David Shaywitz

Technology companies are experiencing a staggering reversal of reputational (though not financial) fortune; their stature seems reduced with each successive news cycle.  Gone is the halo many tech companies once enjoyed.

The implicit (and often explicit) assumption that tech innovation inevitably makes the world a better place has been replaced by real concerns that the picture may be far more mixed, a skeptical reaction that’s been called the “techlash.” 

Profound privacy worries abound — not surprising, given that the underlying business model of several leading companies is hoovering up all your personal information and using these data to precisely target ads. While behavior modification has always been the goal of advertising, we are only just beginning to see what the world looks like when precision-targeted digital advertising (of products, political campaigns etc.) becomes strikingly effective at influencing individual and group behavior.  (If only precision medicine was nearly as effective!). 

The idea that we should aspire to measure everything everywhere about everyone all the time, as some digital transformers cheerfully advocate, seems an increasingly questionable goal. The surveillance aspects, we now understand, are overtly invasive, while the benefits to the surveilled seem uncertain at best. 

The Wall Street Journal recently profiled a lobbyist for Oracle with a knack for providing real-time demonstrations of just how much data Oracle rivals like Google routinely collect.  During one meeting with Australian regulators, according to the Journal, an official noted that the Google operating system on a phone “was running for 13 minutes while we had been meeting, and had talked to Google at least 418 times….Second by second sensor readings, going straight to Google.”

One can’t help but wonder: What are Google and other data-hungry companies doing with our information – and who else may be accessing it? 

The incredibly detailed information gathered by large tech companies enables them to sell precisely targeted ads. The ability they have to measure engagement so effectively makes it easier to iteratively optimize such messaging, an approach that has now become an important component of political campaigns.  Deepa Seetharaman, writing in the Journal, recently described how the Trump campaign learned how to leverage social media remarkably effectively in 2016 (this episode of The Journal podcast especially recommended), and a captivating Atlantic piece “The Billion Dollar Disinformation Campaign to Re-elect the President” by McKay Coppins suggests these approaches will be an even larger factor in the 2020 campaign (as Coppins discusses in this episode of The Bulwark podcast).

While platforms selling ads clearly benefit by capturing rich data from apps and devices, the upside for users is debatable – even when users are the ones capturing the information for themselves. 

Consider the experience of many quantified selfers, such as former Wired editor Chris Anderson.  “After many years of self-tracking everything (activity, work, sleep),” he tweeted in 2016, “I’ve decided it’s ~pointless. No non-obvious lessons or incentives :(”.

We have also seen how technology, even where successful along some dimensions, can generate significant unanticipated consequences, including exacerbating problems they promised to solve.  One conspicuous example: ride-sharing apps like Uber and Lyft promised to reduce traffic burden, yet data suggest these apps have actually made traffic worse, according to fascinating recent article by Eliot Brown in the Journal. Brown also cites Facebook and Juul as other “solutions” that seem to have created serious new problems.

Hype, Disappointment, Resolution: All Part Of Tech Adoption Cycle

For those seeking to bring technology to health, the concerns surfaced by the techlash are not surprising, considering the historical arc of technology adoption, and need not staunch progress, if we respond thoughtfully rather than reflexively to these developments.

As Carlota Perez has described (see here), the path of novel technologies from initial emergence to widespread adoption seems to follow a predictable contour, which includes considerable hype and energy towards the start, as it becomes clear that there’s likely “something” to the technology but it’s not yet clear what this “something” is or might be. 

The British economist Christopher Freeman, in his introduction to Perez’s 2002 book, wrote, “the uncertainty which inevitably accompanies such revolutionary developments, means that many of the early expectations will be disappointed, leading to the collapse of bubbles created by financial speculation as well as technological euphoria or ‘irrational exuberance.’” 

In the Perez scheme, such disappointment regularly precedes the point at which the technology starts to become stably integrated in institutions and gains widespread acceptance, and it’s likely that we’ll see the same phenomenon with the application to healthcare of the four technology vectors entrepreneur Tom Siebel associates with digital transformation: cloud, big data, AI, and internet of things (IoT), as I recently discussed.

Arguably the best news about the increasingly pervasive concerns about technology is the grounded and often difficult dialog it forces about the range of consequences. In today’s more skeptical climate, it’s acceptable to view new technologies through a lens of healthy skepticism without being tarred as a mere “luddite,” a non-“it-getter,” and an exemplar of the sort of stodgy old-school thinking that’s about to be disrupted and displaced.

Needed: Authentic Mutual Respect

What we desperately need is authentic engagement between health and tech, from a position of mutual respect.  This theme has emerged from a number of recent conversations I’ve had, including just this past Friday, at a fireside chat I conducted in Philadelphia at the 2020 Wharton Health Care Conference with FDA Deputy Director Amy Abernethy, who has considerable experience bringing these worlds together – in academia, industry, and now government.

David Shaywitz in conversation with FDA deputy commissioner Amy Abernethy at the Wharton Healthcare Conference, Feb. 14, 2020.

My key takeaway from Abernethy – and others, including Recursion CEO Chris Gibson and Brandon Allgood, CTO of Numerate (now Integral Health) (the three of us discussed effective health/tech collaboration in January in San Francisco at a pre-JPM panel I moderated at the East/West CEO Conference), and J&J’s Sean Khozin (listen to our recent Tech Tonics podcast) – is the urgent need to get the working relationship between health talent and tech talent right, ideally by avoiding a “vendor mentality,” and ensuring the key stakeholders are on equal footing.

Getting health and tech experts collaborating effectively remains a critical stuck point for successful integration of health and technology – and arguably may represent an area where startups have a significant advantage.  Generally speaking, in tech companies, engineers are dominant, and many non-engineers, including physicians, tend to feel like second-class citizens; I hear this all the time from physicians and life-scientists connected with large tech companies, as well as tech-driven health startups. 

In many hospitals, the situation tends to be reversed, with physicians dominant in the hierarchy, and tech experts relegated to the role of service providers.  In pharma R&D, drug hunters and developers tend to rule the roost; the key data experts, statisticians, while acknowledged to be essential, are too often treated as service functions, called in to crunch numbers for sample size calculations, say, then sent back to their cubicles. (Of course there are exceptions, but this is the recurrent power dynamic I’ve seen throughout the industry.)

When one tribe is clearly dominant, it creates conditions that make it difficult to have thoughtful, dynamic back-and-forth between professionals of complementary skills. Without true mutual respect for what people bring to the table, it’s hard to imagine tackling effectively the highly complex problems at the nexus of health and tech. 

Solving this problem isn’t going to be easy, given the reluctance of those with power to relinquish it, but mutual authentic recognition of the value of both perspectives will go a long way. 

In our conversation at Wharton, Abernethy described an approach from Flatiron that she thought was effective: hackathons (internal software development competitions) where each team had to include not just programmers but also domain experts, whose knowledge was critical for success. 

The idea, more broadly, is that as people work together towards shared goals, their distinct skills become far more appreciated, and collaboration far more valued.  Right now, this is most apparent in startups (where the success of each person vitally depends upon everyone pulling in the same direction), while large organizations tend to be characterized by lip service to collaboration (especially when it’s a C-suite directive – see here, here), but intrinsic resistance to change, especially change that seems threatening or pointless.  From the perspective of many entrenched stakeholders in pharma and in healthcare systems, the change that “digital transformation” champions seems to offer a bit of both.

On the other hand, the folks I know in pharma R&D are there because they want to create impactful medicines to help patients; the people I know in care systems truly aspire to help patients. But all these experienced stakeholders – understandably — are very skeptical about grandiose promises; health and disease are inordinately complicated, and durably improving either drug discovery or care delivery is far more challenging than those outside these systems tend to recognize or acknowledge.  

Progress at the intersection of health and technology is achievable, but will also require established stakeholders and technology developers to collaborate effectively and respectfully, and evolve practical, tangible solutions.  As sociologist Robert Nisbet presciently wrote over a half century ago, “human beings do not come together to be together: they come to do something together.”  In this spirit, successful partnership between health and tech experts seems most likely to occur not just by co-locating such collaborators in a building or on a team, but rather, when the concerted engagement of both is required to accomplish something both meaningful and difficult.

I’m confident we will begin to see real success stories here – there are just too many real opportunities for just this sort of collaboration.  The interesting question is whether the biggest strides will come from legacy organizations that have managed to “digitally transform,” despite the huge political hurdles, or from startups that have managed to achieve traction despite the lack of legacy relationships and established capabilities. I would love to see both – and at this point, would be happy to see either.

David Shaywitz

Digital transformation (as I recently discussed), and the implementation of emerging technologies more generally, is routinely pitched by enthusiasts like Tom Siebel as both urgent and inevitable, something organizations need to embrace or risk irrelevance, if not extinction. 

Yet the “embrace or die” assertion is under increasing, and healthy, scrutiny, as the “techlash” (technology backlash) gains steam. 

“Surveillance Capitalism”: Tech As Force For Harm

Voices of concern have started to coalesce under the banner of what Harvard Business School professor emerita Shoshana Zuboff has termed “surveillance capitalism.” She synthesized and amplified this growing concern in her 700+ page 2019 book The Age of Surveillance Capitalism. For a shorter summary, I recommend reading this recent New York Times essay by Zuboff, and listening to this especially informative interview with her conducted by distinguished technology journalist Kara Swisher (of Recode and the Times).   

The core of Zuboff’s critique can be found in the story of Google itself, a company that (as described in the Recode podcast) initially came to prominence by building a phenomenally effective search engine that users appreciated. But the company struggled to make money in the early days, and “very swanky venture capitalists were threatening to withdraw support,” according to Zuboff. In an existential panic, Google apparently realized that it was sitting on a huge amount of interesting data, far more than was needed to improve the search algorithm. 

At its inception, reports Zuboff, Google had rejected online advertising as a “disfiguring force both in general on the internet and specifically for their search engine.” 

But spurred by the threat of extinction, Zuboff explains, Google declared a “State of Exception,” akin to a state of emergency, that “suspended principles” and permitted the company to contemplate previously shunned approaches. They recognized they had accumulated “collateral behavioral data that was left over from people’s searching and browsing behavior,” data that had been set aside, and considered waste. But upon further review, says Zuboff, Google engineers realized there was great predictive power in the combination of this data exhaust plus computation: the ability to predict a piece of future behavior — in this case, where someone is likely to click — and sell this information to advertisers. 

The result, according to Zuboff, was a radical transformation of online advertising, turning it into a market “trading in behavioral futures,” while claiming “private human experience” in the process.  “We thought that we search Google,” writes Zuboff, “but now we understand that Google searches us.”

As this model caught on, Zuboff explains, tech companies accrued exceptional influence, due to “extreme asymmetries of knowledge and power.” Over time, these companies began to “seize control of information and learning itself.”

These technology companies, asserts Zuboff, “rely on psychic numbering and messages of inevitability to conjure the helplessness, resignation, and confusion that paralyze their pray.” She argues “the most treacherous hallucination of them all” is “the belief that privacy is private.” It’s not, she argues, because “the effectiveness of … private or public surveillance and control systems depends upon the pieces of ourselves that we give up – or that are secretly stolen from us.”

Notably, Swisher strongly shares these privacy concerns, even writing a year-end commentary in the Times last December entitled “Be Paranoid About Privacy,” urging us to “take back our privacy from tech companies – even if that means sacrificing convenience.” She writes, “We trade the lucrative digital essence of ourselves for much less in the form of free maps or nifty games or compelling communications apps.” Adds Swisher, “It’s up to us to protect ourselves.”  

(In contrast to some health tech execs I know, Swisher views Europe’s General Data Protection Regulation [GDPR] and California’s recently-enacted Consumer Privacy Act as positive developments.)

Both Siebel and Zuboff seem to agree on the power of the emerging technology. They vehemently disagree about whether it’s a force for good or ill. 

The Pinker Perspective: Cautious Optimism

But another perspective is that both Siebel and Zuboff overstate at least the near-term power and utility of technology by accepting as a given that the impetus to collect every possible piece of data about every possible thing will soon result in remarkably precise predictions.

This is what Siebel promises, and Zuboff fears.

In contrast, I found myself agreeing with the more grounded viewpoint Harvard psychologist Steven Pinker offered in a 2019 discussion with Sapiens author Yuval Noah Harari (who was making the case for surveillance capitalism).

In recent years, Pinker has attracted controversy by arguing (in his 2018 book Enlightenment Now, and elsewhere) that despite endless lamentations and prophecies of doom, life is actually getting better, and is on a trajectory to improve still more. 

Besides Pinker, this encouraging perspective has been recently discussed by a number of authors including Hans Rosling (Factfulness), Andrew MacAfee (The Second Machine Age, More From Less – my Wall Street Journal review here), and John Tierney and Roy Baumeister (The Power of Bad – my Wall Street Journal review here).

Pinker says he’s not losing sleep about emerging technologies, in large part because he suspects the rate and extent of technological progress has been significantly overstated. Consider human genetic engineering, he says, where frightening concerns had been raised about engineering people with a gene that made them smarter or better athletes. That turned out to be a wild oversimplification, he argues – many genes impact most traits, and since genes tend to be involved in many functions, there’s a good chance any intervention would do at least as much harm as good. The limitations of genetic data is also something Denny Ausiello and I anticipated in this 2000 New York Times “Week in Review” commentary, and something Andreessen-Horowitz partner Jorge Conde thoughtfully reflects on in this recent a16z podcast.

Returning to AI, Pinker notes that “predicting human behavior based on algorithms” is “not a new idea,” nor one likely to immediately destroy the planet.  “I suspect,” Pinker says, “we’ll have more time than we think simply because even if the human brain is a physical system, which I believe it is, it’s extraordinary complex, and we’re nowhere close to being able to micromanage it even with artificial intelligence algorithms. The AI algorithms are very good at playing video games and captioning pictures, but they are often quite stupid when it comes to low probability combinations of events that they haven’t been trained on… even the simple problems turn out to be harder than we think.”

He adds, “When it comes to hacking human behavior – it’s all the more complex. Not because there’s anything mystical or magic about the human brain – it’s an organ – but an organ that ‘s subject to fantastic non-linearities and chaos and unpredictability and the algorithm that will control our behavior isn’t going to be arriving any time soon.”

In a 2018 op-ed, Pinker notes the “vast incremental progress the world has enjoyed in longevity, healthy, wealthy, and education,” and adds that technology “is not the reason that our species must some day face the Grim Reaper. Indeed, technology is our best hope for cheating death, at least for a while.” 

He describes threats such as “the possibility that we will be annihilated by artificial intelligence” as “the 21^st century version of the Y2K bug,” which was associated with apocalyptic prophesies, yet ultimately had negligible impact.

In a particularly interesting exchange between Harari and Pinker, Harari expressed concern that the surveillance state was turning our lives into a continuous, extremely stressful job interview, suggesting we’re heading to the point where everything we do every moment of our lives could be surveilled, recorded, and analyzed in a way that could impact future employment.

Pinker, in response, noted that “One of the most robust findings in psychology is that actuarial decision making – statistical decision making — is more reliable than human intuition, clinical decision making.  We’ve known this for 70 years but we typically don’t do what would be more rational.” In this example, it would be rational to scrap job interviews, and use statistically-informed predictors instead.  Even though we know job interviews are subject to bias and error, Pinker points out, we still use them, and don’t “hand it over to algorithms.” 

Of course, many technophiles – and technophobes — would say this is exactly what’s already occurring.

The Taleb Quadrant

There’s actually a fourth quadrant to consider – which I think of as represented by Nassim Taleb, who is critical (as he articulates with particular clarity in Antifragile) of what he sees as our worship of new technology, not because he fears it’s about to immediately lead to the end of life as we know it, but rather because he thinks our increased interconnectivity places us at greater risk of a catastrophic failure – i.e. make us far more fragile. He trusts approaches that have stood the test of time “things that have been around, things that have survived,” and worries about our “neomania – the love of the modern for it’s own sake.”

Implications for Health Tech

While perhaps inconvenient for some health tech entrepreneurs in the short term, the increasingly robust discussion about the impact of technology represents a positive development for the field.

Why positive? Because it creates the intellectual space needed to challenge tech assertions and assumptions, while demanding rigorous proofs of value. 

I incline towards Pinker’s perspective. Technology, in my view, offers us real hope in our efforts to maintain health and forestall and combat illness. Figuring out how to derive meaningful benefit from the technology will not be nearly as easy nor as rapid as consultants promise. As we work through these challenges, we need to be thoughtful and deliberate, and consider the right kind of guardrails we want to put in place as we bring ever-more powerful technologies to bear in our healthcare system. The hurdles we must clear – technological, social and political in nature – as we create systems that can meaningfully intervene and improve upon what we have in healthcare are enormous. We would be foolish to underestimate the work ahead – and even more foolish not to embrace the challenges and get going.

David Shaywitz

I had the privilege of serving as emcee for the “Data Science and AI” track on the first day of this week’s Precision Medicine World Conference (PMWC) in Santa Clara, CA, as well as chairing a panel discussion on data mining and visualization. 

I came away with a sense of optimism and need, organized around several key themes.

In Praise Of Incrementalism

In a day focused on technology, and featuring a number of startups, you might have expected to hear a lot about “disruption” and “disruptive innovation” – but I didn’t.  Instead, the watchword of the moment seems to be “incrementalism” – not in the dispirited sense of having minimal aspirations, but rather in the grounded (versus grandiose) sense of seeking to motivate buy-in from existing healthcare stakeholders by demonstrating a discrete and useful (if not super-sexy) benefit. 

Kaisa Helminen, the CEO of digital pathology company Aiforia Technologies (which I’ve written about here), emphasized the importance of first taking small steps, before attempting to make larger strides.  She amplified this point in a follow-up email:

“Labs should start with incremental steps in utilizing AI in digital pathology, e.g. starting with quality control (QC), workflow optimization or with a few applications that are painful for pathologists to count (e.g. counting mitosis) to get them used to the tech and to facilitate adoption.”

Similarly, Vineeta Agarwala, an impressive physician-scientist who recently joined Andreessen-Horowitz from GV, and who was previously a project manager at Flatiron, emphatically and repeatedly stressed the importance of incrementalism, even in the context of AI.  For example, she noted that at Flatiron, which focused on deriving clinical trial-like data from EHR data (see here), a key use of AI at this tech-driven company was…to determine which patient charts to spend time manually extracting the data from!  It seems unsexy, but apparently it delivered immediate benefits in operational efficiency.

Vineeta Agarwala

Grounded Health Tech Investors

A pleasant surprise at this conference was the number of VCs represented who both seemed interested in the nexus of tech and health and appeared to be approaching it in a grounded fashion, led by investors who have relevant domain experience. Greg Yap from Menlo Ventures, and Vijay Pande and Agarwala from Andreessen-Horowitz, particularly stood out. 

Pande emphasized there’s “nothing magical about AI,” and acknowledged that developing new drugs is not a fast process, as even compounds designed with the help of AI require, in his words, “the usual stuff” such as a battery of preclinical assays and extensive clinical trials.

Similarly, Agarwala described AI as simply “technologies to better learn from data,” and emphasized that “progress is going to be incremental.” Yap was perhaps even more cautious about AI, worried that we seem to be “at the peak of the AI hype cycle.”

Many (but not all) of the VC firms gravitating towards the “AI and data science” opportunity in healthcare and biopharma seem to be tech firms (Menlo Ventures, Andreessen-Horowitz, DCVC stand out) that have added domain expertise on the healthcare side, rather than healthcare VCs that have added domain expertise on the tech side; one conspicuous exception, perhaps, is Jim Tanenbaum’s Foresite Capital, a firm with deep healthcare roots that’s deliberately pursuing a technology dimension.

The Calcified Hairball Problem

The most dispiriting panel of the day, by far, was a discussion of interoperability led by Stan Huff of Intermountain, and featuring Michael Waters of the FDA, and James Tchung of Duke, describing (among other challenges) the excruciating ongoing effort required by the FDA SHIELD initiative to create a unifying schema for the representation of laboratory data. 

Hurdles seemed to be everywhere, and the realized rewards appeared uncertain at best.  The problem seemed to me to reflect the “calcified hairball system of care” to which VC Esther Dyson has famously referred. Listening to the panel describe the extensive painful effort involved in even the most basic efforts to extract meaningful information reinforced the sense that the existing system may be a virtually intractable mess; engaging with it seemed likely to result in a huge suck of time and money, with brutal political fights at every turn, and perhaps with little ultimately to show for the effort – the little juice you extract may prove not to be worth the squeeze.

Who could blame investors like Pande, then, who emphasized the value he sees for startups who think from the outset about how to collect data that (in contrast) works well with AI, and is designed from the ground up with that application in mind.  This seems to be the approach that prominent drug discovery startups like insitro (Andreessen-Horowitz-backed) and Recursion are taking, for example. 

While this doesn’t solve the problem about what to do about all the legacy data stuck in existing systems – which Tom Siebel, recall, describes as a (the?) competitive advantage of incumbent companies in an increasingly digital world — it feels like a contemporary example of what happened to factories after the arrival of electricity, as I described in this column last year. While most factories rapidly converted to electricity, established industries (due to sunk costs) were reluctant to extensively rework or reimagine their factories – they kept the design the same, and just substituted electricity for steam-power. The real beneficiaries were the emerging new industries, who had both the need and the opportunity to design work flows from the ground up, unencumbered by existing approaches. This led to the design of the modern factory. 

Similar new opportunities – where entrepreneurs can freshly leverage the power of new technology while minimizing dependency on the limitations of legacy technology – seem to represent the kind of investments that VCs like Pande are seeking out today.

Transparency and Trust

A thoughtful conversation between Atul Butte, a physician-scientist who oversees health data science for the entire University of California (UC) system (you can hear his Tech Tonics episode here) and Cora Han, UC Health’s newly-minted Chief Health Data Officer – explored why interactions with health systems and tech companies are now appearing so regularly in the news (see this WSJ, this WSJ, this WSJ, this FT, this JAMA commentary, and this JAMA commentary).   

Health systems contracting with technology companies is hardly new or unusual, Butte noted, wryly adding that it seems like only when specific names are attached to the two (such as “Ascension and Google”) that this common type of relationship is suddenly  portrayed as “sinister.” Cora suggested that factors contributing to the apparently escalating concern include (a) the potential for staggering scale, and (b) the theoretical intersection of medical and consumer data, which “seems scary.” She emphasized the foundational importance of “trusting the entities with whom you interact.”

Atul Butte

This connects with a related discussion of the role of transparency in increasing trust, a point several speakers emphasized. For example, Butte noted that if a company in stealth mode (meaning no information about it is publicly available) comes to him and asks to explore access to UC information, Butte tells them not to bother; if the company doesn’t even have a website and other basic information easily accessible, he’s not going to refer them to anyone in his organization.

Interestingly, several speakers on my panel – Helminen and Martin Stumpe (now SVP for data science at Tempus and previously the founder and head of the Cancer Pathology initiative at Google) – both emphasized the role of data visualization can play in fostering trust in technologies, especially AI, that can often seem inscrutable. 

At the same time, as Butte astutely suggested, there may be a bit of a double standard here in demanding this of technology since “physicians are also black box,” and can arrive at decisions of dubious quality via an uncertain and impenetrable process, as Atul Gawande and others have eloquently documented.

Regulation and outlook

Michael Pellini, a VC at Section32 (and former CEO of Foundation Medicine) expressed a strong sense of optimism regarding the near-term outlook for both technology itself and the approach to it he’s seen from regulators (more on this below). From a reimbursement perspective, he anticipated the outlook for therapeutics is likely going to get much worse (presumably a comment on the rising concerns around drug pricing), while diagnostics – where entrepreneurs have struggled for reimbursement for a long time, as Pellini presumably knows all too well — may see marked improvement in their future (presumably a comment on their increased ability to guide patients towards demonstrably better outcomes).

Michael Pellini

Similarly, life science VC (arguably the dean of life science VCs) Brook Byers effusively praised the commitment of the FDA to seek out improved technologies, citing two “heroes” – FDA Deputy Commissioner Amy Abernethy (see here, listen here for her Tech Tonics interview, and here on The Long Run) and FDA ophthalmology expert Malvina Eydelman.

His biggest worry, he said (a concern I share) is the sort of sentiment voiced in a recent NYT masthead editorial, urging the FDA to “Slow down on drug and device approvals.”  The Times argued,

“The F.D.A. has made several compromises in recent years — such as accepting ‘real world’ or ‘surrogate’ evidence in lieu of traditional clinical trial data — that have enabled increasingly dubious medical products to seep into the marketplace. [New FDA Commissioner] Dr. Hahn ought to take a fresh look at some of these shifting standards and commit to abandoning the ones that don’t work. That will almost certainly mean that the approval process slows down — and that’s O.K.”

To be sure, regulators have an intrinsically difficult task – if they’re too strict, promising drugs take longer to reach patients (if the medicines reach patients, or are even developed, at all); if regulators are too permissive, then patients can be exposed to harmful products before the danger is recognized.  However, as appealing as it may be to lean into the adage “first do no harm,” as critics such as the NYT are wont to do, invoking this perversion of the precautionary principle as a justification for moving slowly, it’s critical to realize the extensive harm that inaction can cause as well – as I’ve written here and elsewhere.  Regulators need to balance the totality of risk (including the harms of staunching innovation) and benefit; it’s an intrinsically difficult job given the inevitable uncertainty, and requires nuance and customization — “precision regulation” I’ve called it.

What should be avoided, as Tierney and Baumeister argue in The Power of Bad (my WSJ review here), is encouraging regulators to stomp on the brakes reflexively, driven by an outsized fear of risk, as if informed by the credo, “never do anything for the first time.”

Ultimately, what matters most (as I’ve argued) is real-world performance; a randomized clinical trial, where feasible and ethical, is the ideal approach to demonstrate the potential benefit of an intervention. But the most important parameter is what happens to actual patients taking medicines after approval.  Much of the anxiety experienced by regulators reflects the challenges gathering such data – thus once a medicine is released into the wild (even provisionally), it can be difficult to figure out if is working out as anticipated. 

Here is an opportunity. Improved ability to comprehensively gather and continuously evaluate such data as part of routine care would not only improve patient care, but could also make regulatory approvals less fraught. Visibly, we are a long way from this, yet it’s where we ought to be headed, and the direction, I’m increasingly convinced, healthcare is (slowly) starting to go.