Humility may not be the first word you associate with “genetics,” “precision medicine,” and “Harvard,” but it was unquestionably the theme of the day at a fascinating panel this week convened by the Harvard Data Science Initiative.

The discussion (video here), was remarkably grounded. It reflected hard-earned learnings from experts who have tried to implement data-driven, “precision” health solutions outside the academy, and had scars to show for it. 

Five themes stood out for me in this conversation:

• Appreciation for the successes of “precision medicine” – particularly in some cancers as well as in some monogenetic diseases;
• Recognition of the limitations of precision medicine – i.e genetics are often just not that determinative, and generally are not especially useful or impactful in the prevention and management of disease;
• Emphasis on the underappreciated implementation challenges associated with driving innovation into practice;
• An urgent plea for improved approaches for the identification and effective treatment of mental health conditions;
• A powerful sense that a key opportunity for improving health is the development of improved, ideally more precise, approaches to durable behavior change.

Because readers are likely familiar with my extensive discussion of precision medicine over the last 10+ years, I’m going to focus on the last three topics: implementation challenges, the mental health need, and behavior change opportunities.

Implementation Challenges

Tremendous advances in our scientific understanding and in the technologies we can deploy have invigorated researchers seeking to apply these advances to the study and treatment of human disease.  Yet the gap between theory and practice remains wide.

Foundation Medicine Chief Scientific Officer Priti Hegde described an early experience she had when she started out in the pharmaceutical industry at Genentech. 

”We had this vision,” she said. “We should collect data from every patient on our clinical trials, and that data will transform drug discovery.”

So what happened?

“You won’t believe it, but it took us over five years just to get systems in place where datasets can be put in place in a manner that allows us to analyze data; that we need to have cloud compute architecture, that we need to have data access policies. These things may be kind of boring for a scientist, but critically important for us to get to a place where we can actually start to analyze the data and make something out of it.”

(As I’ve written, the challenges Hegde saw have also been publicly discussed at Novartis – see here, here – and Lilly – see here.)

Hegde has taken this understanding to her current role. “At Foundation Medicine, I realized I can innovate ‘til the cows come home. But if the insurance providers don’t see value in what we’re doing, and if we can’t demonstrate value for patients, there is no point in innovating. So it always comes down to the practical, boring, mundane things that drive true innovation.”

(For more on the importance of implementation see here, here, here.)

Harvard’s Mark Namchuk, a pharma veteran, also spoke eloquently, and plaintively, about the challenges of moving from the “pristine biology” of the lab to the complexities of clinical trial design and execution. “There’s this practical world that you’re going to run into,” he explained, and urged students and trainees considering careers in translation to seek out the perspective of experienced clinical trialists.

Duke bioinformatician Jessie Tenenbaum, who is Chief Data Officer for the North Carolina Department of Health and Human Services, also highlighted the need for pragmatism.

“It’s the implementation,” Tenenbaum said. “It’s how do you build that into a workflow that’s extremely entrenched with clinicians and providers and genetic counselors and everyone doing what they do?  Make sure to get it in front of them in a way that’s going to be digestible in the busy day that they have.”

Tenenbaum also emphasized actionability – “what do we do” with the information, which requires a consideration of context. 

Tenenbaum shared an example from earlier in her career, when the genetics of warfarin metabolism were elicited. “When I was in grad school and learning about pharmacogenomics,” she said, warfarin was “the poster child for success” as researchers “figured out the genes and figured out the clinical guidelines based on the genes.” 

Her big revelation came afterwards, however, when she was at Duke. It arrived courtesy of cardiologist Rob Califf (who President Biden recently nominated to serve, for the second time, as Commissioner of the FDA).

As Tenenbaum tells it, Califf “made me get up at some ungodly hour, to round with him, to see the patients and how this [warfarin management] really worked. There was someone who was about to be prescribing warfarin, and Dr. Califf asked the doctor “did you think of doing the genetic test?” 

No, the doctor replied.

The patient “was an elderly person, had many other complicating factors…genetics just didn’t even play a role,” Tenenbaum recalled. 

(Indeed, most clinicians have found that dosing warfarin empirically, following the traditional “go low, go slow” advice, works well — especially since at least half the variation in dosing seems to depend upon factors besides the two genes implicated in warfarin metabolism.)

The point: it’s great to appreciate the ideal and abstract elegance of precision medicine, but to deploy it effectively, you also need to grapple with the messy reality of clinical care. 

The Mental Health Crisis

One particularly concerning reality of contemporary clinical care is the expanding mental health crisis sweeping the country in the wake of the pandemic.

The impact on children, in particular, was emphasized poignantly by Zak Kohane, a pediatric endocrinologist by training, who now chairs Harvard’s Department of Biomedical Informatics. 

“There is an unbelievable disaster happening in pediatric mental health right now,” Kohane explains.  “Our local hospital [i.e. Boston Children’s Hospital] is not accepting elective admissions — not because of COVID, not because of trauma, but because our psych wards are overflowing. And this is, by the way, not just us. It’s nationwide.”

Tenenbaum shares Kohane’s sense of urgency. Her research has focused on mental health, and she’s edited a forthcoming book, Mental Health Informatics. 

She points to several challenges in applying technology to mental health. 

First, she notes that “as opposed to cardiology, where we kind of understand how the heart works and how to quantify that, there’s just different epistemology of understanding what is knowledge.” 

She adds, “There are different concepts. They’re all self-reported and subjective, as opposed to something we can quantify. So there’s a huge number of challenges in that space.”

(There are some fledgling academic and industrial efforts to bring more quantitative data analysis to neuroscience R&D; see TR coverage of Neumora Therapeutics, Oct. 2021)

Tenenbaum also highlighted the profound need, in her current role in public health in North Carolina, to address mental health. It’s hard to make strides when essential data infrastructure is missing, she said.

When she started the role, she explains:

“We think about the sexy predictive analytics, machine learning, what can we do? And you get in and realize, ‘Oh my gosh, they’re using faxes, that’s where we’re at that.’ So data IT infrastructure, governance, quality automation — those are the things we need to get into place first, and then ask me next year about what we’ve managed to do with mental health and public health.” 

Tenenbaum hastens to add that many of the challenges in public health stem from efforts that “have been drastically underfunded for decades,” and notes that much of the fax reporting has now been automated.

John Quackenbush, a computational biologist at Harvard, also emphasized the unmet need represented by mental health, and expressed hope for the opportunities represented by “digital phenotyping,” a topic under investigation by the Onnela lab at the Harvard School of Public Health. 

The Onnela lab, Quackenbush said, was initially interested in the promise of wearables for neuropsychiatry, but more recently has focused on leveraging the passive data collected by smartphones. (I am somewhat cautious about this – multiple mental health startups were founded based on this exact principle, yet to the best of my knowledge, most ultimately pivoted away from it.)

Precision Behavior Change?

There was a strong emphasis from the panel on the urgent need to motivate healthier behaviors – precision behavior change, perhaps – but very little sense of how this can be done effectively, especially at scale.

All the panelists pointed out that for most people, you don’t need fancy assays to tell you how to live a healthy life. As Tenenbaum, put it, “I can tell you based on your genome what you should do. Eat right and exercise.”

Harvard’s Quackenbush tells me he offers people similar actionable advice based on their genomes: “Eat well, exercise, maintain a healthy weight, and don’t smoke.”

Obviously, we don’t need a genome sequence to tell us this.

Likewise, Namchuk emphasizes the lack of utility of microbiome analysis. He explains, “People are fascinated with the microbiome. I find the microbiome both fascinating and unbelievably frustrating because I have no idea what to do interventionally with the microbiome.”

Adds Tenenbaum, “So much of it comes down to eat right and exercise. And people can know that. And that doesn’t mean they’re going to do those things…we know in America there’s an obesity epidemic and we all know what we’re supposed to do, and it didn’t prevent myself and others I know from putting on that weight during COVID.”

The challenge, Tenenbaum says, is “figuring out the precision way to get people motivated to do it. I don’t know if we’ve cracked that nut yet.”

As Harvard’s Kohane summarizes:

“The sort of behavioral characteristics that we’re trying to modify turn out to be very hard. We don’t do it. Facebook knows a lot more than most of us know about how to modify behavior [see my discussion of ad-tech for health here], but the healthcare system doesn’t know how to modify behavior. Health insurance companies don’t know how to modify behavior.  They’ve tried all sorts of incentive programs for cigarette smoking, weight loss, and we just don’t know how to do that, and we’ve failed at scale.”

He adds, provocatively: “Do we have enough leverage to do behavior modification … or should we just say, you know, humans are weak-willed individuals, and if we really want to combat the biggest epidemic in our lifetime, namely obesity, we’re going to have to go pharmacologic?”

The takeaway: for most people, the key to improved health isn’t a diet or exercise program based on specious conclusions from sequencing your genome, analyzing your microbiome, or surveilling your smartphone. Rather, it’s figuring out how to motivate people to adopt and stick with basic healthy living behaviors. 

It’s really about consistent coaching, guiding people to lead healthy lives – in a customized fashion that maximizes the chances of success.

Companies like Omada Health, Virta Health, Noom, and Precision Nutrition, among others, are aspiring to do variations on this theme. (Disclosure: I’ve recently reviewed course materials for Precision Nutrition.) The challenge, from a business perspective, is finding a way to combine the power and value of one-on-one interactions with the technology that offers the potential for additional insight as well as the promise of solutions at scale. 

Hence health’s multi-billion dollar question: can technology truly enhance the power of health coaches, enabling health coaching to scale while providing customized insights and improving adherence? Or will the efficacy of health coaching inevitably be reduced and diluted in the insatiable drive to achieve exponential growth?

Our future health may depend on how this question is resolved.

“It’s been a busy few weeks for product announcements in the world of fitness,” industry observer Anthony Vennare recently commented, citing the latest offerings from the Peloton, Mirror, Tempo, and other digital fitness platforms.

On the one hand, these developments are encouraging, offering customers pursuing fitness — and who are willing and able to afford the steep premiums — a greater range of options.

At the same time, when you look at these products – and the slick ads that often accompany them – it’s pretty clear that the target market is the already fit, or at least the fit-adjacent. While certainly some previously sedentary people have been inspired to start riding a Peloton or rowing a Hydrow, the inactive do not appear to be the main customers these digital fitness companies are pursuing.

Which, from a health perspective, is really too bad.

Here’s why.

Minimal Exercise Is Unreasonably Healthy

There are plenty of data arguing that exercise is good for you, and a huge amount of ink that’s been spilt trying to define the ideal weekly threshold.  But if you are really looking for the most bang from your buck – the most benefit for the smallest change in amount of weekly exercise – the data seem remarkably clear. 

Going from inactive to exercising a little bit each week seems to result in a disproportionate boost in your health, including specifically years of added life. The amount you benefit if you make this relatively small change in weekly exercise is roughly equal to the benefit you can expect to receive if you then go from exercising just a little to exercising intensively, all the time.

As Andrew Yang might say, it’s just math – and in particular, the shape of the benefit curves. (See Figure 1 of this 2012 paper by Steven Moore and colleagues at the National Cancer Institute, and Figures 2 and 3 from this 2017 review by Warburton and Bredin of the University of British Columbia.)

Earlier this year, I discussed what seemed like an important opportunity for digital fitness companies to target these customers – a need for what investment banking analyst Aarti Kapoor called the “Planet Fitness of the Digital World.” 

For Kapoor, this opportunity reflected a market gap revealed by her economic analysis, but if you layer in the disproportionate health benefit from serving this population, the need for such an offering seems that much more compelling.

Motivating Small Activity Is A Big Challenge

So why haven’t more digital fitness companies seemed to have targeted this demographic?

One reason: it’s easier to persuade someone who’s already active to try out a different product than it is to convince an inactive person to get off the couch in the first place.

A second reason might be the intangibility of the benefits: a previously sedentary individual who now exercises a little every week might be significantly better off from a health perspective, but might not feel all that different, or look all the different, and might regard themselves as still fundamentally deconditioned.

A related challenge is the Peltzman Effect – the tendency to compensate for a reduced risk by feeling more comfortable engaging in riskier activities. You can think of it like permissioning; for some, wearing a seatbelt justifies driving faster; a skateboard helmet makes it easier to contemplate more dangerous tricks. In the area of health and fitness, I imagine this tendency might be renamed the “Diet Coke Effect,” since making one healthy choice (like opting for a calorie-free beverage) often seems to justify indulgences (say a package of Yodels) that can more than offset the potential benefits of the first decision. 

While this challenge impacts all of us – it’s easier to justify ice cream after an intense workout – it’s easy to imagine that any potential health benefit of slightly increased exercise might be lost if the activity made it easier to justify additional caloric consumption.

There are also a range of what might be termed structural obstacles, as Dr. Michael Joyner of the Mayo Clinic pointed out to me, ranging from challenges in the built environment (e.g. “neighborhoods that are either not safe or not walking friendly”) to economic factors to limited opportunity for active transportation.

No one ever said it would be easy.

At the same time, it’s difficult not to be impressed by the engagement of emerging digital fitness technologies; Peloton is arguably a leading example of this, as I’ve discussed –also here).

Who Will Figure It Out?

So, first question: where will the digital health company serving the inactive come from? Will it emerge from a sophisticated digital fitness player like Peloton developing a new offering that targets a different demographic (with all the attendant risks to the brand), or from a company like Planet Fitness, developing digital offerings that deeply engage and encourage the currently sedentary?

Alternatively, might it come from a wellness and mental health brand like Noom, Headspace Health, or Happify Health, seeking to extend their program with a fitness hardware component? Or might the winning entry be something yet to be created?

Second question: if a company does develop a compelling offering, will the benefits delivered be robust enough, and achieved rapidly enough, to meaningfully reduce health costs – what I often think of as the Al Lewis test. 

This deceptively simple goal represents a remarkably difficult challenge, as I’ve recently discussed, but one that, if met, would obviously be of exceptional interest to self-insured employers, as well as payors seeking to promote disease prevention and encourage health maintenance.

We may get some early visibility into the first question from Aarti Kapoor herself, who has recently left her investment banking job to become CEO of a SPAC — “VMG Consumer Acquisition Corp”— that went public just this week. The stated intention of this blank check company is ”to identify and complete a business transaction with a company in the high-growth consumer and retail industry.”

Given Kapoor’s previous interest in a digital fitness offerings focused on the less active, it’s exciting to contemplate the possibility that she might identify a promising high-growth company in this category.

Whether or not Kapoor invests here, the space remains compelling from both a business and health perspective. It represents an important opportunity for aspiring healthtech entrepreneurs.

The best concise summary I’ve seen of tech industry travails in healthcare comes from Blake Dodge, a reporter at Business Insider. Dodge had just written a piece about Apple Health, and took to LinkedIn to share an additional quote that didn’t quite make the final copy. 

“I think they came to it pure of heart, really thinking that they could design something that would be ultimately logical,” a source at Apple told Dodge. “But the problem is, is that the actual way that healthcare is delivered in the US is not a one size fits all experience. It’s actually incredibly heterogeneous.”

Bingo. 

What’s more, this heterogeneity is expressed on every level, from the services and care provided to the way health information is managed – as Google engineer Adam Connors recently discovered.

An unexpected illness brought Connors face-to-face with the healthcare system, an experience that shocked him as a patient and troubled him as an engineer who thinks about how to improve systems.  Writing at CNN, Connors described poignantly the very personal consequences of a system unable to consistently access coherent and complete data, deficiencies that delayed diagnosis and complicated treatment. Being sick was stressful enough; dealing with a dysfunctional healthcare system made it even more frustrating.

Connors wondered how much waste and confusion could have been averted if his healthcare providers had a longitudinal health record, which would include all of an individual’s health information, independent of where care had been received. (I couldn’t agree more, and at a recent seminar at Duke, I pointed to the need for a comprehensive account of our health journeys as the single most important requirement for data-driven medicine of the future.)

It’s easy to feel discouraged about information sharing. For those working in healthcare, the obstacles are all-too familiar.

Consider an example that emerged from a panel discussion I recently moderated on digital health and diabetes co-hosted by the Digital Medicine Society (DiME) and the Veterans Administration (video here).

To facilitate the care of diabetic populations, a startup called Glooko developed a platform to aggregate a range of diabetes-related information. Glooko’s hope is to integrate data in a way that enables healthcare providers remotely monitor the health of their diabetic patients based on the data the patients regularly collect. Dr. Varman Samuel, an endocrinologist at Yale University and the VA, highlighted two conspicuous compatibility problems for Glooko: data from Medtronic insulin pumps and from Abbott’s continuous glucose monitoring system. Moreover, it seems that Glooko used to have access to data from Abbott devices, until it was “unilaterally blocked” by Abbott, according to this Medscape report. 

An Abbott scientist on the panel defended the company, saying it collaborates with a number of manufacturers. Yet, as one of the panelists suggested, it appears Abbott has opted for a closed system, developing a data platform, LibreView, that’s analogous in intent to Glooko, yet which seems only to work with Abbott devices. It’s a familiar “walled garden” strategy utilized by a number of medical device manufacturers (for example, those who make ICU equipment), intended to motivate purchasers to remain loyal to the brand. Fellow residents of Apple’s walled garden will recognize the approach (as the Wall Street Journal’s Joanna Stern discusses here).

Harvard bioinformatics professor Dr. Zak Kohane has seen many such examples, where well-intended data sharing schemes collide with more parochial interests.

Kohane recently offered an especially thoughtful perspective on the challenges in healthcare data in a just-released podcast hosted by Apple AI expert Adriel Saporta and Harvard AI researcher Parnav Rajpurkar.  

The whole podcast is required listening, including an excellent, concise overview at the top of the show by the hosts of the history of health data legislation, moving briskly from HIPAA to HITECH to MACRA to 21^st Century Cures. 

In the subsequent discussion with Kohane, seven themes stood out.

1. Flying blind

According to Kohane, despite decades of lamentation, “we have an uninstrumented healthcare system” meaning that “things happen in our healthcare system and we don’t even count them.”  This matters, he says, because “you can’t have accountable care if you can’t count.” 

2. Data access

“The beginning of making our data work for us,” Kohane says, is to have our data “in a systematic, computable, form, or at least to have it,” so that various statistical methods can potentially “transform it into something computable” which can be used to generate “insights at a population level, and actions on the individual, clinical level.” In Kohane’s mind, while having computable data is helpful, he would absolutely prioritize gaining access to data in the first place, whether these data are immediately computable or not.”

3. Data silos and human behavior

Despite years of handwringing and public opprobrium, data silos remain a fixture of the healthcare landscape, as Google engineer Adam Conners’s experience painfully captures. The fundamental issue, Kohane suggests, isn’t technology, it’s people. 

“Lack of sharing,” Kohane says, “is core to the human condition,” and is exhibited by stakeholders throughout the system, from companies to healthcare systems to academic researchers to patient advocacy groups (yes, you heard that right). Kohane cites an example of “three different breast cancer groups not sharing data with one another,” an observation that would surprise no one familiar with the space. (I’ve discussed challenges of data sharing, including contrast between stated and revealed preferences, here.)

4. Challenge of incentives

From the perspective of patients, the benefits of improved health data sharing are clear. Kohane shared the published example from 2009 showing that fairly coarse hospital discharge data could be used to detect domestic abuse in emergency room patients. Yet according to Koahne, “twelve years after we published that study, no one is doing it.” It’s “a huge health issue,” Kohane points out, but “is not an obvious income generating thing.” 

Kohane also highlights the challenges faced by hospital CEOs, who might need to balance the desire of a physician to share data with what can feel like asymmetric downside risk, such as a data leak, that puts the executive on “the front page” of the paper. In other words, the risks associated with sharing data tend to feel larger (and certainly more palpable) to decision-makers than the consequences of not sharing. 

Thus, while data sharing may be something that clinicians, researchers, and hospitals should do, actually persuading people to do things they don’t perceived as serving their self-interest can be a challenge. As Upton Sinclair famously said, “It is difficult to get a man to understand something when his salary depends on his not understanding it.” 

Incentives impacting the adoption of emerging technologies have also been a preoccupation of thoughtful healthcare leaders including Stanford’s Dr. Kevin Schulman, who has emphasized the need for “innovative business models,” data that are “actionable by patients,” and an economic model that leverages data but focuses on service.

5. Multiple paths forward

Kohane discussed several potential paths forward, advocating the pursuit of all of them simultaneously. 

For starters, Kohane leads several  data sharing initiatives, such as the i2b2 consortium, and the 4CE consortium to evaluate COVID. By demonstrating the clinical and scientific value of data sharing, and learning what the sticking points are, the hope is to pave the way for broader changes. Kohane also highlights the role of regulation in compelling institutions to share data more substantively — as well as the need for institutions to understand and effectively manage the many regulations that seem to inhibit data sharing. 

But ultimately, Kohane seems to have the greatest faith in what he describes as an “end run” – consumerism.

“Our data may or may not be made to work for us by regulators, by medical leadership, and by other august leaders,” he says. “But the consumerist wave, which will continue to flow over healthcare” may drive progress. He cites the example of the ability to download information from an increasing number of hospitals to Apple Health using standard APIs (programming interfaces) as an indication of where the future may be headed. “In the long term, I think it’s all going to be in the consumer play because it’s where you have the scale and where you have alignment of interest.”

6. No database to rule them all

Asked whether he’d like to see a single, consolidated database with all healthcare information, Kohane said he much prefers a federated approach, meaning that data live locally, and can be selectively shared using agreed-upon standards. As Kohane and Dr. Ken Mandl wrote in 2016, a distributed model enables “each institution’s data” to be “maintained separately,” which “afford local control over data.”

Why the federated approach? As Kohane explained on the recent podcast, “Medicine itself is so heterogeneous… it’s a very labor intensive, still, artisanal practice in many, many ways that if you just shove things up into a shared database, you’re actually missing a lot of the differences in practice.” 

In essence, co-host Saporta summarized, “without having the context of where the data is coming from, you’re losing a lot of very important information.”

Instead, Kohane advocates the creation of “purpose-built” federated databases. He emphasizes that “no interpretation of clinical data … is universal for all purposes.” It’s critical to understand the context in which data are generated, he says, echoing a point that both Dr. Amy Abernethy and I have repeatedly made. Context matters.  

As Kohane explains, “It’s just like science: a good biologist who refuses to understand the system in which they measure the proteins is as unlikely to come up with an interesting and robust finding as a machine learning expert who doesn’t want to get down and deep into how the data was generated.”

7. Trust

The notion of sharing health data faces increasingly strong headwinds, as discussions of tech company’s questionable use of data and penchant for surveillance has increasingly attracted national attention. The opportunity, says Kohane, is “to leverage the intuition that many patients have, which is they trust their doctor and they trust their healthcare system more than they trust some anonymous large data company.” He suggests we should “focus on the sociology of where trust lies and maybe try to widen it to different spaces.” 

The importance of the trusted relationship between provider and patient, he noted, was evident in his experience assisting his elderly mother, who had struggled with her fluid balance and was hospitalized several times for diuresis. As Kohane had originally described to NPR, he had managed to help his mother stay out of the hospital simply by monitoring her daily weight, and, critically, engaging with her effectively when the numbers were off. 

The key, Kohane emphasizes, wasn’t the sophistication of his (incredibly basic) treatment algorithm, but rather, his relationship with the patient. He notes that while previous peer-reviewed studies of his approach had generally not been effective, he was able to succeed, he believes, because of the robust and daily dialog.

Bottom line: 

Healthcare is still struggling to effectively utilize technologies that have transformed other industries. It seems clear by now that the most significant hurdle isn’t technology, but people and human behavior. At the same time, the greatest hope for progress in health also lies with people. 

The challenge for technology is to leverage the best of medicine – including the often-powerful therapeutic relationship between caregiver and patient – while remaining cognizant of the very real anxieties. Most patients don’t want to be monitored all the time, or to continuously think about their health; hospitals want to provide excellent care but not reveal information that places them at a competitive disadvantage; researchers want to contribute to science but also manage their careers; companies have shareholders to consider. 

Emerging digital and data technologies continue to often exceptional promise for healthcare, but delivering on this potential remains elusive. Success will depend less on the elegance of a particular technology, and more on the emotional and organizational intelligence of the health technology team attempting to wield it.

Success will depend less on the elegance of a particular technology, and more on the emotional and organizational intelligence of the health technology team attempting to wield it.