it’s others who seem to be leaping ahead.

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Being Human

The human connection is threatened by medicine’s increasingly reductive focus on data collection, algorithms, and information transaction.

If you follow digital health, Rachel King’s recent Wall Street Journal piece on Stanford physician Abraham Verghese should be required reading, as it succinctly captures the way compassionate, informed physicians wrestle with emerging technologies — especially the electronic medical record.

For starters, Verghese understands its appeal: “The electronic medical record is a wonderful thing, in general, a huge improvement on finding paper charts and finding the old records and trying to put them all together.”

At the same, he accurately captures the problem: “The downside is that we’re spending too much time on the electronic medical record and not enough at the bedside.”

This tension is not unique to digital health, and reflects a more general struggle between technologists who emphasize the efficient communication of discrete data, and others (humanists? Luddites?) who worry that in the reduction of complexity to data, something vital may be lost.

Technologists, it seems, tend to view activities like reading and medicine as fundamentally data transactions. So it makes sense to receive reading information electronically on your Kindle — what could be more efficient?

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Digital Health: Almost a Real, Live Business

While the evolution of the digital health ecosystem has seemed at times almost painfully contrived, it now appears to have reached the point where it requires but a few sprinkles of magic fairy dust to be truly alive.

The basic idea behind digital health is pretty clear: we can (and must) do health better, and technology should be able to help,

There’s also an ever-increasing amount of support for early-stage innovators in this space. A remarkably large number of digital health incubators have sprung up around the country, as Lisa Suennen captured with characteristic verve in a recent Venture Valkyrie post.

On top of this, a slew of corporate VCs have now emerged – many from payors, but some from communication companies, and even a few from big pharmas such as Merck – all keen to invest strategically in the digital health space.

Deliberately, many of these large corporations also represent likely buyers for the products or services that will be produced, so it really does seem like an example of the savvy external sourcing of innovation.

So we’re good, then – right?

Well, not so fast.

It turns out that many high profile VCs continue to eschew this space, other than perhaps an occasional investment or two. The reason? As one extremely well-regarded VC – with extensive healthcare experience – told me yesterday, “I haven’t seen a viable business model yet.”

Translation: how do you make (serious) money here? Where’s the revenue?

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Don’t Confuse Hard Science With Bad Pharma

A key lesson of science is the importance of a control group; I worry that a lot of coverage and discussion of the biopharma industry (in which I work) neglects this lesson, and instead contrasts (implicitly or explicitly) industry behavior to that of an imagined, idealized standard of perfection, and fails to place the actions in the context of medical science as a whole.

I appreciate critical coverage of the industry: reporters should always maintain high standards, approach new information skeptically, and not take anything at face value.

However, what disappoints me is the common, implicit assumption that industry science deserves to be treated as a special case, rather than considered within the broader framework of contemporary research.  I’m especially disappointed by the frequent assumption that the behavior of industry scientists should be viewed more skeptically than the behavior of academic scientists; this strikes me as a magical, often self-serving belief that has now become elevated to the status of conventional wisdom.

Take data sharing, a topic in the news today (and discussed very thoughtfully here by John Wilbanks, the guru of open science).  While most media coverage of this topic (both today and over the years) has focused on the transparency of industry research, I’ve been attending the annual Sage Commons Congress since its inception in 2010 (disclosure: I served as a founding advisor to Sage, a non-profit organization focused on open science, founded by Eric Schadt and Stephen Friend), and hearing every year about how incredibly difficult it is to get academic groups to share with each other, for a wide variety of reasons.  (See this exceptional talk from Josh Sommer of the Chordoma Foundation at the First Sage Congress).  Getting scientists (or any group of competitive human beings) to exchange data turns out to be a real problem — especially in the highly-regulated environment in which clinical data sit.

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Rethinking the Provider Certification Game

Quality is the new watchword in healthcare; it’s what we seek – and increasingly, what we try to measure.  Medications, devices, care delivery, hospital services – all are now scrutinized as we seek to gauge their benefit, and justify their cost.

The idea of using metrics to evaluate quality make sense, but only if we can trust the metrics themselves.  Otherwise, we risk becoming party to an updated version of craniometry, systematized false-precision that focuses on easily-measurable parameters (such as head circumference) that may not represent meaningful proxies for the assessments we’re really after (i.e. intelligence).

The good news is that the science of testing, of developing evaluation instruments, has improved over time.  We’re now better able to recognize the qualities and properties of good tests – and to identify where they’re likely to fall short.

We’re also getting more comfortable with demanding robust evaluation instruments.  For example, the FDA’s approach to patient-reported outcomes places exceptional (and appropriate) emphasis on the assessment tool chosen, and requires that it demonstrates the appropriate properties before relying on its results.

Unfortunately, one critically important area within our healthcare system that seems to have escaped such careful review is the way the competence of care providers is typically assessed and certified.

Whether you are an X-ray technician, a physical therapist, a registered nurse, or a transplant surgeon, you are required to pass through a gauntlet of costly certification exams.  These tests, already significant, are assuming an even greater importance as the healthcare system increasingly looks to them as proxies for quality.  Certification can be required for employment and for admission privileges, and frequently impacts the reimbursement rate for healthcare providers.

All this makes complete sense – provided the certification tests themselves are sound.

Unfortunately, the world of healthcare worker certification remains a bit like the wild west, as medical organizations and professional societies approach certification testing with profoundly different degrees of rigor — and generally little-to-no transparency.

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Better Than Pandora for Cats

Health VCs: Desperate…

As a tech VC recently told me, refuting the latest flimsy rumor of a huge tech-dominated fund contemplating significant new investment in life science, “Wow, you healthcare guys are really desperate for some good news!”

It’s true; not only are LPs looking ever more critically at VC as an asset class – especially since the publication of the Kauffman report – but the life science sector, in particular, has been devastated, and health VCs have been hurting.  (Added Sept 27: See this fascinating, just-posted Xconomy profile of Avalon’s Kevin Kinsella and discussion of the current sorry state of healthcare VC.)

Part of the issue, as Bruce Booth and Bijan Salehizidah have described previously, and as Sarah Lacy summarized nicely this week in PandoDaily, is that the return profile of life science venture investments looks very different than tech in general, and consumer web (the focus of Lacy’s article) in particular.

The sex appeal of tech investing is that a relatively small initial investment can blossom very quickly to yield huge returns; the catch, of course, is that this happens very rarely, and much like at a casino, and the tremendous attention lavished upon these winners can almost make you forget how infrequently they occur.

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