Tag: David Shaywitz

Health Deserves A Vision More Capacious Than Dashboard Metrics

May 16, 2025

By DAVID SHAYWITZ

Consumer health and wellness is experiencing a flurry of activity.

The lab testing company Function (motto: “It’s time to own your health”) acquired Ezra, a whole body MRI company promising “the world’s most advanced longevity scan.”

Oura, maker of the popular smart ring, recently added an integration for continuous glucose measurement as well as the ability to calculate meal nutrition based on a photo. Oura also hired Dr. Ricky Bloomfield as its first Chief Medical Officer; Dr. Bloomfield had previously served as Clinical and Health Informatics Lead at Apple, and is known for his expertise in health data interoperability.

Meanwhile, Oura competitor Whoop, maker of a smart band, just announced the latest versions of its device, with the ability to monitor blood pressure, ECG, and to assess what it describes as a measure of biological age, which it calls “Whoop Age.” Whoop now says it seeks to “unlock human performance and healthspan,” enticing users with the pitch, “Get a complete picture of your health.”

Towards a Personal Health Operating System (OS)

Notice a pattern yet?

What unites these approaches and so many others, as the industry newsletter Fitt Insider (FI) recently observed, is they reflect an attempt to generate a “personal health OS,” intended to “give individuals agency over their well-being,” and more generally, wrest control back from a health system that’s often perceived (especially by young adults) as somewhere between useless and obstructive.

Citing a recent Edelman survey, FI reports,

…nearly half of young adults believe well-informed people can be as knowledgeable as doctors, two-thirds see lived experience as expertise, and 61% view institutions as barriers to care.

Fed up with reactive care, many already collect data across wearables, lifestyle apps, DTC diagnostics, and more, but most are siloed. Rolling up, Function is architecting a unified platform capable of generating clinically relevant insights from raw inputs.

FI points to the proliferation of companies like Bright OS, Gyroscope, and Guava Health focused on “day-to-day data management,” as well as startups like Superpower (“Delivering concierge-level metrics minus the PCP”) and Mito Health (a “pocket-sized AI doctor” that “generates comprehensive digital health profiles by merging labs, medical records, family history, lifestyle info, and more.”)

AI seems poised to play an increasingly central role in many of these companies.

FI speculates,

A step further, end-to-end LLMs could close the loop, linking cause and effect, turning insights into actions, syncing with PCPs, and laying the foundation for an AI-powered medical future.

This is a good time to take a deep breath – as well as a closer, more critical look at this vision of consumer-empowered, data-fortified health.

A Powerful Vision

Unquestionably, there’s a lot to embrace here, including in particular:

The opportunity for individuals to gather more and richer health data from a greater variety of sources, including in particular wearables;
The increased possibility of relevant insights (a key deficiency of early “Quantified Self” efforts) from these data.
The explicit centralization of your health data around you (Superpower’s tagline is “Health Data, In One Place”), a long-promised but often frustratingly elusive healthcare goal in practice. Today, still, (still!), so many patients find themselves having to beg and plead for efficient access to their own health information, data that health systems tend to view as a competitive advantage and aren’t eager to let go.

A tech-enabled approach to health where you have more abundant data about you, that are explicitly in your control, and which could lead to healthier behaviors represents the sort of progress that deserves to be celebrated.

At the same time, when I look at many of these approaches to health, I see two broad categories of concerns.

Concern One: Plural of Fragile Data May Not Be Insight

The first, perhaps more concrete worry, is that, to paraphrase comedian Dennis Miller, “two of [crap] is [crap],” and simply the collection of a lot of data, much of which may be fragile, isn’t sure to translate into brilliant insight, even if the magical power of AI is fervently invoked.

In an especially incisive “Ground Truths” blog post focused on “The business of promoting longevity and healthspan,” Dr. Eric Topol writes that “getting hundreds of biomarker results and imaging tests in an individual greatly increases the likelihood of false-positive results,” a concerning possibility.

I’ve discussed the challenge of false positives here, and get into some of the details around Bayes Theorem (which informs the assessment) here. The OG reference in this space may be this 2006 paper by Zak Kohane and colleagues, in which they introduce the term “incidentalome.”

To be fair, at least some of the proponents of extensive testing recognize the challenge of false positives but feel that the opportunity to collect dense data on individuals over time enables important inflections to be observed, a point Dr. Peter Attia explicitly emphasizes in Outlive; I discuss his “risk-management” mindset here.

Similarly, Nathan Price, a professor at the Buck Institute and the CSO of Thorne, has argued that close inspection (assisted by AI) of rich individual data could identify (for example) opportunities for supplement intervention. These interventions may not make much of a difference on the population level (hence the paucity of persuasive clinical trial data for supplements, as Dr. Topol notes in his latest book, Super Agers – my WSJ review here), but could in selected individuals. (I also discuss Price here, here).

Proponents of the “personal health OS” also might emphasize the presence of tailwinds – the likelihood of improved predictions as measurement technologies continue to get better, denser data become available, and the AI tools become ever-more capable. Perhaps we’re not quite at the point of realizing the future we imagine, advocates might argue, but we’re close enough to start to see what it might look like.

The False Choice Between Science And Economics

May 1, 2020• 1

By DAVID SHAYWITZ, MD, PhD

As the nation wrestles with how best to return to normalcy, there’s a tension, largely but not entirely contrived, emerging between health experts—who are generally focused on maintaining social distancing and avoiding “preventable deaths”—and some economists, who point to the deep structural harm being caused by these policies.

Some, including many on the Trumpist-right, are consumed by the impact of the economic pain, and tend to cast themselves as sensible pragmatists trying to recapture the country from catastrophizing, pointy-headed academic scientists who never much liked the president anyway.

This concern isn’t intrinsically unreasonable. Most academics neither like nor trust the president. There is also a natural tendency for physicians to prioritize conditions they encounter frequently—or which hold particular saliency because of their devastating impact—and pay less attention to conditions or recommendations that may be more relevant to a population as a whole.

Even so, there are very, very few people on what we will call, for lack of a better term, “Team Health,” who do not appreciate, at least at some level, the ongoing economic devastation. There may be literally no one—I have yet to see or hear anyone who does not have a deep appreciation for how serious our economic problems are, and I know of a number of previously-successful medical practices which are suddenly struggling to stay afloat amidst this epidemic.

Pressed to Demonstrate Utility, Digital Health Struggles — Just Like Traditional Medicine

Dec 1, 2019

By DAVID SHAYWITZ, MD

After absorbing several years of increasingly extravagant promises about the remarkable potential of digital health, investors, physicians, and other stakeholders are now unabashedly demanding: “Show me the data.”

By now, most everyone appreciates the promise of digital health, and understands how, in principle, emerging, patient-focused technologies could help improve care and reduce costs.

The question is whether digital health can actually deliver.

A recent NIH workshop, convened to systematically review the data on digital health, acknowledged, “evidence is sparse for the efficacy of mHealth.”

As Scripps cardiologist Eric Topol and colleagues summarized in JAMA late last year,

“Most critically needed is real-world clinical trial evidence to provide a roadmap for implementation that confirms its benefits to consumers, clinicians, and payers alike.”

What everyone’s asking for now is evidence – robust data, not like the vast majority of wellness studies that experts like Al Lewis and others have definitively shredded.

The goal is to find solid evidence that a proposed innovation actually leads to measurably improved outcomes, or to a material reduction in cost. Not that it could or should, but that it does.
Continue reading…

We Are Not A Dashboard: Contesting The Tyranny Of Metrics, Measurement, And Managerialism

May 13, 2019• 2

By DAVID SHAYWITZ

The dashboard is the potent symbol of our age. It offers the elegant visualization of data, and is intended to capture and represent the performance of a system, revealing at a glance current status, and pointing out potential emerging concerns. Dashboards are a prominent feature of most every “big data” project I can think of, offered by every vendor, and constructed to provide a powerful sense of control to the viewer. It seemed fitting that Novartis CEO Dr. Vas Narasimhan, a former McKinsey consultant, would build (then tweet enthusiastically about) “our new ‘control tower’” – essentially a multi-screen super dashboard – “to track, analyse and predict the status of all our clinical studies. 500+ active trials, 70+ countries, 80 000+ patients – transformative for how we develop medicines.” Dashboards are the physical manifestation of the ideology of big data, the idea that if you can measure it you can manage it.

I am increasingly concerned, however, that the ideology of big data has taken on a life of it’s own, assuming a sense of both inevitability and self-justification. From measurement in service of people, we increasingly seem to be measuring in service of data, setting up systems and organizations where constant measurement often appears to be an end in itself.

My worries, it turns out, are hardly original. I’ve been delighted to discover over the past year what feels like an underground movement of dissidents who question the direction we seem to be heading, and who’ve thoughtfully discussed many of the issues that I stumbled upon. (Special hat-tip to “The Accad & Koka Report” podcast, an independent and original voice in the healthcare podcast universe, for introducing me to several of these thinkers, including Jerry Muller and Gary Klein.)

AI Doesn’t Ask Why — But Physicians And Drug Developers Want To Know

Nov 16, 2018• 6

By DAVID SHAYWITZ MD

At long last, we seem to be on the threshold of departing the earliest phases of AI, defined by the always tedious “will AI replace doctors/drug developers/occupation X?” discussion, and are poised to enter the more considered conversation of “Where will AI be useful?” and “What are the key barriers to implementation?”

As I’ve watched this evolution in both drug discovery and medicine, I’ve come to appreciate that in addition to the many technical barriers often considered, there’s a critical conceptual barrier as well – the threat some AI-based approaches can pose to our “explanatory models” (a construct developed by physician-anthropologist Arthur Kleinman, and nicely explained by Dr. Namratha Kandula here): our need to ground so much of our thinking in models that mechanistically connect tangible observation and outcome. In contrast, AI relates often imperceptible observations to outcome in a fashion that’s unapologetically oblivious to mechanism, which challenges physicians and drug developers by explicitly severing utility from foundational scientific understanding.

It’s The Platform, Stupid: Capturing the Value of Data in Campaigns — and Healthcare

Oct 1, 2018

By David Shaywitz, MD

If you’ve yet not discovered Alexis Madrigal’s fascinating Atlantic article (#longread), describing “how a dream team of engineers from Facebook, Twitter, and Google built the software that drove Barack Obama’s re-election,” stop right now and read it.

In essence, a team of technologists developed for the Obama campaign a robust, in-house platform that integrated a range of capabilities that seamlessly connected analytics, outreach, recruitment, and fundraising. While difficult to construct, the platform ultimately delivered, enabling a degree of logistical support that Romney’s campaign reportedly was never able to achieve.

It’s an incredible story, and arguably one with significant implications for digital health.

(1) To Leverage The Power of Data, Interoperability Is Essential

Data are useful only to the extent you can access, analyze, and share them. It increasingly appears that the genius of the Obama campaign’s technology effort wasn’t just the specific data tools that permitted microtargeting of constituents, or evaluated voter solicitation messages, or enabled the cost-effective purchasing of advertising time. Rather, success flowed from the design attributes of the platform itself, a platform built around the need for inoperability, and guided by an integrated strategic vision.

A Health Tech’s Secret Weapon: The People Under The Hood

Feb 20, 2018• 3

By DAVID SHAYWITZ, MD

The recently-announced acquisition of the oncology data company Flatiron Health by Roche for $2.1B represents a robust validation of the much-discussed but infrequently-realized hypothesis that technology entrepreneurs who can turn health data into actionable insights can capture significant value for this accomplishment.

Four questions underlying this deal (a transaction first reported, as usual, by Chrissy Farr) are: (1) What is the Flatiron business model? (2) What makes Flatiron different from other health data companies? (3) Why did Roche pay so much for this asset? (4) What are the lessons other health tech companies might learn?

The Flatiron Business Model

To a first approximation, Flatiron has a model that can be seen as similar to tech platforms like Google and Facebook – delight (or at least offer a useful service to) front-end users, and then sell the data generated to other businesses. For Flatiron, the front-end users are oncologists (mostly community, some academic), and the data customers are pharma companies. In contrast to Google (and also in contrast to the less successful Practice Fusion, recently acquired at a loss), Flatiron doesn’t sell access to front-end users themselves (e.g. through targeted ads), but rather access to de-identified, aggregated clinical information.

Success of this model requires that the Flatiron platform is attractive to oncology practices, who must feel that they’re getting distinct value from it and believe that it helps them fulfill their primary mission of taking care of cancer patients. If this is true, then the Flatiron platform will enjoy continued traction from its current base, and may more easily win over new users (including practices that use a different EMR system, like Epic, but still want access to the Flatiron network and analytics).

Pharma’s (Big) Data Problem

Jan 23, 2018• 2

By DAVID SHAYWITZ, MD

C.P. Snow, author of “The Two Cultures”

Despite (some might say, because of) a raft of new biological methods, pharma R&D has struggled with its EROOM problem, the fact that the cost of successfully developing a new drug, including the cost of failures, has been relentlessly increasing, rather than decreasing, over time (EROOM is Moore spelled backwards, as in Moore’s Law, describing the rapid pace of technology improvement over time).

Given the impact of technology in so many other areas, the question many are now asking is whether technology could do its thing in pharma, and make drug development faster, cheaper, and better.

Many major pharmas believe the answer has to be yes, and have invested in some version of a by-now familiar data initiative aimed at aggregating and organizing internal data, supplementing this with available public data, and overlaying this with a set of analytical tools that will help the many data scientists these pharmas are urgently hiring to extract insights and accelerate research.

The Implementer’s Dilemma

Dec 12, 2017• 2

By DAVID SHAYWITZ, MD

One word: implementation.

Increasingly, I’m convinced that the underappreciated challenges of implementation describe the ever-expanding gap between the promise of emerging technologies (sensors, AI) and their comparatively limited use in clinical care and pharmaceutical research. (Updated disclosure: I am now a VC, associated with a pharma company; views expressed, as always, are my own.)

Technology Promises Disruption Of Healthcare…

Let’s start with some context. Healthcare, it is universally agreed, is “broken,” and in particular, many of the advances and conveniences we now take for granted in virtually every other domain remain largely aspirational goals, or occasionally pilot initiatives, in medicine.

Healthcare is viewed by many as an ossified enterprise desperately in need of some disruption. As emerging technologies shook up other industries originally viewed as too hide-bound to ever change, there was in many quarters a profound hope that advances like the smart phone or AI, and approaches like agile development and design thinking, could reinvent the way care is delivered, and more generally, help to reconceptualize the way each of us think about health and disease.

A Fail For Activity Trackers: The I Told You So’s vs Need More Datas

Sep 22, 2016• 3

By DAVID SHAYWITZ, MD

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Perhaps the normally measured physician-economist Aaron Carroll best captured the reaction and sentiments of the healthcare community in response to a recent JAMA article demonstrating that subjects in a weight reduction study using activity trackers lost significantly less weight than those in the control group:

“I TOLD YOU SO!!!!!!” (Emphasis in original.)

These results were cheered for several key reasons.

First, many in healthcare are irritated by the idea of simplistic technical fixes for complex medical (and social) (and cultural) (and economic) problems–like obesity.

Second, as Carroll has pointed out, exercise is healthy for many reasons, but weight loss is probably not one of them; changing your diet seems to matter a lot more.

However, it’s important to critically evaluate research even (especially) when it seems to produce an ego-syntonic conclusion–a conclusion with which we so strongly agree.

My initial reaction to the result was that perhaps it reflects an example of the concept of “moral licensing” that Malcolm Gladwell discusses so thoughtfully on his Revisionist History podcast–i.e., when you deliberately act morally in one context, you may be more likely to act less morally in another context, having already demonstrated to yourself your moral bona fides.