Pharma’s Digital Transformation: Enduring Challenges, Sustained Hopes, And A Progress Report From Lilly’s CEO

When it comes to emerging digital and data technologies, most pharma CEOs today are singing from the same hymnal.

They all emphasize their commitment to digital transformation, and assert that the adoption of digital processes are key to their companies’ success, and vital for the industry’s future. 

A recent Lazard survey of healthcare leaders echoes this message. The investment bank found that most senior executives believe “advances in digital technologies, data analytics AI and ML” represent the force that will “most transform the healthcare industry over the next 5-10 years.”

Outside of the C-suite, however, the view of emerging digital technologies seems noticeably more pessimistic. As one physician managing a large team at a big pharma wrote me this week, reacting to a STAT article on a disappointing clinical sepsis-prediction algorithm:

“Hard for me to mentally reconcile real world experience like this with all the hype around how we probably won’t need doctors in the near future because ‘AI will do the same work, but better,’ and we won’t really need drug hunters either because AI is going to solve that too.”

This operator candidly captures the current sentiments of most practicing physicians and drug developers, who’ve noticed that the promise of emerging digital technologies doesn’t yet appear to have found consistent, meaningful, impactful expression in their work. 

If digital technologies are going to transform the industry, where is this change? And when are we going to start to see palpable and persuasive examples, rather than continuing to learn on theoretical use cases and rosy consultant forecasts?   

Time Course Of Innovation

For starters, we should consider the life cycle of innovation (a topic I’ve discussed at length here), based on the model of economist Carlota Perez. 

Short version: it characteristically takes a long time — a really long time, on the order of decades — to go from a raw discovery to implementation at scale. It’s hard for people to get their heads around something that’s new. Often, a series of incremental tweaks are required before a novel concept can achieve widespread use. 

This phase – trying to get our hands around emerging digital and data technologies – is exactly where we are now in healthcare. Figuring out how to apply these technologies effectively is both our abiding challenge and our remarkable opportunity.

Often, we seem to have a magical view, of technology in general and of AI in particular. There’s even a phrase – “enchanted determinism” – that’s been used to lampoon the nearly-religious view that some have of AI, the view that AI will somehow solve what ails us. 

But AI is a tool not a deity, despite the reverential way it is spoken of by some impassioned advocates, entrepreneurs, and investors. It’s a useful tool for solving certain types of problems.

The next question is how do we begin to unlock and access the exceptional potential of emerging digital and data technologies in biopharma?

Here, we seem to be trapped between two extremes – magic and nihilism:

Magic in the sense of the belief that a thorny problem can be resolved simply by applying a powerful technology, generally AI. It can often seem like AI is pitched as the solution for every problem, including those that haven’t yet been identified. The tendency to exaggerate the potential of a new technology to promote adoption only adds to the challenge. The almost inevitable failures here tend to reinforce a sense of…

Nihilism, a belief that emerging digital and data technologies are so overhyped as to be functionally useless at best, and a waste of time and resources at worst. Busy drug developers often find themselves figuring out how to avoid getting dragged into what they see at the latest innovation initiative so that they can instead use the time to get their actual work done.

Slope of Enlightenment?

So why am I still so optimistic about the potential for emerging digital and data technologies to radically improve the way new medicines are discovered, developed, and delivered?

Short answer: because we’re learning. 

1. Tech seems to have developed a more nuanced appreciation for the complexity of healthcare, drug development, and the human organism. Deep domain expertise is critically required for success – one of the reasons that leading technology companies have recruited and organizationally empowered some of the world’s most thoughtful and integrative physician-scientists, including Dr. Taha Kass-Hout, now at Amazon, and Dr. Amy Abernethy, who recently joined Alphabet’s Verily.
2. Healthcare organizations and the biopharmaceutical industry wrestle every day with a range of challenges that digital and data technologies should be able to help address. Data obviously are critical to the durable maintenance of health, the effective treatment of disease, and the efficient development of meaningful new therapies. There remains an urgent, desperate need to upgrade the way we gather, utilize, and share information. This was a critical learning from the pandemic, including in a soon-to-be-published analysis that Abernethy, Microsoft’s Peter Lee, and I, along with an extraordinary team of collaborators, conducted as part of a more comprehensive initiative organized by the National Academies of Science, Engineering, and Medicine (stay tuned!).
3. Tech, once seen largely as an entrepreneurial force for good, is now evaluated far more critically – this is essentially the theme of every AI book I’ve reviewed for the Wall Street Journal in last several years (see here, here). The good news here is that this fall from grace has prompted many tech companies to engage more thoughtfully and collegially with healthcare and biopharma companies (as I allude to here).
4. Perhaps most importantly, digital and data technologies are becoming less exotic and more normalized – capabilities that are starting to be more routinely incorporated into the training of physicians, scientists, and business executives. I remember how excited I was when I bought my first smartphone; in contrast, my teenage kids view smartphones as ordinary, an established component of their world. For them, smartphones use is unremarkable and routine. Future generations of physicians and drug developers are likely to view the application of today’s emerging digital and data technologies in much the same way.

Worked Example: Lilly

To see how these trends are starting to play out, we can consider the example of Lilly, a company that, for the first time, was ranked top in innovation in Idea Pharma’s annual Pharmaceutical Innovation Index, released in April 2021.

Last week, at the “Future of Health Data Summit” in Washington, DC organized by Datavant, Lilly’s CEO Dave Ricks was interviewed onstage by Martin Chavez about Lilly’s approach to digital and data technologies. Ricks took the reins at Lilly in January 2017; Chavez is the former chief financial officer and chief information officer of Goldman Sachs and currently partner and vice chair of the global investment firm Sixth Street Partners.  (Note: I have no relevant disclosures related to either Datavant or Lilly.)

Chavez’s interview of Ricks covered two general areas:

–       Examples of impact/examples of continued challenge

–       Approach to organization and talent

Lilly: Examples of Impact

In 2019, about a year after Vas Narasimhan became CEO of Novartis and told the Wall Street Journal he aimed to “become a focused medicines company that’s powered by data science and digital technologies,” Narasimhan reflected on his early learnings. 

On the positive side, he cited AI’s contribution to the company’s finance and clinical trial operations. But he also acknowledged that the “The Holy Grail of having unstructured machine learning go into big clinical data lakes and then suddenly finding new insights” remained elusive. 

What was perhaps most striking about Chavez’s recent discussion with Ricks was how similar many of the themes were. Ricks similarly emphasized the use of data on the commercialization side – “pricing problems…spend optimization problems” while also pointing out this use case is “pretty well-trodden in pharma.” 

Also like Narasimhan, Ricks highlighted the value of data in improving clinical trial operations, calling out in particular a clinical trial optimization capability, which he described as “a virtual tool” used by Lilly to run simulations seeking to optimize trial protocols by examining the effect of tweaking a range of parameters on outputs such as projected recruitment speed and screen failure rate.

Ricks seemed far more cautious about the contribution of AI to the discovery phase of their work, although he acknowledged the apparent success of BenevolentAI’s approach in identifying Lilly’s marketed rheumatoid arthritis drug baricitinib (a JAK kinase inhibitor marketed as Olumiant) as having potential application in the treatment of COVID-19.

Subsequent clinical studies have documented the utility of the medication in a subset of hospitalized patients, as MGH infectious disease physician Rajesh Gandhi concisely summarizes here. Gandhi tells me the data suggest “baricitinib has an important role in hospitalized patients who are rapidly worsening and have evidence of inflammation.”

More generally, however, Ricks remained guarded about the application of AI to discovery. “The idea that you could just give a biology problem to a computer and it will tell you what a drug design is for it is not a reality, and it’s not going to be a reality for maybe a decade or more,” he said.

Asked by Chavez to identify the key obstacles in leveraging data, Ricks explained that “in life sciences, in general, the problems…are the big ones, which is we don’t have enough data or enough understanding about how to organize it to simulate the human organism. I think that’s a big problem.”

In contrast, Ricks said, “The easier problems are from the market working in.” In Ricks’s view, many of the commercialization challenges in pharma are not fundamentally different from other sectors, “there’s just different data.”

Manufacturing challenges in pharma, Ricks suggested, are also similar to other industries. As Siebel mentions in his book, Ricks pointed to the application of data tools to optimize predictive maintenance.

While clinical trials are obviously not a component of most other industries, they also represent, Ricks says, an “operational problem” and thus are also ripe for data-driven optimization that doesn’t rely on deep biological understanding. “We don’t need to envision how liver cells interact with heart cells to understand this,” he explains. “We just need to know our own operation and then work on ways to optimize that.”

Ricks argues Lilly’s efforts here over the last decade have reduced drug development cycle time (the time between IND filing and FDA approval) significantly, from over 12 years to under six years on average). He said Lilly,and the industry more generally, could get even faster in drug development, saying five years from IND to NDA is an appropriate goal.

In contrast, on the discovery side, Ricks doesn’t expect similar holistic, data-enabled improvements in the overall process, but believes “the discovery side will be more about tools that become platforms that eventually become solutions.” He adds, “we have to start somewhere.” In other words, AI might not comprise the whole “toolbox,” but might offer “a tool or two…that can help the human medical chemist be better at their job, speed up certain processes, and allow you to find success.”

Several specific data tools have yielded promising results in discovery, Ricks says. For example, the company has had some success “applying ML tools to very specific organic chemistry problems.”

He also highlighted the company’s positive experience using computers to profile protein structures, and predict which chemical entities in a known library are likely to have the best fit – a useful shortcut, he says. 

One pleasant surprise: the utility of algorithms in monoclonal antibody development. Although biologics tend to be large molecules, he says, the relevant interface space is much smaller and essentially more manageable and predictable. 

“It seems paradoxical,” he acknowledges, “because biologics seem more complicated, but in this way they’re less complicated.” He asserts this sort of approach saves “maybe 15, 20% of the time on monoclonal antibody discovery,” adding “we use it routinely.”   

Lilly: Approach to Organization and Talent

Since becoming CEO in 2017, Ricks says, he has focused on the company’s digital and data strategy and associated company organization. He’s placed someone specifically in charge of data and data strategy, and established a centralized group that’s in charge of all enterprise data management and analytics and yet does not operate through a “centralized model.”

Instead, Ricks describes the group as working through something more like a “consulting model,” where data scientist careers belong to the Center (for data and analytics), but their job reports to the function they’re in (such as clinical pharmacology or new product planning). They roll back to the data center when a project is complete, he explains, and then wait to go out to work on the next suitable problem.

Ricks notes that the market for data scientists is “hotter than anything,” and “so competitive,” and notes “10% of our [data scientist] roles are open any given time.”

One of the most interesting observations shared by Ricks was the apparent success of a program that enabled interested, traditionally trained scientists to “go back to school while they work,” receiving additional training (on Lilly’s dime) as data scientists. When they return, Ricks says, “they become the data scientist partner of their former lab mates.”

According to Ricks, this represents “a much better approach than just dropping in theoretically trained data scientists who then have to learn everything about chemistry and biology, which is complicated.”  Ricks says because the model has been so successful, it’s been extended from discovery scientists to include clinical development researchers as well.

In other words, according to Ricks, it seems to work out better, at least at Lilly, to teach traditional (but motivated) pharma researchers about data science than trying to teach traditional data scientists about pharma, suggesting the industry domain expertise is what’s most difficult to learn.

It’s not clear this choice will even be necessary in the future. 

As Chavez, the former Goldman Sachs executive, pointed out, data science first came to finance several decades ago. Today, he says, “people who are traders are also data scientists. So rather than having people in different roles with different experiences collaborating together, you wanted to get it all in the same body.” 

Ricks emphasized that he didn’t think everyone at Lilly needed to become a data scientist, though he said data are part of everyone’s job. Consequently, over the last three years, Lilly has “run a pretty comprehensive retraining of the entire workface on basic tools,” with an emphasis on teaching employees how to “self-serve your own data queries and how to access the data you need to access in this enterprise dataset – and know when you’re at your edge and when you need to call in an expert.”

Bottom Line

Between the breathless promises and ensuing disappointments, it’s easy for clinicians and researchers to become disillusioned, and write off the application of digital and data technologies as just the latest innovation trend. 

This would be a mistake.

Emerging digital and data technologies are following a familiar innovation journey, and we are incredibly fortunate to be at one of medical history’s most exciting inflection points. Clinical providers and medical researchers in universities and industry – those on healthcare’s front line — have the remarkable opportunity – and arguably also the obligation — to figure out how to leverage powerful but still relatively raw technologies, and come up with a way to apply or adapt them to our most pressing health challenges. 

Bridging the gap between technology and application absolutely requires the insight, experience, and expertise of those in the trenches, actively wrestling with problems and intensively searching for more effective solutions. Both the engineers developing new technology, and the lead users in healthcare and biopharma who seek to apply it, increasingly recognize the urgent need for partnership and creativity if the potential associated with emerging technologies is to be translated into durable improvements in human health.