The current challenge for data science and technology (DST) in healthcare is moving beyond the “dancing bear” stage, where “the wonder is not how well the bear dances, but that he dances at all.” It’s time for DST to evolve past the novelty publications and the click bait, and demonstrate its ability to materially impact health and disease.
The need for DST impact in pharma is especially acute, and challenging, as this column has critically explored. In the last year, I’ve focused on the cultural challenges, the distinction between invention and implementation, the frustrations of black box AI, the potential for AI to generate misleading clinical classifiers, the fetishization of big data, and the opportunities to improve clinical research, among other topics. With few exceptions, it seems that DST’s exceptional potential has yet to find meaningful expression in most drug development organizations.
It’s easy to be misled by the deafening buzz. The conference circuit is now exploding with “AI and pharma” conferences (I’m speaking at several), consultants excitedly discuss pharma’s digital transformation (and convince each pharma they’re distinctly behind), and exuberant stories about the power of data and AI resound almost daily across social media.
Yet when you dive beneath the froth, a very different scenario is revealed. In an achingly perceptive 2017 essay, Dr. Sachin Jain, former CMIO at Merck and now CEO of CareMore Health, candidly laments the unexpected gap between discussions of tech innovation in healthcare and actual tech innovation in healthcare, describing our present state as an “innovation bubble.”
While Jain touches briefly on pharma (he shares my observation that the eternal dream of creating a “service beyond the pill” has struggled), his focus is on healthcare delivery. Enter Vas Narasimhan.
Pharma, Powered By Data & Digital (?)
Narasimhan is the 42 year old physician and former McKinsey consultant who joined Novartis in 2005 and took the reigns as CEO last year, drawing attention with his focus on technology. “We need to become a focused medicines company that’s powered by data science and digital technologies,” he told the Wall Street Journal in Feburary 2018.
Now, a year into this new role, he’s had a chance to reflect on his company’s “digital journey” (as his McKinsey colleagues might say), which he’s graciously done in an absolutely captivating podcast interview with several members of the silicon valley tech VC firm Andreessen Horowitz (best known for its slogan, “software is eating the world”). General Partners Jorge Conde and Vijay Pande, and Editorial Partner Sonal Chokshi, sat down with Narasimhan during the recent JP Morgan conference in San Francisco.
It’s hard to imagine a better window into how a forward-thinking large pharma, led by a relatively young, innovative physician leader, is actively wrestling with the issues and many challenges of incorporating DST approaches into their R&D efforts. The entire episode is a must-listen; I’ve extracted several key highlights, below, which reinforced in my mind both the difficulty and the urgency of leveraging DST approaches in pharma R&D.
Narasimhan sets the stage by pointing out the industry’s miserable attrition rate: of the twenty drugs that enter clinical studies, only one makes it. Worse, this rate apparently hasn’t moved in the last fifteen years, while costs have continued to increase). This is the fundamental problem (as I’ve also emphasized) for which the industry is trying to solve.
Narasimhan suggests increase costs reflect the increased complexity of clinical trials (essentially due to an increased amount of work each trial is being asked to do, gather information for regulators, of course, but also data that address scientific as well as market access questions). He reports that tech may be able to excise up to 20% of this cost (which sounds like a classic consultant SWAG).
Responding to a question, he said he saw a role for engineering especially in improving their processes around the manufacture of new categories of medicines, such as cell therapies and gene therapies, where he says the field is still at the “learning to crawl” stage. He also suggested engineers could contribute to chemical biology, helping to design more effective molecules rather than rely on empiricism to discover them.
AI – But First, Data
When asked about AI and ML, he began by level-setting: “As we’ve gotten quite scaled and working on digital health and data science, we’ve learned there’s a lot of talk and very little in terms of actual delivery of impact.”
Wow – “very little in terms of actual delivery of impact.”
“We’ve learned a lot,” he continued.
He did call out several areas of promise for AI. The first involved imaging – Novartis has embarked on a massive project to digitize all of their pathology images, he said, partnering with a startup called PathAI, as a prelude to machine learning. He can envision repeating this process for other categories of images as well. At the moment, this feels like a work in progress – lots of images being digitized, value of the effort TBD.
“Sounds like a gold mine,” Chokshi observed.
“It should be,” Narasimhan cautiously replied.
There are two areas where AI approaches are apparently already delivering actionable results: clinical trial operations and finance. In operations, Novartis has set up a control center that monitors all of their clinical trials. As Narasimhan describes it,
He notes that they’re not looking at patient-level data, but are, deliberately, a level up from that.
He also says that AI is proving quite useful in finance. “AI does a great job predicting our free cash flow,” he says, “predicting a lot of our sales for key products. It does better than our internal people because it doesn’t have the biases, and the data are really clean, and we have a lot of long-term data.
In short: one ambitious but unproven AI effort in science, two apparently successful efforts in non-science (operations and finance). Even there, I wonder if the role of “AI” is perhaps overstated, and means something different and less profound than when used in the DeepMind/AlphaZero context, but that’s just my hunch.
Similarly, look what Merck R&D head Roger Perlmutter told Matthew Herper (then at FORBES) in 2013:
And now, this month, look what Narashahim went out of his way to emphasize at the end of his podcast interview:
While humility is not generally the first quality one associates with CEOs, physicians, or McKinsey consultants, our staggering collective biological ignorance, and the enormity of the challenge of drug development, brings even the mighty to their knees.
For technologists hoping to impact disease, it will be critical to move away from solutionism, the belief that an app or an algorithm will effortlessly solve the complex and often ill-defined medical problems that plague patients and preoccupy drug developers. DST entrepreneurs need to evince some appreciation of the messy complexity of biology, and to understand just what they’re getting into – and to recognize that even a successful product is likely to solve only a very small (though potentially important) part of the overall problem.
As Narasimhan emphatically conveyed, our understanding of the human organism, in both health and disease, is exceptionally primitive. It requires a strange combination of audacity and foolhardiness to believe you can create a product that will impact disease in a meaningful way. We need to bring our best technology – biological and digital – and our most creative people together to work on this monumental challenge.
One clear take-away from the Narasimhan interview: pharma is at the very earliest stages of figuring out how to do this.
David Shaywitz Contributor