David Shaywitz

Today, we’ll begin with a consideration of the promise for AI some experts see in healthcare and biopharma.

Next, we’ll look at some of the obstacles – some technical, some organizational – and re-visit the eternal “data parasite” debate.

Finally, we’ll conclude with a few suggestions for summer reading.

The AI Opportunity: Elevating Healthcare for All

Earlier this month, I moderated a conversation about AI and healthcare (video here, transcript here) at Harvard’s historic Countway Library of Medicine, in a room just down the hall from a display of Phineas Gage’s skull and the tamping iron that lanced it on September 13, 1848, famously altering his behavior but sparing his life.  The episode soon became part of neurology history and lore.

With less overt drama, but addressing a topic of perhaps even greater biomedical importance, the panelists – Harvard’s Dr. Zak Kohane, Microsoft’s Peter Lee, and journalist Carey Goldberg (all co-authors of the recently published The AI Revolution in Medicine: GPT-4 and Beyond, discussed here), addressed their subject.

A key opportunity for AI in health that Kohane emphasized was the chance to elevate care across the board by improving consistency. He told the story of a friend whose spouse was dealing with a series of difficult health issues.

Innovation image created on DALL-E.

Kohane said his friend described “how delightful it was to have a doctor who really understood what was going on, who understood the plan. The light was on.”

However, Kohane continued, the friend would then “go talk to another doctor and another doctor, and the light was not on. And there was huge unevenness.”

The story, Kohane reflected, “reminds me of my own intuition just from experiencing medical training and medical care, which is there are huge variations. There are some brilliant doctors. But there are some also non-brilliant doctors and some doctors who might have been brilliant but then are harried, squished by the forces that propel modern medicine.”

Kohane says he saw Chat-GPT as a potential response to physician inconsistency. For Kohane, generative AI represented a disruptive force that “was going to happen, whether or not medicine and the medical establishment were going to pick up the torch.” Why? Because “patients were going to use it.”

Goldberg, too, recognized the opportunities for patients, and spoke to the urgent need she felt to access the technology:

“Okay, we get it. It has inaccuracies, it hallucinates. Just give it to me. Like, I just want it. I just want to be able to use it for my own queries, my own medically related queries. And I think that what I came away from working on this book with was an understanding of just the incredible usefulness that this can have for patients.”

Goldberg also shared a story of a nurse who suffered from hand pain and was evaluated by a series of specialists who were unable to identify the cause. Desperate, the nurse typed her symptoms into Chat-GPT, and learned that one of her medications could be causing the pain.  When this was changed, the pain resolved.   

Kohane sees the ready availability of a savvy second-opinion a tremendous resource for physicians. When he was training, he said, the physicians used to convene after clinic and review all the patients. “Invariably,” he notes, “we changed the management” of a handful “because of what someone else said. That went away. There’s no time for it.”

Innovation image created on DALL-E.

The lack of review represents a real loss, Kohane points out, because “even the best doctors will not remember everything all the time.” Kohane says he is convinced that generative AI will restore this capability and enable it serve a co-pilot function, providing real-time assistance to busy providers.

Another opportunity to make physicians’ lives better, the panelists suggested, was in the area of paperwork and documentation, such as the dreaded pre-authorization letters, often required to beseech payors for reimbursement. 

Since Lee contributed an entire chapter about the impact on paperwork reduction in healthcare, I asked him whether we’re just going to see AI’s battling with each other: provider AI’s writing pre-authorization letters, and payor AI’s writing justifications for rejection. 

Lee responded that this was very similar to a scenario Bill Gates has mentioned, where an email starts as three bullet points you want to share, GPT-4 translates this into a well-composed email, then GPT-4 at other end reduces it back to three bullet points for the reader.  

I told Lee this reminded me of Peter Thiel’s famous quote: “We wanted flying cars, instead we got 140 characters.” Surely, I asked, generative AI must offer healthcare something more profound than more efficient paperwork? 

In response, Lee highlighted the opportunities associated with the ability to better connect and learn from data – perhaps getting us closer to at long last fulfilling the elusive promise of a “learning healthcare system” (see here). In particular, Lee highlighted the potential of AI serving as a “universal translator of healthcare information,” allowing for the near-effortless extraction and exchange of information. 

For more perspectives on how AI could benefit healthcare and the life sciences, I’d recommend:

1. A recent Atlantic piece by Matteo Wong emphasized the opportunities for leveraging multi-modal data – a topic Eric Topol and colleagues have consistently highlighted.
2. This short Nature Biotechnology essay, by Jean-Philippe Vert, a data science expert who now leads R&D at the health data company Owkin.  Vert describes four ways AI may impact drug discovery.  Of particular interest: his suggestion that generative AI might provide a “framework for the seamless integration of heterogeneous data and concepts.”  However, he acknowledges that “How exactly to implement this idea and how effective it will be largely remain open research questions.”
3. This recent Nature Communications paper from M.I.T. and Takeda (disclosure: I work at Takeda, but wasn’t involved in this collaboration), demonstrating an application of AI in manufacturing. This operational area seems especially amenable to AI-driven improvements, in part because the richness and completeness of data capture (see also here).

Pesky Obstacles to AI implementation

The inconvenient truth is that while generative AI and other emerging technologies have captivated us with their promise, we’re still figuring out how to use them.

Innovation image created with DALL-E.

Even user-friendly applications like ChatGPT and GPT-4-enabled-Bing are not always plug-and-play. For example, in preparation for an upcoming workshop I’m leading for a particular corporate function highlighting the capabilities of GPT-4, I tried out some of the team’s most elementary use cases with Bing-GPT.  The results were disappointing and included a number of basic mistakes. Often, Bing-GPT seemed to perform worse than Bing or Google search alone.  The results generated seemed unlikely to inspire corporate colleagues to urgently adopt the technology.

These challenges are hardly limited to GPT-4 or Bing. From the perspective of a drug development organization, technology issues seem to surface in every area of digital and data.  Far more often than not, the hype and promise touted by eager startups seem at odds with the capabilities these nascent companies demonstrably can deliver. In fairness, the difficulty many legacy biopharma companies have figuring out how to work in new ways with these healthtech startups probably also contributes to the challenge. 

To understand the issues better, let’s consider one example, outside of biopharma, recently discussed by University of North Carolina Vice Chair and Professor of Medicine Spencer Dorn.  His focus: the adoption of AI in radiology.

Dorn notes that while AI expert Geoffrey Hinton predicted in 2016 that AI would obviate the need for radiologists within five years, this hasn’t happened. In fact, Dorn says, only a third of radiologists use AI at all, “usually for just a tiny fraction of their work.” 

Dorn cites several reasons for AI’s limited adoption in clinical radiology:

• Inconsistent performance of AI in real-world settings, compared to test data;
• AI “may be very good a specific tasks (e.g. identifying certain lesions)…but not most others”;
• “Embedding AI into diagnostic imaging workflows require time, effort, and money,” and basically, the juice doesn’t seem to be worth the squeeze;

Dorn warns that generative AI “in healthcare will need to overcome these same hurdles. Plus, several more.”

Similar issues apply to the adoption, for high-stakes use-cases, of a range of emerging technologies, including digital pathology, decentralized trials, and “the nightmare” of digital biomarkers – challenges this column has frequently discussed.

Innovation image created on DALL-E.

But remarkably, technology problems are probably not the most difficult issue for healthtech innovators to solve. Technology tends to improve dramatically over time (think about the camera on your smartphone). No, the most difficult sticking point may well be organizational behavior.  Essentially, the return of the eternal, dreaded “Data Parasite” debate (as I discussed in 2016 in a three-part series in Forbes, starting here.) 

In most large organizations, both academic and corporate (I am unaware of many exceptions), there is a constant battle between those who effectively own the data and those who want to analyze the data. In theory, of course, and depending upon the situation, data belong to: patients / the organization / taxpayers, or some combination of the three. Researchers, meanwhile, are just “stewards” or “trustees” of the data. Yet in practice, someone always seems to control and zealously guard the access to any given data set within an organization.  

Typically, those who “own” the data (whether an academic clinical investigator or a pharma clinical development team) are using the data to pursue a defined, high-value objective. Others who want access to these data tend to have more exploratory tasks in mind. Theoretically, there’s a huge amount of value that can be obtained by enabling data exploration. Once again, in practice, the theoretical value is often difficult to demonstrate, and is often viewed as offering little upside – and a fair amount of perceived downside risk, as well as gratuitous aggravation – to the data “owners.” Much of this perceived risk relates to the concern about sloppy or ill-informed analyses that generates, essentially, “false positive” concerns, as I allude to here.

I’ve seen very few examples where the data “analyzers” have sufficient leverage to win here.  In general, the data “owners” tend to hire data scientists of their own and say “let us know what you want to know, and we’ll have our people run the analysis for you.” This has the effect of slowing down integrative exploratory analyses to a trickle, particularly given the degree of pre-specification the data “owners” tend to require.

If you are a data owner, you probably view this as an encouraging result, since analyses are only done by people who ostensibly have a feel for how the data were generated and understand the context and the limitations. As discussed in a previous column, “data empathy” is vitally important. 

But if you are a data analyzer not working directly with a data “owner,” you are constantly frustrated by the near-impossibility of obtaining access to data you’d like to explore. Perhaps most strikingly, many researchers who fiercely defend their own data from external analyses are often fiercely critical of others for not sharing data the same researchers hope to explore. As Miles famously observed, “where you stand depends on where you sit.”

Innovation image created on DALL-E.

Of course, it’s possible that technology could help ease sharing. Even so, it’s really difficult to envision the tight hold on data changing, so long as so much power in organizations clearly rests with those in control of the data. Perhaps, as Lakhani and others suggest, this can be addressed in new companies who have a fundamentally different view of data (Amazon – driven by the “Bezos Mandate” — is the canonical example), and can readily monetize data fluidity.  Alternatively, the demonstrated utility of exploratory integrated analyses across multiple data silos and “owners” in legacy organizations could potentially facilitate more consistent access. 

For now, in both academia and biopharma, virtuous stated preferences to the contrary, this revealed tension remains very much alive.

Briefly Noted Summer Reading

A must-read for all biotechies, For Blood and Money, by Marketwatch’s Nathan Vardi, tells the captivating story of two cancer medicines targeting the BTK kinase: ibrutinib and acalabrutinib.  A decade ago, for Forbes, I wrote about the beginning of the ibrutinib story. 

It was thrilling to read Vardi’s account of the medicine’s complete journey – and the journey of its competitor, acalabrutinib (which, fun fact, was originally discovered by the same company in the Netherlands that discovered the product that became the blockbuster Keytruda, see here). As Jerome Groopman’s thoughtful review in the New York Review of Books suggests, Vardi’s book also raises difficult questions about the role of luck vs skill in drug development, as well as the role of capital vs labor, since the investors appeared to make out far better than the scientists who did the lion’s share of the work. This pithy review by Adrian Woolfson, in Science, also provides a good summary.

Less essential but fascinating for readers who recall the rise of companies like Gawker and Buzzfeed, is Traffic, by Ben Smith. He describes how emerging media companies – and the young men and women who contributed the content – desperately chased reader traffic, with important consequences both for them and society. See here for an excellent review of the book by the Bulwark’s Sonny Bunch.

Also intriguing, if a bit uneven: Beyond Measure, a book about the history of measurement, written by James Vincent, Senior Reporter at The Verge. See here for a thoughtful review of Vincent’s book by Jennifer Szalai in The New York Times.

Finally, a few recommended posts. On the concerning side, this piece about the devolution of clinical medicine captures what I seem to be hearing from nearly every single physician I know.  Even doctors who were once so excited about taking care of patients now seem abjectly miserable, trapped in a system that has reduced them to widgets. (See also here, here, here.)

On the innovation front, several comments about the wildly popular GLP-1 medicines tirzepatide and semaglutide caught my eye (see also my last piece, here). On the one hand, it’s clear the development of these powerful and promising medicines was, as Dr. Michael Albert of Accomplish Health suggests, clearly the result of deliberate, meticulous effort, both by companies like Lilly and Novo Nordisk, and pioneering academics like physician-scientist Daniel Drucker (who also maintains this authoritative website on the evolving science). On the other hand, it’s interesting that (as Sarah Zhang writes in The Atlantic), these medicines may have entirely unanticipated application in the management of addictions and compulsions.

Bottom Line

Generative AI offers the possibility of elevating the quality of healthcare patients receive.  However, the implementation of AI and other digital technologies may be impaired both by the growing pains of nascent technology and, more significantly, by the territoriality of those who control access to data silos within large organizations — although this may also ensure that the data are more likely to be analyzed by those who have a greater feel for the context in which they were generated). Finally, For Blood and Money, by Nathan Vardi, Traffic, by Ben Smith, and Beyond Measure, by James Vincent are all good additions to your summer reading list.

David Shaywitz

Biopharma relies on innovation to stay in business. Success depends on our collective ability to discover, develop, and deliver new products that cure or meaningfully mitigate disease over and over again.

Patents allow for innovators to be rewarded, for a while. When patents expire, allowing us to purchase powerful generic medications like atorvastatin for pennies, manufacturers must come up with something new to support the enterprise.

The pressure to discover and develop the big new thing is intense.

We have seen remarkable advances in many areas, including cystic fibrosis and of course the rapid development of COVID vaccines. We routinely contemplate a range of modalities that a decade ago would have been considered fanciful (see here). We also acknowledge that, tragically, many dreadful conditions like glioblastoma multiforme, pancreatic cancer, and amyotrophic lateral sclerosis remain largely resistant to our efforts – so far.

While we recognize the value of innovation, we also appreciate that often, there seems to be a lot more heat than light, at lot more self-congratulatory social media posts than real evidence of progress.

The Harm of Innovation Theater

Writing this month in Forbes, Dr. Sachin Jain, a physician-executive with experience across all of healthcare, from academic medicine to pharma to payors, plaintively expressed his frustration with the excessive celebration of innovation. He called out the dichotomy between the triumphant characterization of innovation by many healthcare and biopharma organizations, and the often far less impressive reality.

Sachin Jain

“I was struck by the difference between what I read and [what] I was seeing on the ground in practice,” he writes, noting, for example, that many highly-touted advances were only small pilot programs, and never actually scaled (or planned to scale).   

He’s previously described the difference between what he calls the “change layer” – “the cloud in which visionary ideas about transforming healthcare resides” – and the “reality layer,” the place “where most care is delivered.” While both layers are necessary, he writes, he’s observed “little mixing between them.”

Moreover, he suggests, the change layer perversely may insulate organizations from real change by providing a conspicuous, dynamic narrative around innovation and disruption, even though these innovations and disruptions rarely meaningfully permeate into the day-to-day business of the company. He cites several examples of prominent healthcare demonstration projects that persist (if at all) only as isolated examples.

Jain is hardly the first to note the distinction between speaking and acting. Aesop, born more than two and half millennia ago, reportedly observed, “when all is said and done, more is said than done.” More recently, University of Chicago economist John List has examined, in The Voltage Effect, some of the reasons why promising pilots often fail to scale. 

But Jain is making an important, somewhat more provocative point: that our relentless celebration of innovation sustains a false illusion of progress, enabling incumbents to highlight their commitment to change while continuing to practice business as usual.

While his current focus is on healthcare, he’s also discussed some of the challenges he observed when he worked in pharma, writing:

“I watched with curiosity as the industry launched countless initiatives to move ‘beyond the pill’ to build services and solutions business to enhance patient outcomes, only to undercapitalize them and quietly shut down without notice. The industry was unable to sustainably think about a future outside of high margin molecules – just as many hospitals are unable to think of a future without fee-for-service.”

Here, of course, we immediately think of the famous observation by Upton Sinclair in 1934, “It is difficult to get a man to understand something, when his salary depends upon his not understanding it.”

Jain, to be sure, acknowledges that disruptive innovation is, by definition, difficult. But what worries him is that the gap between the innovation we trumpet and the innovation we implement seems to be growing, cultivating an abiding sense of deep cynicism – call it disruptive innovation fatigue — in the trenches, which makes true change even more challenging and less likely. 

I can think of several examples from digital and data: we constantly hear about triumph of distributed clinical trials, which bring clinical trials to the patient.  This is truly a worthy and important goal. Yet the success of these endeavors has been far more limited, the logistics far more difficult, and the impact far less profound, than the constant publicity would suggest.  It is perhaps not surprising, for instance, to hear that CVS is shutting down its nascent clinical trial business.

Similarly, we are constantly hearing about the great success of AI drug discovery. I am extremely optimistic about the ability of AI to dramatically improve aspects of the process. Nevertheless, the realized impact to date has been far less than publicity would suggest. I recently read in a prominent publication about a supposed triumph of AI-based drug discovery by a VC-backed startup, leading to an attractive licensing deal around a promising molecule. 

Perplexed by this “retconning” – a term from cinema and politics that refers to “retroactive continuity,” revising an established narrative to align with a new storyline — I pinged one of the founding venture capitalists. The VC was also amused by what the investor termed “revisionist history.”  Instead – and far more credibly — the VC attributed the success to the team of “smart people” doing structure-based drug discovery. 

Nevertheless, the investor shrugged, AI is “the buzzword of the day.”

At the far extreme of cynicism, I think about an assertion I once heard from a senior management consultant, who argued that at its core, big pharma is about clinical trial orchestration and product commercialization, rather than about innovative early research.  The consultant argued that the work in pharma labs essentially serves as a public relations distraction while corporations seek new products to license from biotechs.  As this consultant saw it, pharmas excel at orchestration at scale, rather than organic scientific innovation.  The key competencies of pharma, in this view, are successfully managing the incredibly complex processes required for global clinical development, international regulatory approvals, and worldwide commercialization. 

(I’ve also heard some suggest that the most significant contribution of discovery research teams in big pharma is understanding a field in enough detail to enable rigorous evaluation of in-licensing candidates.)

Efficiency Matters, Even For Innovators

Before we consider a more sanguine view of pharma innovation, it’s important to recognize that for all large organizations, even the most innovative, it’s critical not only to develop new products, but to ensure this is done with ruthless efficiency.

As World War II General Omar Bradley reportedly said, “Amateurs talk strategy; professionals talk logistics.” (Or, if you prefer Frederick the Great: “An army, like a serpent, goes upon its belly.”)  True, the vision for Apple’s success was developed by Steve Jobs – but the ability to make it happen required the supply chain management led by Tim Cook, who later became CEO. 

The Wall Street Journal recently profiled Zach Kirkhorn, the CFO of Tesla, who the Journal says performs a behind-the-scenes role similar to the one Cook played for years at Apple.  “While Mr. Musk revolutionized the auto industry by taking often risky bets that upended the status quo,” the Journal writes, “Mr. Kirkhorn earned a reputation for fine-tuning operations.”

The Journal quotes Tesla’s former Chief Technology Officer, JB Straubel, who says, “It’s probably the hundreds and thousands of hours of slaving away to make things incrementally better where he left the biggest mark and is leaving the biggest mark.”

Adds former Tesla board member Steve Westley, “Predictability is everything with a CFO. What you can’t do is surprise people, and he has not surprised people.”

Thus, while it’s exciting to imagine AI helping us come up with important new drugs, it’s not surprising that many of the earliest uses have been focused on improving process efficiencies (see here).

Pharma Innovation: Making The Elephant Dance

Efficiency may be necessary, but it’s hardly sufficient. A tight supply chain may be critical for the commercial success of Apple and Tesla, but only if these companies are producing innovative products that customers want to buy. 

For big pharma, innovation often means in-licensing the right products or acquiring the right biotechs, typically in oncology. Such transactions were critical to the recent success of Gilead (Kite, Immunomedics) and AstraZeneca (Acerta Pharma).

Encouragingly, several of big pharma’s most promising medicines of the moment were developed entirely in house. For example, Lilly discovered and developed both donanemab (for Alzheimer’s disease – see here) and tirzepatide (Mounjaro, FDA-approved for type 2 diabetes and likely soon, weight loss). (Notably, Novo Nordisk’s semaglutide [Wegovy/Ozempic], already FDA-approved for both diabetes and weight loss, was also developed internally.)

A recent, in-depth Wall Street Journal article by Peter Loftus examined Lilly’s R&D, and described a culture that underwent a profound change after a key acquisition – in this case, in the person of physician-scientist Daniel Skovronsky, the CEO of Avid Pharmaceuticals, a neuro-biomarker company acquired by Lilly in 2010. 

As he experienced the big pharma’s culture, Loftus writes, Skovronsky “was frustrated with Lilly’s slow pace. ‘Let me understand this,’ he recalled saying at a committee meeting setting timetables for getting experimental drugs to market. ‘Our goal is to be slower than average, and we’re failing at that goal? This can’t be the way to do things.’”

Consequently, in 2015, according to the Loftus, Lilly’s board asked Skovronsky (then senior vice president of clinical and product development), to “help analyze Lilly’s research flops over the prior 10 years and figure out how to do R&D better.”

Daniel Skovronsky

Skovronsky’s big conclusion: key decisions were being driven by commercial needs, rather than the best science.  Marginal products were advanced (only to later fail) because they targeted a specific commercial need.

According to the Loftus, Skovronsky recommended that “Lilly pursue drug projects where it best understood the science and lean less on commercial sales estimates. Lilly was not very good at predicting a drug’s sales over time anyway, he concluded, but could better predict the scientific probability of a drug’s success.” (I’ve discussed the challenge of predicting drug sales here, and also, in collaboration with Nassim Taleb, here.)

Skovronksy was soon promoted to Chief Science Officer and Chief Medical Officer, where he pushed to address another challenge he observed, endemic to large organizations (and described in excruciating detail by Safi Bahcall in Loonshots – see here, also here). 

As Loftus writes:

“One internal committee after another second-guessed every recommendation to advance a promising drug candidate. ‘The decisions got revisited every step of the way,’ recalled J. Anthony Ware, who led product development at Lilly before retiring in 2017.  The committees were intended to ensure thorough vetting, but in practice became a limiting process that squeezed out bold ideas, according to Dr. Skovronsky.”

To address this, Skovronsky “reorganized to move more quickly.”

Loftus continues:

“To stop the second-guessing of decisions, Lilly established independent internal units operating like biotech companies—with less bureaucracy and faster decision-making—to manage each of its high-priority drug projects,” including the one that would lead to Mounjaro.  Each unit “had its own board of directors, made up of senior researchers and executives from Lilly’s diabetes business unit. They were given a budget, and charged with making quick decisions on their own.”

For example, according to Loftus, “after a Lilly researcher proposed a last-minute change to the design of the second phase of human testing” for a study of tirzepatide, the review board “met within 24 hours and approved the change so the study could start on time.”

Lilly’s agility may be familiar to colleagues at smaller biotechs and also to those familiar with Pfizer’s CEO-led development of the COVID-19 vaccine (see here) but is otherwise not representative of how most big pharmas go about their business, as Bahcall trenchantly observes.

Loftus’s narrative about Lilly is also shared by several colleagues either at Lilly or who have deep familiarity with the company.

Bernard Munos

According one to colleague, the innovation expert Bernard Munos who spent 30 years at the company, Lilly’s CEO Dave Ricks (who took the job in 2017) played a critical role: 

“He understood that Eurekas cannot be scheduled, and that innovation is a byproduct of culture, not the outcome of a process – even if some amount of process is clearly necessary. He realigned his leadership team with like-minded executives and let Lilly’s talented scientists (and there were many), free from bureaucracy, return to what they loved doing: cutting-edge science and translation.”

Munos adds:

“In short, there was no magic recipe. Lilly’s scientists had innovation in their DNA but could not express it under the culture that swamped the company for a couple of decades. Lilly was not alone in its predicament. The whole industry got caught in the same warp. This was the heyday of Six Sigma and its black belts. In the 1990s, the scientists had lost the leadership of the industry to non-scientists, and the idea that you could de-risk drug R&D by codifying work into processes, optimized by efficiency experts, that would deliver innovation on demand, that idea really resonated with non-scientist leaders — as harebrained as it was to most scientists. Today, the pendulum has swung back.”

I suspect it may be reasonable to offer two cheers for Lilly here, for the success of their innovative mindset and agile approach. My reservation is that every success quickly finds a narrative. I don’t know of any biopharmas that have not conspicuously adopted a “biotech” approach and mindset and might well attribute any success to this structure. 

In other words, maybe Lilly’s recent success is attributable to their adoption of a more nimble approach, or maybe their products happened to work, and then the organizational characteristics – which may not be all that unique – are suddenly elevated.

(In the same way culture is said to eat strategy for breakfast, you could argue, especially in biopharma, that good luck, aggressively pursued [e.g. Merck’s Keytruda – see here] eats both.)

Consider Lilly’s decision to de-emphasize commercial influence. On the one hand, the observation resonates, at every level of R&D. For example, an industry colleague recently shared an example where translational oncology researchers evaluating early-stage compounds felt pressure to interpret coarse biomarker data in a fashion that would support the advancement of a compound into one of the company’s priority indications.

On the other hand, the deliberate and successful expansion into areas of high commercial value (e.g. oncology) are critical to the elevated stock prices now enjoyed by companies like Gilead. 

History, according to the old saw, is written by the victors. Unfortunately, this often leads to the most-repeated, least-actionable strategic advice in our industry: pick winners, our equivalent to “buy low, sell high.” Moreover, since the selection of winners often feels like a crapshoot, it’s not surprising that management tends focus on seemingly more tractable parameters, like improving operational efficiencies.

In the same way culture is said to eat strategy for breakfast, you could argue, especially in biopharma, that good luck, aggressively pursued [e.g. Merck’s Keytruda] – eats both

Bottom Line

The biopharmaceutical industry relies upon innovation to develop new products to replace the medicines whose patent protection has expired. As Sachin Jain observes, relentlessly hyping innovation, particularly early pilot projects that never scale, generates harmful cynicism.  We also recognize that even the most innovative companies, like Apple and Tesla, still need to pay attention to the unsexy details of supply chain optimization – and big pharmas must focus on improving process efficiencies as well.  Even so, efficiencies won’t generate the new products pharma needs (though it might help them develop promising products faster).  Pharmas might learn from Lilly’s recent re-organization that seems to have liberated the innate creativity of company scientists.