Time For Biopharma To Jump On The “Big Data” Train?

In a piece just posted at TheAtlantic.com, I discuss what I see as the next great quest in applied science: the assembly of a unified health database, a “big data” project that would collect in one searchable repository all the parameters that measure or could conceivably reflect human well-being.

I don’t expect the insights gained from these data will obsolete physicians, but rather empower them (as well as patients and other stakeholders) and make them better, informing their clinical judgment without supplanting their empathy.

I also discuss how many companies and academic researchers are focusing their efforts on defined subsets of the information challenge, generally at the intersection of data domains.  I observe that one notable exception seems to be big pharma, as many large drug companies seem to have decided that hefty big data analytics is a service to be outsourced, rather than a core competency to be built.  I then ask whether this is savvy judgment or a profound miscalculation, and suggest that if you were going to create the health solutions provider of the future, arguably your first move would be to recruit a cutting-edge analytics team.

The question of core competencies is more than just semantics – it is perhaps the most important strategic question facing biopharma companies as they peer into a frightening and uncertain future.

In my experience, a company’s view of its core competencies translates directly into how it prosecutes its mission, as well as in the quality of talent it’s able to recruit and retain.   An enterprise that sees itself as defined by world-class sales and marketing would be expected to look very different, and emphasize different things, than a company that specializes in making novel biologics, for example, or a company that’s focused on clinical development.  Companies are usually built around what they do well, and can underestimate the complexity of other areas, especially those selected for outsourcing.  You don’t know what you don’t know.

In the case of big data analytics, this means that unless a pharma company is deliberately built around this capability – or this function is developed and nurtured in a fenced-off, skunkworks fashion – it’s unlikely to get adequate traction, and will be vulnerable to the usual corporate antibodies, especially when budget time comes around.

Conversely, I suspect that a biopharma company built entirely around analytics would suffer from a problem similar to that experienced by many academic researchers who seek to drive their laboratory discoveries into real-world application:  it’s far more difficult to successfully develop, manufacture, receive approval for, and market a new medical product than most outside the business appreciate.

(Perhaps a stretch, but you might also argue health portals such as Google Health and Microsoft HealthVault failed in part because sponsors were so focused on the data and analytic opportunities that they lost sight of essential real-world considerations, and didn’t have a sufficiently granular sense of what’s required to be a successful business in the always-difficult health space.)

Presumably, once big data analytics firmly establishes itself as an essential capability, serious biopharma players will have no choice but to consider this function vital and integral – and I suspect most eventually will.  Thus, the real question for big pharmas is when to jump on board the big data train; they’ve been burned in the past by premature investments in overhyped technologies, so you can certainly appreciate their reluctance.

At the same time, given both the overwhelming amount of available data and the fact that traditional pharma approaches to innovation seem to have largely run out of steam, you’d think that a bet on big data analytics might make a lot of sense now.  Given the headwinds facing the industry, it’s a bold play you’ve got to believe someone will be wise enough, brave enough, or desperate enough to make.

David Shaywitz is co-founder of the Center for Assessment Technology and Continuous Health (CATCH) in Boston.  He is a strategist at a biopharmaceutical company in South San Francisco. You can follow him at his personal website. This post originally appeared on Forbes.

2 replies »

  1. Lawrence and Lincoln Weeds’ “Medicine in Denial” 101.

    “[W]ere we to close the gap between medical practice and patient needs, society then could find enormous opportunities to harvest resources now going to waste. These wasted resources include not only vast sums spent on low-value care but also a vast body of medical knowledge that all patients and practitioners could use more effectively, simple tests and observations that in combination could uncover solutions to patient problems, patients who could become better equipped and motivated to improve their own health behaviors, routine patient care that could become a fertile source of new medical knowledge, and the firsthand insights of practitioners and patients who could participate in harvesting that new knowledge for their own benefit.

    Closing the gap between medical practice and patient needs would transform how medicine is personally experienced by practitioners and patients alike. Practitioners could find their work to be less exhausting and more rewarding, emotionally and intellectually, than what they now undergo. The physician’s role could disaggregate into multiple roles, all freed from the impossible burdens of performance that physicians
    are now expected to bear. The expertise of nurses and other non-physician practitioners could deepen, and their roles could be elevated. All practitioners could follow time-honored standards of care that in the past have been honored more in the breach than the observance. All practitioners and patients could jointly use electronic information tools for matching data with medical knowledge, radically expanding their capacity to cope with complexity. All could use structured medical records, whose structure would
    itself bring order and transparency to the complex processes of care. Inputs by practitioners could thus be defined and subjected to constant feedback and improvement. A truly evidence-based medicine could develop, where evidence would be used to individualize care rather than standardize it. And a system of checks and balances could develop, where patients and practitioners would act on incentives for quality and economy far more effectively than before”