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Tag: David Shaywitz

Do Something Really Innovative In Health: Crowdsource Problems, Not (Just) Solutions

Businesses exist to solve problems, right?  Certainly, this is the heart of the classic entrepreneurial model: you become obsessed with a particular problem, and create a business to solve it.  Example: eBay was created by Pierre Omidyar to solve a perceived problem with inefficient markets, and since its inception has generally focused on doing exactly this.

Most enterprises are not blessed by such a coherent focus, at least not for long.  More often, organizations – including university research labs as well as for-profit businesses – have a point at which they realize that their challenge has changed, and the problem they thought there were going to solve has shifted or even completely disappeared.  The team – often an impressive group of people representing a wide range of capabilities — is then left to figure out what to do.

While disbanding is always an option, it rarely seems to happen, at least volitionally.  Businesses, projects, academic enterprises – all are obsessed with their own survival, which rapidly becomes the defining mission.  As a result, the organization urgently tries to figure out a way to pivot, a way to apply established resources in a different, useful way as it searches for a purpose to justify its existence.  Very often, the question becomes: what should we do – what problem should we solve?

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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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Balancing Consistency and Innovation in Healthcare

Our healthcare system is now facing a problem that has plagued business leaders for years: how do you  balance consistency and innovation?

The drive for consistency in healthcare is based upon the fundamental observation that physicians across the country treat similar medical conditions in dramatically different fashions.  Sometimes, these different approaches are costly, such as using a more expensive treatment when a less expensive approach might be as effective.  In other cases, these practice variations are dangerous – failing to provide patients with treatment the evidence suggests is best.

Standardizing the delivery of care — identifying “best practices,” and then insisting physicians follow these guidelines – could, in theory, save money while improving quality, and is the basis of Obama’s healthcare proposal.Continue reading…

The Right to Live


After generations in denial, doctors and lawmakers are paying attention to
the importance of allowing sick people a dignified death, and to the value of
helping patients and their families let go and say good-bye. Aggressive medical
intervention in terminal cases is increasingly considered an avoidable cruelty,
inflicted on a suffering patient by someone — occasionally a doctor, but more
often a family member — unable to acknowledge the inevitable.

As an intern, I see this almost every day, and I’m grateful that most
physicians now go out of their way to emphasize to patients and their families
the limitations of medical technology. Medical students attend lectures on
caring for dying patients, and medical journals remind doctors of the importance
of letting patients die with respect and, as far as possible, without pain.

But as an experience in my own family made clear, this newfound concern for a
good death can be taken too far during a patient’s final days.

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Science Is Leading Us to More Answers, but It’s Also Misleading Us

Be careful what you wish for. That is the unexpected lesson of the past decade of biomedical research, which has been characterized by an overwhelming abundance of interesting things to study and powerful ways to study them. A pioneer of this era, MIT geneticist Eric Lander, speaks eloquently of the “global view of biology,” meaning that scientists now have extraordinary tools to study not only individual genes, but also multiple genes at the same time. Rather than immediately investing all their resources in a few favorite genes (the traditional approach), modern researchers first can survey thousands of initial candidates, then identify and ultimately direct their attention to the most important players and pivotal networks.But we are increasingly discovering that this global perspective comes at an unexpectedly steep price: We’re making a lot more mistakes. Or, at least, we seem to be having a lot of trouble picking out the rare, meaningful signal from the deafening noise in the background.

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Rx For Medical Research

Most biomedical research is framed by an outdated view of disease, a linear mind-set that focuses on simple causes rather than complex relationships within dynamic systems. If we are to achieve President Obama’s audacious goal of “a cure for cancer in our time,” we must radically alter the way we think about biology and disease.

Physicians and medical researchers are traditionally taught to consider disease in terms of simple causes and isolated linear pathways. This one-gene-one-disease approach also informs the way most animal models of disease are developed. Technology readily enables researchers to engineer mice with specific molecular defects in one or a small number of genes as an experimental proxy for human disease. While some of these models are informative and reasonably predictive, most are not.

The limitations of animal models are highlighted by results emerging from powerful genomic studies of human diseases ranging from Type 2 diabetes to pancreatic cancer. For these and many other conditions, the cause is not a single defect, or even a handful of defects, but rather, combinations of hundreds of possible defects, each contributing slightly to the overall risk of disease.

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