David Shaywitz

Medicine is plagued by a feedback gap, or perhaps more accurately, a feedback paradox. 

On the one hand, clinicians are bombarded by feedback. Every day, there are a slew of process and billing metrics to review, providing an accounting of the volume of patients seen, and the intensity of each visit. 

How thorough was the exam? What procedures may have been performed? 

Even beyond the measures related to billing are an ever-growing number of measures related to guideline adherence. Doctors have an increasingly long set of questions to answer, boxes to check.

Did you ask about seatbelts use? Did you ask about home safety? Have you scheduled an eye exam for your patient with diabetes? These are often, individually, sensible and relevant considerations. In aggregate, however, they can be overwhelming and lead to guidance fatigue and ethical slippage, as Bill Gardner (here) and Drs. David Blumenthal and J. Michael McGinnis (here) have discussed, and as I recently reviewed in the context of COVID-19.

Yet for all these metrics and assessments, what’s often lacking is any sense of how the patients are actually faring – the thing that matters most. 

It’s not at all clear whether all the carefully documented and billed work is leading to improved outcomes for patients. Are there approaches for some patients that are leading to better outcomes than other approaches, and which could be generalized?  Are there other approaches that should be abandoned?

The heart of the problem is how most clinical research is done. On the one hand, to demonstrate, scientifically, that a particular approach (whether a novel drug or a new approach to therapy, like placing sick COVID-19 patients on their stomachs instead of their backs when they’re in the hospital) works, a clinical trial – ideally, a randomized, double-blind, placebo-controlled, to the extent possible – is performed. This is a highly choreographed exercise, where the exact characteristics of the subjects to be enrolled, the evaluations to be performed, and the criteria for success are all spelled out, clearly, in advance (or at least, ought to be). Enrolled subjects are tracked meticulously, and ultimately, you should be able to determine if the intervention was more effective than your control at achieving the pre-specified outcomes. 

This is the way medical science advances – through rigorous clinical studies, and then the adoption of these results in clinical practice.

The conceit of this approach is that the results from a rigorous clinical study will be widely generalizable, meaning that the patient you’re treating will respond like the subjects in the study, and the treatment you’re providing will mirror that offered in the study.  

Of course, we all know side effects can emerge over time that weren’t seen in clinical trials, and we recognize that certain groups – women and minorities in particular – have historically been underrepresented in clinical trial populations.  

To the extent that you accept the basic premise that a well-run clinical trial yields results that can guide care, then it makes sense for health systems to impose processes that try to ensure patients with a particular condition receive the treatment indicated by clinical trials.  

This approach has given rise to the idea of “disease pathways,” that provide a   template for managing patients with a particular condition, and the embrace of  process metrics, that try to ensure doctors are following the updated guidance – rather than, say, doing what perhaps they’ve always done because it’s what they know.

In practice, there are two problems with this system: one stemming from doctors who disregard these pathways, the other related to doctors who adhere to them.

Many physicians (perhaps especially in august academic institutions) worry most about their colleagues, especially those who practice in the community. 

These physicians are often presumed by their university colleagues to be less attuned to the latest scientific literature, and hence neither of relevant advances in care nor practicing in a setting that encourages them to do so. Consequently, their patients may not be receiving the most optimal care, and have no way of knowing it.

But a second category of challenge involves the physicians who are attuned to guidelines, perhaps because they closely follow the literature, or perhaps because their health system nudges them to adhere. While arguably the patients in the care of these physicians are on balance better off than those in the first group, there are still important challenges – and embedded opportunities.

For starters, subjects in clinical studies are notoriously unrepresentative of the more general population – enrolled subjects tend to be healthier and to have fewer co-existing conditions, in effort to enable a clearer evaluation of the intervention, and provide a sort of “best case” read-out.

In addition, subjects in trials tend to be followed unusually closely – they may be reminded to take their medicine, for example, and encouraged along the way by study staff.  While inclusion of a placebo group controls for the therapeutic impact of the extra attention (known as the “Hawthorne Effect”), it doesn’t account for the combined effect of experimental intervention plus attention – the increased likelihood that a subject in a study will reliably take a given medicine, say, compared to a patient in a doctor’s office, where the medication adherence rate can be astonishingly low. (It’s also why tech-enabled health service companies that feature a strong coaching and tracking component, like Omada and Virta, have gained considerable traction.)

As UCSF’s Dr. Fred Kleinsinger noted in a 2018 publication:

“Medication nonadherence for patients with chronic diseases is extremely common, affecting as many as 40% to 50% of patients who are prescribed medications for management of chronic conditions such as diabetes or hypertension. This nonadherence to prescribed treatment is thought to cause at least 100,000 preventable deaths and $100 billion in preventable medical costs per year.”

Finally, some patients in clinical practice may actually respond better than expected to a particular intervention – perhaps because of an intrinsic characteristic (involving their genetics or their microbiome, for example), or perhaps because of something subtle a particular physician or care provider did, a tweak that might goose the patient along a little bit. These are opportunities to learn from “happy accidents,” to institutionalize serendipity, so to speak, and benefit from the ingenuity of inquisitive physicians (medicine’s lead users, to use von Hippel’s term) –opportunities that are easily missed in the course of routine care.

The common feature of all these clinical care scenarios is the almost complete absence of tracking the one thing that matters most – actual patient outcomes over the long term.  

• How is the psychiatrist doing with her patients with depression? 
• How about the endocrinologist with his diabetics (in fairness, A1c is occasionally tracked)?
• Is the neurologist getting average, worse, or better, results than expected for patients with multiple sclerosis? For which patients?

A new medicine may get approved on the basis of robust clinical trials, but the approval of a drug, as I’ve argued, doesn’t necessarily equate with the improvement of outcomes of real world patients.

Why aren’t outcomes tracked in routine care the way they are in clinical trials? For one, it’s surprisingly hard to track patient journeys, especially as patients often get care from multiple medical systems – though even rigorously determining the outcome for a patient entirely within one medical system can be remarkably difficult, especially in the context of real life, when appointments are missed, and assessments can be spotty and inconsistent, and documentation unreliable.

But the second reason the information isn’t tracked is, essentially, no one (besides the patient!) really cares, in the sense of being personally invested in (and accountable for) the outcome. Certainly not in fee-for-service systems, where doctors (who, to be clear, obviously try their best for each patient) charge based on their activity rather than patient outcome. Hence, the extended push for approaches that seek to prioritize improved care, and specifically, improved outcomes. But even here it’s challenging – doctors will always argue that their specific patients are different – more complex or sicker, say, so of course they’ll do worse. 

Often, the doctors may be right, and efforts to increase transparency around the success of certain surgical procedures has naturally led to notorious gaming of the system, where a mediocre heart surgeon who selects only the easiest cases scores higher than an exceptional surgeon who takes on the most difficult ones. 

Attempts to adjust for complexity, inevitably, go only so far.

What emerges from this is a health system that, despite what may be the best intentions of providers, essentially, is flying blind, and lacks the basic ability to see what’s it doing, a system that lacks the fundamental ability to iteratively optimize and improve. 

Yes, care improvement occurs – but over incredibly long periods of time, driven by the slow pace of robust clinical trials, rather than by the opportunities to systematically learn from patients and providers every single day. 

For years, we’ve championed the concept of a “learning health system,” and today, it remains largely an unrealized aspiration.

What is to be done?

Mark McClellan, professor, Duke University

A remarkable recent two-day conference organized by Dr. Mark McClellan and colleagues at the Duke-Margolis Center (all materials available here) dug into exactly this challenge, drilling into the question of whether there can be some kind of convergence between the process of collecting and analyzing data for clinical trials and doing the same for clinical care; data collected outside of traditional clinical trial is often called “real world data,” or RWD. 

While it would be a disservice to condense such a rich conference into tidy conclusions, there were nevertheless several powerful lessons that I took away.

For one, I was reminded yet again of the rigor and meticulousness of clinical trials. I knew this, of course, having designed, written and executed a number of studies, and having been immersed for years in research environments focused on this process. Even so, it was instructive to go through the challenges of defining who, exactly, has a particular condition – what is the definition of a “case?”  What is the definition of an “outcome?” In the context of clinical trials, the criteria tend to be exceptional explicit, and evaluation of results can often require a deliberate, pre-defined process of adjudication.

The challenge, many speakers emphasized, is that doctors, busy taking care of patients – perhaps seeing patients every 15 minutes or 20 minutes (not uncommon, and I’ve heard of less) – barely have enough time to perform the minimum services (and documentation) required to get paid. 

The detailed evaluation typically required in clinical trials feels like a luxury few busy physicians have as they fly through their day. Many speakers highlighted the importance of not contributing to provider burden, and several noted the increasing rate of doctor burnout, which some have attributed to the “death by a thousand clicks” nature of existing interactions with the electronic health record system.

Another huge challenge is that, while clinical trial data is typically captured in a dedicated database explicitly built with the desired analysis in mind, patient care data is often scattered across a healthcare system, or multiple healthcare systems, where it can be difficult to even know what other relevant data exist and might be germane. 

For at least one speaker, UCSF breast surgeon and clinical trial innovator Dr. Laura Esserman, the right solution would involve the radical re-engineering of care delivery — “a sea-change in how clinicians practice,” so that clinical-trial quality data are routinely captured. By doing a much better job collecting data, and by improving how we gather and share data, she argues, we can ultimately both save time — by entering data once, and using it many times — and improve care. 

Laura Esserman, professor, surgery; UCSF

As Esserman points out (and I couldn’t agree with this point more), “Imagine a business where you have no idea what your outcomes are, no idea what your metrics are. We must be in the business of quality improvement.”

For many other speakers, the priority was searching for ways to improve the system while not requiring extensive changes in the way doctors practice. For all the discussion of stakeholder alignment, the intrinsic tension between providers and researchers was palpable, and acknowledged by a number of the presenters.

In one of the day’s best talks, Chhaya Shadra of Verana Health, a startup focused on real world data in several specialties including ophthalmology, neurology, and urology, argued “If you have to take time to improve documentation to help researchers, it’s not fair to clinicians whose primary responsibility is patient care.”

Similar sentiments were expressed by UCSF’s unfailingly insightful and articulate health IT policy researcher Julia Adler-Milstein, in a recent Annals of Internal Medicine commentary on a paper revealing that physicians spend around 16 minutes per patient engaging with the EHR system, about a third of the time on chart review (i.e. trying to find information), another quarter of the time on documentation (adding their own notes), and another 17% of the time on order entry – all told, a remarkable amount of hunting and pecking.   

The dilemma we face, Adler-Milstein observes, is:

“How do we generate the foundation of clinical data needed to support the EHR’s many high value uses (including but not limited to clinical care) while doing so efficiently (for example, improving user interface design, using digital scribes, and simplifying documentations)?  Even with the most efficient approach, physicians (and many other types of clinicians) will never obtain a direct return from future use of their documentation equal to their time cost of documentation.  At a minimum, acknowledging this mismatch and making physicians feel valued for the time they spend in the EHR is needed.  (emphasis added)

Many of the mitigation approaches Adler-Milstein described were also highlighted by speakers at the Duke conference: for instance, the founder of Google Glass company Augmedix described the company’s focus on serving as a digital scribe; other presenters emphasized the need for improved EHR user interface, and more generally, for more human factors research and design.

Julia Adler-Milstein, professor of medicine, UCSF

A number of presenters also emphasized the importance of understanding the questions you are trying to ask, and pointing out that for a number of applications – some population trends, for example – you may not need perfect data, and may be able to extract real value from what you have. But, even there, you still need to have a sense of both the quality of the underlying data and the nature of the existing imperfections, so that you don’t subsequently use the data inappropriately, and draw misguided conclusions.

Dr. Paul Friedman, chair of cardiology at the Mayo Clinic, offered examples that highlighted both the possibility and the limitations of using existing data. On the bright side, he highlighted a project demonstrating the use of artificial intelligence to deduce, with remarkable accuracy, a patient’s ejection fraction (a measure of heart output typically determined using cardiac echocardiography) from routinely collected electrocardiograms (ECGs) archived by the Mayo. 

On the other hand, Friedman described the frustration of trying to predict COVID-19 infections (which may impact heart cells) using the sort of detection technology commonly available on smartphones and some wearables. In this case, he says, a key barrier turned out to be the lack of underlying standardization among these tools. 

While all may report out something looking like a “standard” ECG, their approaches to deriving this are so different that you really struggle to develop an algorithm from these disparate data. Alignment around some set of standards involving sampling rate and dynamic range would help, he suggested.

Speakers evinced particular interest in the ability to stitch together multiple data types (say EHR data with claims data and specialty clinical data) using privacy-preserving technology like that offered by Datavant, a Bay Area startup cited by several presenters. This approach obviously won’t solve issues related to underlying quality of data that’s being linked, but it can help not only develop the rich data set representing a patient’s longitudinal journey, but by bringing together a range of sources, the technology may help surface — and in some cases, resolve — data entry errors and other ambiguities.

The opportunities – and the risks, especially around privacy – of collecting and including more data from wearables and other “consumer” products in a patient’s health record were also highlighted by several speakers. This 10-minute talk by Elektra’s Andy Corovos may be among the best overviews I’ve seen on this topic — a topic that for years has been especially near and dear to my heart. 

As Veradigm’s Stephanie Reisinger noted in (another) compelling presentation, the consumerization of healthcare, including the increased use of devices and apps, represents a major healthcare trend, “empowering us to see and share health data,” and “driving informed consumers to demand a greater say over health journeys. Human bodies are becoming big data platforms.”

Of course, this possibility also relates directly to the sorts of security concerns Coravos and others highlighted.

What’s also clear is that pharma is looking at these large integrated datasets in different ways than they once did. Initially, pharma companies turned to real world evidence primarily in the context of health economics – typically “health technology assessments.” Primarily, pharmas were seeking evidence of real-world performance, to facilitate engagement (and negotiations) with payors (and, ex-US, regulators who require a discussion of relative value). 

Today, Veradigm’s Reisinger says, the most common questions from pharma partners involve protocol optimization (understanding the impact of various inclusions and exclusion criteria and the number of patients you might be able to recruit, for example), patient finding (recruitment – an effort to connect eligible patients with suitable trials) and the development of synthetic control arms for some studies (for situations when a suitable control arm may not be feasible or ethically appropriate but a relevant basis of comparison is required, for example).

Finally, several speakers, including FDA’s Dr. Amy Abernethy, highlighted the potential value of real world data in understanding and developing effective therapeutics for COVID-19; this is the focus of the COVID-19 Evidence Accelerator, for example. The recent Surgisphere scandal involving COVID-19 data (I discuss this in detail here) was cited by several speakers including Abernethy, who highlights the need for “ruthless transparency.”

I left the conference feeling neither entirely elated nor thoroughly despondent – though perhaps with a healthy mixture of the two emotions. As Abernethy observed, “doing science is messy, and we’re doing science together as a community.” 

There are real opportunities here, as well as intrinsically difficult hurdles – many presenters pointed out that the social/political/”psychological” (as one speaker said) hurdles are far greater than the technology hurdles. That said, it’s also quite possible that improved technology could catalyze advances that might highlight the potential value here and motivate further collaboration. 

Another hope would be that an innovative health delivery system, perhaps reimagined along the lines that I’ve described here, and that Esserman has championed, emerges somewhere and proves so compelling to patients that it becomes the immediate gold standard, and is widely adopted. 

A more immediate step might be the voluntary adoption of some of the technical standards for devices that Friedman describes.

In short, I came away impressed by the urgent need to improve how our healthcare system captures and shares data, and thus learns – or, presently, fails to learn – from patient experience. I also appreciate, along a range of dimensions, just why this seemingly urgent and obvious problem has remained such a stubbornly difficult nut to crack.  

Above all, I’m struck, by the need – in the phrase of legendary NASA flight director Gene Kranz – to continue to work the problem.

David Shaywitz

Entrepreneurial stories, often told through the narrative framework of the hero’s journey, are by now well past the point of cliché. 

Yet, it’s important, perhaps even instructive, to remind ourselves every now and again about the exceptional, transformative power of entrepreneurship, the can-do thinking it motivates, and the remarkable progress it can propel.

This potential emerged as a central theme of Dr. Jill Hagenkord’s recent appearance on the Tech Tonics podcast that Lisa Suennen and I regularly host, as Hagenkord shared her journey that Suennen nicely characterized as “a rags to riches story in the most American of ways.”

A Tough Childhood in Rural Iowa

Hagenkord grew up in rural Iowa, an already difficult childhood made worse, she says, by her parents divorce when she was ten. Dependent on government assistance for housing and food, she was a self-described “behavior problem” throughout school.

Jill Hagenkord

She chose to attend college — an almost unheard of decision for girls in her area, according to Hagenkord — because she wanted to get away, and heard there were “great parties and cute guys” at the University of Iowa, in Iowa City, 90 minutes away.

At college, she partied hard but also worked hard, both in her classes — she made the Dean’s List first semester, despite the nearly complete absence of college prep in high school — and beyond, where she typically held down 2-3 jobs to make ends meet. 

Initially, she focused on fortifying the humanities education she realized she missed in high school. 

However, her life direction was then profoundly influenced, as she tells it, by a “jerk boyfriend,” a medical student who she dated for six years. When the guy told her she was only doing well because she was taking easy humanities classes, she took science classes in order to prove him wrong – which she did. 

Then he bet her she could never succeed in the most difficult pre-med classes, a biology lab. She aced it, despite lacking any of the prerequisites. She won the wager and claimed the prize: dinner at the “nicest restaurant in town” – the local Red Lobster. 

Unimpressed, Hagenkord’s perversely significant other assured her she could never get into Harvard or Stanford University. So she applied and won admission to the MD/PhD program at Stanford.

The Bay Area, Part I

Hagenkord was excited about coming to the Bay Area, but never felt “culturally comfortable” at Stanford’s campus in suburban Palo Alto. So she lived in the city, almost an hour away, and never showed up for class, just for the exams, which she apparently crushed anyway. 

She dropped the PhD part, and decided to pursue a medical residency in pathology — a discipline without a lot of one-on-one contact with (living) patients. She had found aspects of clinical medicine — like telling a patient or a family that there was nothing that could be done — overwhelming sad. 

She started her pathology residency training at UCSF, but was drawn away by the lure of a startup. It was the boom of the late 1990s, she said, and it seemed everyone in the Valley was joining a startup, where new millionaires appeared to be minted by the day. 

The experience, she said, was “exhilarating.” She was surrounded by “all of these super bright science PhDs and tech people, super bright and super ambitious.”  

She added, “I learned so much and it opened my mind up so much to the possibility of how can I be an entrepreneur in medicine.” 

Hagenkord joined the company when it was relatively small, stayed with it through growth to over 500 people, saw it expand internationally, and was there for its IPO. While it doesn’t sound like she saw a large financial return, the startup experience clearly affected the way she thought about problems. 

Even when she returned to finish her pathology residency — which she did back in Iowa — “every step of the way,” she said, “I was constantly thinking — this could be done better, this could be done more efficiently, how do we do this better?”

As Suennen points out on the podcast, this must have represented a huge contrast for Hagenkord: “After hearing all your life that ‘things cannot be done’ or ‘you cannot do this,’ on the entrepreneurial side, things are characterized by ‘you can do anything, if you work hard enough.’” 

It sounds like this more hopeful attitude suited Hagenkord. “It’s such a long shot but you can will it to happen,” she explains. “It was something that had never been done before. And I did get criticized — but it gave me all the courage I needed to think, ‘I can start my own company.’”

Even so, she initially elected to continue her training in Iowa because:

“I tried to be a ‘normal’ pathologist, live a quiet life, make a good paycheck pushing glass slides — but as I finished my residency, there was too much happening on the bleeding edge. What I saw coming were these massively parallel testing technologies, where instead of looking at one analyte at a time, we were looking at hundreds or millions at a time, and were going to need computational tools to distill this information into medically meaningful nuggets. That’s what I was the most excited about and wanted to learn the most about.”

Consequently, she pursued a customized dual pathology fellowship program. The faculty at the University of Pittsburgh Medical Center liked her proposal so much that they “invented” the fellowship for her. The program allowed Hagenkord to gain specialized pathology training in both molecular genetics and oncology informatics – a combination she says has since become more common. 

When she told the head of her residency program about her plans, Hagenkord says, he told her “Jill, why don’t you do a real fellowship? You’re never going to get a job.” 

She adds, with a grin, “When the old curmudgeonly white dude is telling you that you don’t know what you’re doing, you should feel reassured that you’re onto something.”

Bay Area, Part II

She then returned to Silicon Valley, in a series of roles at genetics companies. Early on, she got to know the co-founders of 23andMe. As she tells the story, “I got to know [23andMe co-founders] Anne [Wojcicki] and Linda [Avey — see her Tech Tonics episode here] through the Silicon Valley network. We’re around the same age, have kids, are working moms, live in the same town.”

She continues, “I could look at what they were doing from a regulatory point of view and recognize gaps. I would always say [to Anne and Linda] the consumer aspect of health is so exciting and you are leading the charge, let me help you any way I can. It wouldn’t hurt you to fix these gaps.”

For while, it seems, Hagenkord offered occasional, informal advice.  But “after they got the warning letter [from the FDA] in November  2013, this led to another long conversation and wound up in a job offer [as Chief Medical Officer]. So I joined the company after they were shut down by the FDA, to try to re-build bridges with medical community.”

Hagenkord worked through that thorny set of issues with the team at 23andMe. These efforts paid off, and over time the company worked itself back into the Agency’s good graces. While Hagenkord left the company, she distinctly remembers the day she sold her 23andMe shares (on the secondary market, as the company hasn’t sold shares to the public). Her shares in the private company were worth enough to be “truly life-changing.”

A “Silicon Valley” Moment

On vacation in Mexico, when she heard the funds had been deposited in her account, she says she left a $1,000 tip for the bartender. 

“It felt awesome — and we got really good service rest of time we were there.”

Reflecting on the experience, she describes it as “one of those Silicon Valley moments — not a ‘founder of Google Silicon Valley moment,’ but a decent Silicon Valley moment, when you have more money that you ever, ever, ever thought you’d have shows up in your bank account one day.”

She then reveled in the opportunity to play what she called, tongue-in-check, “Hillbilly Millionaire”:

“I called family — in Iowa — flew back, and said ‘everybody, get in the car, we’re going to the mall, buy anything you’ve ever, ever wanted.’ They were really bad at it, at first.  They went into Target, went back to the clearance sale rack — spent like $250, and I was like, ‘I didn’t fly back here so you could buy $250 at the clearance sale rack — now get out there and really shop!’ They really did.” 

The experience, she says, was “the best feeling ever” — though she also apparently enjoyed the 50^th birthday present she treated herself to — a trip with 22 family members on a private yacht around coastal Turkey,” which she viewed as a chance to appreciate the “people who really supported me.”

After several more years advising health tech companies, she recently took on a new role, as head of genetics at Optum a subsidiary that’s been described as “the information technology and services arm” of UnitedHealth Group.

Once again, she is thinking of moving back to Iowa.

Lessons About Medicine And Tech

In addition to her inspiring story, Hagenkord shared several lessons about her experiences as an entrepreneurial physician working with technology entrepreneurs in Silicon Valley. 

Although my own life journey could not have been more different from Hagenkord’s, we seem to have come to remarkably similarly conclusions from our experiences at the intersection for technology and medicine. 

“In the last ten years,” Hagenkord says,

“tens of billions of dollars have been invested in healthtech, with very, very few success stories. It’s like watching the same movie over and over again, and it’s pretty torturous, especially as I’m usually the only person with a medical background in the company. 

I know the recipe — there are no shortcuts, and if you try to skip a step, you’re going to set yourself back three years. But to watch these brilliant tech entrepreneurs, who’ve been wildly successful in consumer tech, try to take the recipe that’s worked in consumer tech — go fast and break things, fake it ‘til you make it. If you get it wrong trying to sell somebody a pair of shoes online, and they have to wait three days for their shoes instead of one day, it’s going to be OK. 

The same recipe does not work well when you apply it to a real health product –what we call that is ‘unconsented human subjects research’ in healthcare. You can’t sell someone a finished product that you know is full of bugs without telling them. The philosophical and cultural differences between tech and health need to be better balanced. 

Taking the best of these brilliant tech and design people and combining it with the best of the people with the medical experience and the understanding about how to access the medical market — having that balance are the healthtech companies that are going to be the most successful.

Hagenkord also weighed in on whether coastal entrepreneurs think about people in the middle of the country, and whether one can imagine Silicon Valley-type innovation in places like Iowa.

On how Silicon Valley tech entrepreneurs tend to see the world:

“One of the things you see is someone in love with their own technology looking for a place to stick their technology, as oppoed to looking at a problem and trying to solve the problem.

The Bay Area is such a weird bubble place — so not like middle America where I grew up. To be able to really understand — especially when you’re thinking about your commercialization strategy — the idea that ‘people will just buy this.’ People who can’t really pay their mortgage, and have their lights turned off, and are struggling to get milk — for them, looking at their genetics is a huge luxury that’s beyond comprehension, and I think that gets lost sometimes. But often times there are representative people like me in [startups] to remind them of that aspect.”

And the idea of entrepreneurship in the Midwest, as promoted by champions like J.D. Vance and Steve Case?

“I did spend a lot of time thinking about it — it’s really too hard to be an entrepreneur in the middle of the country, because none of the venture money is there. 

There are very smart people in the Midwest; if I had a good loving family, I’d have wanted to stay there and raise my family there. It’s a matter of access — [to capital] and to mentors — so much of the Silicon Valley game is who you know, and who can introduce you to who, and who can vouch for you. When you’re living in Des Moines, it’s hard to make those connections.”

I raised a final point with Hagenkord, by email, after the podcast, asking her about the thought process leading to her recent decision to join not a venture capital firm or a startup, but rather at a massive insurance company — over the summer, she took at new job as Vice President of Genetics at Optum. 

My own top of mind reaction echoed Hamilton’s King George III’s response to the news that President Washington would be succeeded by John Adams: “Good luck.”

But here’s how Hagenkord sees it:

“Health tech startups can raise an ungodly amount of funds, but they often struggle to 1) identify a real, tractable problem in health, and/or 2) try to apply a consumer tech go to market strategy to a health-related product. They are smart people and they learn and iterate, but often they waste valuable time and money learning how the health care market works and what kind of evidence they need to achieve widespread adoption.

At Optum, I can help tech entrepreneurs understand how to get on the market legally and how to successfully get to widespread adoption and payer coverage (if desired). Since Optum invests, partners with, and acquires health-related startups, the tech entrepreneurs and investors have a vested interest in engaging with Optum. They are more inclined to generate proper evidence data when Optum says they need it. I also have the support of an amazing team at Optum Genomics who are all aligned with this same vision. In short, I could do more good more quickly at Optum as part of a team than as an individual consultant.”

I hope her lived experience at Optum ultimately matches up to these lofty, worthy expectations. 

One thing I know for sure: I wouldn’t bet against her!