On the Road to Big Medicine

Life science companies are no strangers to data, so it would be easy to assume they are adept at making innovative use of huge amounts. Not necessarily. A tradition of rigorous scientific method and clinical trial hasn’t prepared them for the shifting inundation of big data or all its baffling potential. If anything, the reliable, “clinically proven” analytical habits of former decades have hampered some manufacturers from leveraging data in new and needed ways.

Here’s a look at three issues facing pharmaceutical and biotech companies as they adapt to the reality of all kinds of data, all the time.

Access and accuracy

One issue is the quantity and quality of information. Despite the prevalence of electronic health records, which in theory allow seamless information flow between healthcare providers, siloed information continues to be an issue. Healthcare networks are generally well connected, allowing patients’ records to travel between specialists, added to and updated more or less in real time. This is the information that, once assembled in quantity, can provide insight about trends, possible improvements, and new approaches. But patient data still tend to stay siloed, at least electronically, within those discrete systems. The data are there, but not comprehensively available at the click of a mouse. Analysts may question the usefulness of their findings if they are working with too little information, or data that carry the unifying stamp of a particular system’s protocols.

“Every biomedical organization has their data spread across multiple data stores, and the real work to be done is curating it into a form that can be cross-analyzed,” Anthony Philippakis, chief data officer of the Broad Institute, told David Shaywitz earlier this year. Pondering the implications in a recent post on The Health Care Blog, Shaywitz notes that despite technology’s helping hand, “the cost of successfully developing a new drug, including the cost of failures, has been relentlessly increasing.” In response, he says, pharmaceutical companies are “aggregating and organizing internal data, supplementing this with available public data, and overlaying this with a set of analytical tools that will help the many data scientists these pharmas are urgently hiring to extract insights and accelerate research.”

Data availability can also be bottlenecked over issues of control. When companies view analysts as tools rather than players, there’s a tendency to hand them data that have already been managed based on others’ assumprions or directives. Yet, as Shaywitz points out, data analysts do their best work when they’re allowed full access to data and can look at them in ways that others might not. This fresh view is more likely to spot worthwhile trends or anomalies that can lead to true innovation.

Precision medicine

Another issue is the emerging trend of personalized medicine. How do you innovate nimbly in a heavily regulated environment? Software’s usefulness in bringing actionable information to the surface in a sea How do you innovate nimbly in a heavily regulated environment?of data isn’t the issue. However, the speed at which software typically evolves, presenting new ways of looking at what the life science industry can do with the information it has, isn’t always helpful. The “fail fast and iterate” strategy of most software developers can’t or won’t slow itself to allow for the deliberate, negotiated pace of pharmaceutical and medical device development. In the last couple of years particularly, the U.S. Food and Drug Administration (FDA), has focused on streamlining and accelerating its regulations processes, but getting a new medical product safely to market is still a years-long endeavor.

Coupled with these divergent business views—software’s innovate to survive vs. life science’s test and retest—is the individualized digital reality we’re all experiencing. As consumers, whether consciously or not, we’ve come to expect the granular level of interaction made possible by big tech’s algorithms and the internet’s speed of delivery. We’ve only to check out a couple web pages about an ailment to be followed everywhere online with relevant services hoping to gain our attention. We want the digital world to cater to us quickly and precisely, even as we remain alert for any perceived breaches into what we consider our personal space.

Life science companies’ marching orders are therefore to constantly achieve the impossible: safe products, reliable privacy, instant and personalized gratification. Although this is a likely future for medicine, getting there at present is a bit like putting a boat out to sea while the tide is both coming in and going out.

No one understands this better than life science marketers, those companies responsible for bridging the gap between creators and consumers. “As marketers, we have been obsessively building personas and segments to abstract our audiences,” says Jeff Ross, president of Wunderman Health, responding to a query from MM&M (formerly Medical Marketing and Media). “The challenge is they do not satisfy patient and healthcare personnel expectations that we respond to them in a deeply personalized way with direct knowledge of their individual needs and wants. This is confirmed by research that found consumers expect healthcare brands to provide a brand experience comparable to Amazon or Netflix.”

Paranoia, possibly justified

Which brings us to the still evolving relationship between life science and tech firms, particularly big tech firms. Headlines hint about the increasing interest in life science on the part of companies like Apple, with its burgeoning product line of fitness wearables; and Amazon, with its still-covert partnership with Berkshire Hathaway and JPMorgan, ostensibly to develop a health plan for their collective employees.

“You can debate about whose data it is, but one thing for sure is the pharmaceutical company doesn’t own it.” —Severin SchwanAt issue, once again, is the degree to which life science companies are willing to share their data with tech companies, whose native skills could significantly improve research and development. What is the appropriate amount, when the tech companies themselves are briskly investing in the medical industry? Just how valuable is so-called real-world evidence, as opposed to traditional, randomized clinical trials, when a life science company’s livelihood might be at stake?

“The ability to capture the experience of real-world patients, who represent a wider sample of society than the relatively narrow selection enrolled into traditional trials, is increasingly useful as medicine becomes more personalized,” notes Ben Hirschler in a Reuters article. “However, it also opens a new front in the debate about corporate access to personal data at a time when tech giants Apple, Amazon, and Google’s parent Alphabet are seeking to carve out a healthcare niche.”

Proprietary habits die hard, but for life science companies to manage rising costs and expectations, while generating the profits to which they’ve become accustomed, partnering is increasingly necessary. “Historically, it has been hard to get a handle on how drugs work in routine clinical practice, but the rise of electronic medical records, databases of insurance claims, fitness wearables, and even social media now offers a wealth of new data,” writes Hirschler.

Severin Schwan, CEO of the Swiss pharmaceutical and diagnostic company Roche, agrees. “There’s an opportunity for us to have a strategic advantage by bringing together diagnostics and pharma with data management,” he told Reuters. “You can have a big debate about whose data it is—the patient’s, the government’s, the insurer’s—but one thing for sure is the pharmaceutical company does not own it. So there’s no choice but to do partnerships.”

Roche aims to leverage this with the $2 billion purchase last year of Flatiron Health, an oncology research firm.

“If Amazon gets into the pharma game, it will be a huge disruption, with the potential to provide a powerful marketing channel to reach consumers,” says Destry Sulkes, chief data officer at WPP Health and Wellness. “From a pharma perspective, it would put a treasure trove of personal and highly detailed (de-identified) patient information at their disposal.”

Anthony Philippakis argues the other perspective. “It is crazy to think that any one group can create a data platform that will satisfy the needs of all groups across all geographies,” he says. “We need modular and interoperable services that result in an ecosystem of activity.”

With the pharmaceutical and biotechnology markets poised to exceed 1.2 trillion by 2022, we can at least be sure we haven’t heard the last about this clash of the Titans. For us small consumers, while we wait for the dust and noise to subside, we’d be well advised not to stand in their shadows.

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