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.