Pharma adopts data-science culture in move toward AI

Pharma adopts data-science culture in move toward AI

Pharma adopts data-science culture in move toward AI

To whom does pharma turn when facing cutting-edge research challenges such as designing algorithms to comb unstructured EHR data hunting for undiagnosed patients? Who helps glean patient insights from digital data streams such as social media? Who is designing the value-based frameworks behind pharma's latest wave of performance-based agreements with payers?

These are just some of the tasks being fielded by data scientists, a new kind of insight and analytics professional showing up in the biopharma ranks. These experts, skilled as they are in advanced data techniques, are being seriously courted by the industry.

“We're hiring engineers, quantitative pharmacologists, economists, mathematicians, and machine-learning experts. It's with diversity in mind — diversity not only in skill set, but also in experience,” reports Sandy Allerheiligen, Merck VP of predictive and economic modeling, at September's AI and the Near-term seminar organized by the consultancy Luminary Labs.

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Cut from a different cloth than traditional quants charged with tracking multichannel campaign management, they're just as likely to have experience in classic metrics as in Machine Learning.

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And they take their cue from consumer tech advances such as Amazon Alexa and Apple's Siri, the virtual assistants for the home and phone, respectively, that use artificial intelligence (AI). That term was coined in 1956 and roughly translates to “endowing computers with human-like intelligence.” But the field has more recently rebranded as machine learning, an expression that refers to computers using data to gather insights.

What's behind the latest recruiting wave in pharma? Some cite well-known factors such as a renewed focus on outcomes-based payment, the greater diversity of data, and the size of data volume.

Others say it's a response to pressure driven by consumer tech, whether it's the way Apple and Uber have had to quickly figure out how to use huge amounts of data in an intelligent manner to deliver value to the consumer, or machine-learning projects such as Microsoft's Hanover, which aims to predict what drugs and combinations are most effective to fight cancer. And there's IBM, whose Watson Oncology Advisor uses AI to develop individualized treatments.

Hilary Mason, the former chief data scientist for URL-shortening firm Bitly who now heads her own consultancy, Fast Forward Labs, suggests another reason, which she summed up as “technical possibility meets business opportunity.”

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To illustrate, Mason speaks of the ability to analyze images in rich media. When Mason was at Bitly, she said she observed that about 16% of the links were primarily media objects — an image, audio, or video.

“At the time, we weren't able to do anything with that object itself,” recalls Mason, also at the Luminary Labs event. “We were restricted to analyzing text around it or what people said about it.”

Now, Instagram — thanks to a data technique called deep learning — can automatically ascertain what users like to take pictures of by analyzing only the images.

“This complex data that has been unavailable for machine-learning techniques before is now accessible,” she explains. “It opens up many possibilities.”

All of these factors have led to a kind of cognitive-computing inflection point in pharma, where more emphasis is being devoted to preparing for big data and installing more data scientists.

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More than 40% of insight and analytic leaders cited “preparing for or managing big data” and the need to “upskill our organization's analytic capabilities” among their chief priorities for the next two years, according to a 2016 survey conducted by TGaS Advisors.

 



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