Once a year, bioinformatics.org gives the “Benjamin Franklin” award to a person whose practice has “advanced open access in data and methods for life sciences.” There’s no cash prize, and the recipient doesn’t even get to give a talk. It usually gets presented in the 15 minutes before the (already early) 8:00am morning keynote sessions at the Bio-IT World conference. I’ve missed more of the awards ceremonies than I’ve attended because of the early hour.

I became a member of bioinformatics.org back in 2002 – the first year of the award. If I remember right, I signed up after meeting the founder, Jeff Bizzaro at some now-defunct conference. It might have been one of the O’Reilly Bioinformatics junkets, or maybe one of the IDG ones … perhaps ClusterWorld. Jeff was doing the legwork of building an organization, convinced me the award was a good idea, and got me signed up with a free membership.

Last Friday, Andrew Su (@andrewsu) pointed out on Twitter that 17 out of 18 of the Franklin awards have gone to men. He called on the prior recipients to “do something.”

Jonathan Eisen (@phylogenomics), who received the award in 2011, reminded us that he had come to the same realization half a year [edit: actually quite a while] earlier. Eisen took the award off his wall and memorialized the deed on Twitter with a blank-spot-on-the-wall selfie back in May.

Michael Eisen (@mbeisen – the 2002 recipient, not to be confused with Jonathan, above) went further and used some pretty strong language to demand that the award be closed down as a bit of accountability for the “injustice reflected in [its] history.”

My opinions on this are below, but first I want to share a couple of stories from a parallel universe – the world of awards given to science fiction authors.

Sad Puppies

Until late last year, the “John W. Campbell Award for Best New Writer” was an annual thing. Then the 1999 winner, Jeannette Ng, pointed out in her acceptance speech that John Campbell was a noted fascist and a truly odious human being by modern standards.

In the space of about two weeks, the sci-fi community basically said “huh, about that, oh wow, um, yeah – good point, –awkward-, how about if we change the name?” Thus was born the “Astounding” award for best new writer, after the grand old “Astounding Science Fiction” magazine.

Speaking up makes a difference. Jeannette Ng did a good thing.

The sci-fi community has done a lot of this sort of work in recent years. In 2015 through 2017, a right-wing, anti-diversity group, organized under the name “Sad Puppies” saturated the nominations for another venerable award (the Hugos). Fortunately, the community came together and a majority of voters chose “no award” rather than support the puppy’s biased agenda.

Neither of these situations is a great model for what’s going on with the Ben Franklin award. The name isn’t the problem – Franklin was a noted curmudgeon and was certainly a product of his era, but his name and writings are not generally associated with oppression and inequity. Similarly, I’m not aware of any puppy-orthologs who are suborning bioinformatics.org or manipulating the nominations.

Don’t Blame the Mirror

I have huge respect for both Michael and Jonathan’s scientific and public advocacy work, and I hesitate to second guess either of them – let alone both at the same time. Still, I can’t get around the fact that if we don’t like what we see when we look in the mirror, taking the mirror down is not going to improve the situation.

I think that throwing the Ben Franklin award under the bus as if it had actively perpetuated bias and sexism misses a subtle point. This was a sin of omission, of failing to compensate effectively, rather than a sin of commission. This is a common story – people and systems that fail to take pre-existing bias into account wind up perpetuating it. It is, in fact, the mechanism by which good and decent people become complicit in woefully biased organizations.

My experience is that this sort of public punishment of sins of omission tends to drive decent people away from advocacy. This acts to push the community in the wrong direction, away from engagement rather than towards it.

I’ve written about why men hesitate to speak up, and it’s nasty stuff. I also spoke at a recent meeting of the Rosalind Franklin Society about my own challenges in engaging with my well-intentioned but bias-perpetuating colleagues without simply losing friends and cutting myself out of the conversation.

What Should We Do Instead?

We’ve got a ways to go before we’re anywhere close to the high bar set by the community of science fiction authors and fans, but we can take steps. I think that we should modify the award rather than nuking it and shaming the organizers.

My vote is for bioinformatics.org to carry on with the Franklin award, but to modernize the selection process to prevent it from merely continuing to reinforce and amplify bias. The necessary changes from my perspective are (a) insist on a credibly diverse pool of nominees before allowing a vote (which Jeff B. and Bio-IT World should perhaps have been doing all along) and (b) include the possibility of a “no award” vote as a fail safe against a sad-puppies style vocal minority messing things up. Coupled with advocacy and engagement from senior members of the community to nominate and sponsor worthy people who might not yet be household names, we might see the numbers start to change in a relatively short time.

At the same time, yes, let’s create new awards. Let a thousand flowers bloom, and let them be named for Dayhoff and other frequently overlooked pioneers. Let’s just not miss the opportunity to take a clear-eyed look at what drove these biased results, and think about how we can steer the community so that we’re not having the same conversation 20 years from now.

People sometimes ask what motivates me to speak out publicly as an advocate and an ally to women and other underrepresented groups. Sometimes these questions are sincere. Mostly they are not.

As I have found my voice on this topic, I have become aware of a pattern of discouragement, insinuation, insincere questions, and intimidation from some of my male peers. This pressure, and my fear of it, turns out to have been part of why I hesitated for so long before engaging publicly.

My hope is that naming this phenomenon will diffuse its power, both for me and hopefully for other men too. Speaking only for me, I can assure everybody that there is a much better and more enlightened adulthood once you get past the schoolyard bullies.

Anybody who has dealt with bullies knows that it’s a mistake to engage with the details of any particular taunt. A bully’s taunt is a front. It’s the convenience store that’s open until 2am but never seems to sell anything. You have to ask yourself what’s really going on there.

Let’s go ahead and unpack a few of those:

Taunt #1: Virtue Signaling

Let’s start with the idea that advocacy is “just virtue signaling.” It’s supposedly a hypocritical, self reinforcing in-group activity. The particular words carry no more meaning than the back-and-forth honking of geese.

Wikipedia describes virtue signaling as a “pejorative neologism,” in which one “signals support for a cause without actually acting to support the cause in question.” The call-out example is a picture of a man doing the “ice bucket challenge” that was all the rage back in 2014 to raise funds for ALS research.

The example is misleading. That particular campaign raised more than $100M, which transformed and radically accelerated ALS research. Apparently the participants inspired action with their chilly signals.

Bullies don’t get hung up on details.

I am, in fact, trying to signal a directional change here. Honk, honk guys, we’re off course. Let’s steer away from systemic bias and towards equity. Don’t worry about what the haters say.

Taunt #2: Points with the Ladies

I’ve also been told that my advocacy is just a hustle aimed at scoring points with women. Sometimes it’s specific: “Bro, she’s not gonna sleep with you.” Other people seem to posit a sort of gender-wide currency, redeemable with any woman, worldwide.

I’m disturbed that some of my peers still seem to subscribe to the toxic and juvenile model of woman as vending machine. Offer enough tokens and a prize emerges. Most of us got over that idea sometime during or shortly after high school. Bro, you know that’s not how it works – right?

More succinctly: Women don’t owe me (or you) shit.

What I’m being told is that I’m embarrassing myself. I should stop. I look pathetic. They seem to be saying that everybody can see that I’m desperate and lonely. They say that I’m doing it wrong.

They’re saying that speaking up for women is performing manhood incorrectly.

Nasty stuff, right?

Taunt #3: We don’t talk about it

Then there’s the idea that it’s inappropriate and crass to talk about your advocacy. I’ve been told by more than a few peers that they do way more to help women than I will ever know. They just do it so subtly that nobody can detect their good works.

While this is theoretically possible, I think it’s unlikely. The things they are so proud of likely amount to the bare minimum required by law. Failure to abuse women doesn’t count as advocacy. Hiring the occasional woman and paying her equal to her male counterparts is the baseline, not some laudable crime-fighting secret identity. Treating daughters and female spouses with respect and dignity is basic human decency.

The idea that a privileged person should not have to be bothered to even hear about bias is, in fact, part of the structural problem. The freedom to not have to talk or think about equity is the very definition of privilege. This is the domain of the “gender-blind,” or the “color-blind,” with an emphasis on “blind.”

Perhaps the real problem here is that I’m messing with that privilege.

The point: Shut Up

If you keep speaking up for long enough, the threats emerge.

I’ve been told that having a reputation for public advocacy will hurt my career. This hasn’t been my experience, in fact I’m hard pressed to think of a way that my career could be going better. Of course, there’s bias and privilege at work there too. I started from a good place and I got loud only after I was well established. I do suggest that early career folks, to quote a speaker at a recent RFS event, “secure your own oxygen mask before attempting to assist others.”

That’s because vulnerable people do suffer consequences when they speak out. The people who most need advocacy and sponsorship put themselves at substantial risk of reprisal, quiet and loud, when they advocate for themselves. That’s why it is so patently unfair to push the work of advocacy off on members of underrepresented groups.

Bringing it full circle, that’s why I speak up. If I don’t, the burden falls to folks who are much more likely to suffer career consequences for it.

It’s all in your head

Anybody who started off as a child (everybody) has these patterns baked into them. We impose social judgements on ourselves without requiring external censors or gatekeepers. The only bully needed is the one inside my own head. I’m honestly a bit grateful to my peers for giving voice to those quiet thoughts and allowing me see them clearly.

A few years ago, a young colleague approached me, worried that people might think he was “creepy” if he offered to help with the women’s advocacy group at work. I asked, “well, are you doing it for creepy reasons?” After a bit of conversation, we convinced ourselves that his intentions were not creepy. I encouraged him to go for it. It’s good to get a sanity check on these things, and it’s a shame that worry held him back.

Talking about it helps.

How to shut me up

That said, I actually am talking more than I would like here.

There is limited space in any conversation. Attention paid to one voice comes at the cost of others who deserve to be heard. A really important and challenging aspect of advocacy is the art of holding space so that underrepresented voices can be heard. It’s about knowing when to speak up and knowing when to shut up. A recent New York Times opinion piece implored men to “lean out” instead of merely telling women to “lean in.”

But how to lean out without simply maintaining the status quo?

To the bullies and the haters, here’s my commitment: I will quiet down and stop making such an unseemly fuss when our whole conversation is so rich, diverse, and safe for everybody that I feel obliged to back off.

Until then, get used to it.

This post summarizes gender representation in the founding, leadership, board, and scientific advisory teams of 162 biotech companies – observed between October and December of 2019.

Out of 162 companies listed on the “portfolio” pages of the big three Boston biotech venture firms (Third Rock Ventures, Atlas Venture, and Flagship Pioneering), 118 were still in business when I visited their websites. The “about us” pages on those websites listed 2570 distinct leadership roles. Those roles were filled by nearly that many unique people, though some notable individuals did make an impressive showing across a variety of different companies. I counted up the men and women, along with job titles, taking gendered pronouns at face value, and applying only the most basic of statistics.

Simple as A, B. C

My preliminary writeup only covered the first 67 companies on the list, alphabetically. At the end of that earlier post I mused (somewhat facetiously) that women leaders might be over-represented at the far end of the alphabet, making up for the under-representation I had seen so far.

This turns out to not be the case.

Although Zafgen did have an impressive 50/50 split of men and women in leadership, the gender ratios from that first post have been essentially borne out in this larger survey.

Key Performance Indicators

Eleven of the 162 “portfolio” companies have closed their doors. 38 were acquired, 53 made initial public offerings, and the remainder are still privately held. 118 of the companies are still recognizably in business, though some have changed their names and others operate as business units of larger parent organizations.

Out of 901 leaders serving within the businesses there were 643 men and 258 women (29%). This tracks with other recent surveys both of biotech and also tech more broadly. Boards of Directors have lower representation, with 659 men and 126 women (16%). Scientific advisory boards were an even more skewed population, perhaps more reflective of the academy, with 355 men and only 58 women (14%). Women account for just 32 (7%) of 471 of “founders.”

I found seven companies whose websites showed a total of 61 people without including any women at all.

Rank and seniority

As in other recent surveys, the 29% overall representation of women skewed strongly by title. Women show up more frequently at the Director and VP level (~40%), less frequently in the C-suite (24%), and least of all as Chief Executive (13%).

The pattern repeats (though with much smaller numbers) on Boards of Directors. This lines up with the “pipeline” narrative: CEOs and Board Directors are usually people who already have a strong history in the C-suite. Board chairs tend to be people with experience serving on several Boards.

Founding a company is a way that an ambitious person can skip the line and serve as CEO early in their career. Women founders are rare among companies supported by the big three, even in recent years (7% to 17%). This representation will tend to maintain the current distributions rather than disrupting them.

Specialties

When we take professional specialty into account, it becomes clear that the science and engineering “pipeline” is not really all that strong at the leadership level.

I exercised a bit of discretion and stacked directors and VPs of biology, chemistry, physics, proteomics, biophysics, virology, and so on into a single category named “science.” Similarly, I binned VPs of people, human resources, people and culture, and so on into “HR.” Clinical affairs, medical affairs, clinical science, clinical operations, and so on all became “medicine.” General counsels, heads of compliance, and even health policy officers all wound up in “law.”

My findings mirror the distribution found in other studies. Women are a substantial majority in leadership roles for communications, HR, and project management. Strategy and law are about at parity, and one is only slightly more likely to find a woman leader, at any level, in “science” (14%) than to find a woman CEO (13%). Women engineering leads, at 12%, are rarer than women CEOs.

Whatabout

One might ask whether those women CEOs represent the tip of the spear, building the next generation of women leaders. The data says ‘no.’ When I split out companies according to the gender of the CEO, removing the CEO themself from the count, I was left with 32% women on the men’s leadership teams and only 18% on the women’s. This aligns with the lived experiences I heard about at a recent meeting of the Rosalind Franklin Society. Several speakers described male mentors and sponsors as being not just more numerous, but also more effective than the women.

Diversity statements on the “careers” page do correlate with slightly higher representation of women in leadership (33% vs 24%), though those come in a variety of flavors. My intuition says that cold and legalistic disclaimers about obeying applicable law do nothing – but my spreadsheet already includes enough biases, judgements, and assumptions without attempting to grade these toothless paragraphs.

Call to Action

I believe that each of us, regardless of the role we play in our organization, has an opportunity to make an impact on safety, inclusion, equity, and diversity. For the less senior folks in the audience, I will echo the advice of one speaker at the RFS event: “Secure your own mask before attempting to assist others.” There are still career consequences to making waves, and it’s much easier to fall off the ladder than to move up it.

People whose privilege and authority are well enough entrenched that there is no risk in speaking up have an obligation to act and begin to correct the systemic biases we inherited. I shared some specific tips about what works (and what doesn’t) – at all levels – in a recent talk.

Advocacy in the Enterprise (what works, what doesn't) from Chris Dwan

Conclusion

Thank you for reading this far.

Experience suggests that I lost most of my target audience as soon as I mentioned “gender representation” in the title – but one can always hope. I think that we have a great opportunity to make real near-term changes to equity and inclusion in biotech simply by getting more people out of the stands and onto the field.

As I said in my slides, there comes a time in the leadership journey when culture becomes part of the job.

If any of the good folks in leadership roles at the venture firms happen to read this, know that I’m optimistic about the potential you hold. The explosive growth of biotech means that we have an opportunity here to make a dent in our biased culture today rather than waiting 20 or 30 years for the pipeline to make its slow corrections.

More than a billion dollars a year flows into biotech. The community is exquisitely attuned to the interests of “the money.” Do we know whether “the money” looks askance at pitch after pitch by all-male teams? Do you insist that your scouts go far afield to find the very best and most innovative thinkers, or do they just meet up with the usual crew at the Tatte coffee shop on 3rd Street in Cambridge, just off of Binney Street?

I will close with a specific point: None of us should participate in all-male panels anymore. We all know that the correct response is to use the momentum in those invitations to open doors and adjust conversations. We shouldn’t create, fund, or serve on all male boards of directors anymore.

It’s time for equity and inclusion to become part of biotech’s DNA.

On a whim the other day, I scraped the “portfolio” page on the websites of three of the large venture capital firms in Kendall Square (Atlas Venture, Flagship Pioneering, and Third Rock Ventures) to generate a list of 162 biotech companies.

The first thing I noticed was that the company names seem, disproportionately, to start with the letter ‘A.’ If company names were distributed like English words, the most common starting letter would be ‘T’. Instead, for whatever reason, we see a skew towards A, C, and S.

If we wanted to, we could come up with some sort of an explanation for this phenomenon. My personal bet is that there is a benefit to being at the top of the stack in competitive evaluations for funding. A famous study of Israeli parole boards tracked judges becoming steadily less merciful as they got hungry and tired. I’m willing to bet that venture capitalists exhibit similar behavior. If that’s true, and if people (like me) tend to sort things alphabetically, then we would expect the list to be enriched for the likes of Acceleron, Afferent, Agios, and Annovation.

Conversations about bias in the workplace can be stressful. Even bringing up the subject can feel like an accusation and incite a rush to judgement. We get so caught up in what it means and what we should do, that we lose sight of what is actually there. The rest of this post explores gender ratios in biotech leadership. I encourage you to relax away from judgement for a moment and merely consider the numbers as a sort of intellectual curiosity – as if we were talking about lexical anomalies .

Founder Effects

I went through the list in alphabetical order, looking up the people who got credit for “founding” each company. It turns out that “founder” is not terribly well defined. I included all of:

• Whoever Crunchbase said was the founder – these turn out to mostly be people from the venture firms
• Everybody mentioned as a founder on the company website
• Anybody who got a mention as a “Founding Whatever” in launch announcements in industry news sources like Fierce Biotech, or Xconomy.

Then I added a column and tagged each person according to a guess at their gender. I took gendered names and pronouns (he / her) at face value. In cases where I had any doubt, I dug around on the web until I found a bio that used a gendered pronoun. I didn’t find even one person with nonbinary pronouns or presentation in their professional persona.

With a couple days work, I got through the first 67 companies (all the way to “Fulcrum”). In those companies, I found 190 people listed as founders: 177 men and 13 women (7%).

Remember, no judgement, no explanations. We’re just counting. In this case, we’re double counting. We have some incredibly prolific founders, like Noubar Afeyan of Flagship Pioneering who shows up in 9 of these 67 companies.

The plot below spreads out those gender numbers by year. It suffers a bit from the small sample size, but – if I squint my eyes – I can begin to imagine a couple of trends.

Leadership

Out of my list of 67 companies, 47 of them are still doing business under their original name (including 24 IPOs). I went to their websites and scraped the names and titles out of the pages dedicated to the leadership teams, the boards of director, and the scientific advisors.

I took some liberties with the titles. Even though Nick Leschly of Bluebird Bio is listed as “Chief Bluebird,” I still lumped him in with the CEOs. For people with multiple titles, I selected the highest ranking one. I also exercised a bit of judgement and combined different versions of what seem objectively to be the same role (Biology vs. Biological Sciences, for example). I counted 16 distinct ranks (chief through assistant) and 78 distinct areas (science, law, HR, and so on).

Out of 347 people listed on the “leadership” pages, 244 are men and 103 are women (30%).

Gender distribution varies widely with area of responsibility. I didn’t find any women in leadership roles for data or “tech,” nor did I find very many men leading human resources or project / portfolio teams.

The Board

Boards of Directors vary widely from company to company. Most boards are made up of representatives of the firms who have made significant investments, a senior executive or two from within the company, and a selection of industry veterans.

Out of the 286 board members in my little study, 237 of them were men and 47 were women (17%) – which is very similar to the gender representation I found among CEOs. Out of 35 “chair” positions, I found 33 men and two women (6%).

The numbers were similar for the 153 people listed on the scientific and clinical advisory boards. Out of that total, 139 were men and 14 were women (9%).

So What?

As I said at the beginning, these numbers are, by themselves, neither good nor bad. They are simply a snapshot of a tiny slice of an incredibly dynamic industry. For all that, gender representations like 7%, 9%, 17%, and even 30% do seem to beg the question, what’s going on here? This is pretty far from an unbiased selection out of the human population. Scientists are constantly generating hypotheses and ideas for how to test them – I feel that some of that intellectual rigor might be valuable here.

My plan is to continue to parse, sort, sift, and learn. I’ve got 100 more company websites to scrape, and a few more low-hanging analyses to run. I expect to generate at least a couple more blog posts from this data, which will hopefully spark some downstream conversations.

At the very least, I hope to answer the question of whether or not the women leaders are mostly hanging out in the companies towards the latter half of the alphabet.

As always, I’m deeply interested in your thoughts.

This is the second in a series of three or four posts.

The first one, diverse teams perform better, explored some of the research on the measurable performance advantages that diverse teams have over monocultures. This and future posts will share real world examples about measuring and correcting for the bias that leads to a lack of diversity. I hope to build a toolkit of useful techniques that can reduce bias in recruiting, hiring, promotion, and retention.

I believe that my professional communities – high performance computing, biotech, data science, genomics, and the adjacent specialties – have a bias problem. Eventually, I hope to make that argument in some detail. Posts like this are the necessary groundwork. I hope you will bear with me.

Hang In There

Unfortunately, a lot of readers are about to check out on me.

Even though this post is absolutely not about subjective personal labels like “racist” or “sexist,” conversations about the specifics of measuring bias seem to land for some folks as accusations of bigotry. I’ve had more than a few friends and colleagues turn belligerent and hostile at this point in the conversation. I’ve been told that even wanting to talk about diversity beyond a high and fluffy level makes me sound like the “diversity police.”

Apparently nobody likes the diversity police.

Bluntly, if a person can’t bear to even talk about how one might measure and compensate for bias, it’s a pretty safe bet that their organization is rife with it.

Still, I’m not making personal accusations or calling anybody nasty names. Name calling is ineffective and misguided. It’s a waste of time.

Anne-Marie Slaughter said, “systemic bias does not require a conspiracy of men.” The Harvard Business Review built on this in their 2016 piece Designing a bias-free Organization: “…Rather than run more workshops or try to eradicate the biases that cause discrimination, companies need to redesign their processes to prevent biased choices in the first place.” Effective managers and leaders should focus on compensating for bias, rather than “naming and shaming.”

Without the power to cut funding, terminate employment, or otherwise impose consequences – naming and shaming just enrages powerful, biased people. That’s never a good scene.

Still, I acknowledge that we’re on uncomfortable ground. In a good-faith effort to keep my HPC, biotech, and genomics friends engaged, I will start off with an absolutely non-tech-centric example in which I cannot possibly be talking about them.

Let’s go to the symphony.

Curtain Call

It sounds strange to my modern ears, but symphony orchestras used to be predominantly male. In the 1970’s, women accounted for only 6% of the players in top tier ensembles. By the 1990’s that number had risen to 21%. One major factor in that increase was the practice of “blind” auditions, where the player is hidden from the view of the judges behind a curtain or a screen.

According to the 2000 study Orchestrating Impartiality: The Impact of “Blind” Auditions on Female Musicians: “Using a screen to conceal candidates from the jury during preliminary auditions increased the likelihood that a female musician would advance to the next round by 11 percentage points. During the final round, “blind” auditions increased the likelihood of female musicians being selected by 30%.“

No quotas, no diversity police, no name calling. Just a curtain.

Let’s look at how that worked in practice:

In 1980, Abbie Conant auditioned for principal trombone of the Munich Philharmonic. The orchestra did not usually do blind auditions, but in this case one of the applicants was a relative of the decision maker, so they decided to guard against any appearance of favoritism.

Oops.

By all accounts, Conant blew the doors off that audition, performing well enough that one judge leapt to his feet, saying “there’s our trombone.” On finding out who “their trombone” was, the director was nonplussed. He demoted her to second chair, paid her less than her peers, and spent years trying to get her fired. Conant spent more than a decade waging a rather epic lawsuit against the orchestra while at the same time building an international reputation as a soloist and teacher.

The Berlin Philharmonic didn’t do another blind audition for nearly 20 years.

Mere Numbers

I love the simplicity of the study above. They literally just counted the number of men vs. women who got hired before and after a process change. As the HBR article says, “Marketers have been running A/B tests for a long time, measuring what works and what doesn’t. HR departments should be doing the same.“

Of course, when we start trying to count, we find out that some job applicants are constructing their own blind-audition screens. People replace their names with initials and otherwise remove the signals that biased organizations use as unconscious cues.

What can I say? People are smart.

I assume that everybody has heard about Jo Handelsman’s straightforward and awesome study “Science faculty’s subtle gender biases favor male students.” The researchers asked faculty members to evaluate resumes for a notional lab manager that they might hire. The resumes were identical except for the gender of the applicants name.

“Faculty participants rated the male applicant as significantly more competent and hireable than the (identical) female applicant. These participants also selected a higher starting salary and offered more career mentoring to the male applicant.”

One really important result from this study is that the gender of the faculty member didn’t affect the result. “Female and male faculty were equally likely to exhibit bias against the female student.”

So spare me the line about how some particular decision can’t be biased because a woman or a person of color participated in it. That’s not what the data says.

Excuses

Let’s get real for a minute: Very, very few people in any of the industries where I make a living have adopted anything even as basic as the blind audition.

We’re not even looking. We’re not counting. We’re not doing the basic work, and yet we make excuse after excuse.

We’re damn sure not doing the rigorous statistics that would be required to back up arguments about pipelines, cost-benefits of delay vs. performance, and so on. Future posts will get specific about what how we might do that.

For the moment, just rest with this question:

If your company had a “principal” position – trombone or otherwise – and the child of a powerful board member was applying for the job, what would you do to guard against the appearance of bias?

Now do it anyway.

Just don’t be surprised when the person who blows the doors off your interview process isn’t exactly who you were expecting.

In my next three or four posts, I’m hoping to lay out a story that goes something like this:

• Diverse teams outperform monocultures.
• Biases in hiring and retention mean missing out on that performance boost.
• Biased systems self perpetuate. It takes action to break the cycle.
• Here are the most effective actions to take.

My motivation in writing this is that I’m speaking at the November meeting of the Rosalind Franklin Society. My working title is “Advocacy in the enterprise: What works, what doesn’t.” In that talk, I plan to share stories about some times that I’ve attempted to cause organizations to be more diverse and inclusive.

My hope is that writing these posts will force me to check my references and get my thoughts in order. If it turns out that they also make a decent intro for other people – that’s great – but this is mostly a thought exercise for me.

Onward to the first point: Do diverse teams really outperform monocultures?

Direct Experimentation

Many researchers have measured the impact of diversity on team performance. I’ve written before about my very favorite experiment along these lines. Researchers at Northwestern University and Stanford took members of various fraternities and sororities and had them work in small groups to solve a murder mystery. The control groups were homogenous – all pulled from the same greek house. The test groups were spiked with an “out-group” member, matched for gender but from a competing fraternity or sorority.

As an aside, this is just about the most trivial difference that can possibly be measured – a Planck constant for diversity, if you will.

The result: “Groups with out-group newcomers (i.e., diverse groups) reported less confidence in their performance and perceived their interactions as less effective, yet they performed better than groups with in-group newcomers“

This work appears as a 2003 conference presentation titled “The Pain is Worth the Gain“, and then as a 2008 publication in the Personality and Social Psychology Bulletin titled “is the Pain Worth the Gain? The Advantages and Liabilities of Agreeing With Socially Distinct Newcomers.” The Kellogg School of Management at Northwestern published a nice (and open access) summary in 2010 titled “Better Decisions through Diversity.”

The abstract in the journal observes: “Performance gains were not due to newcomers bringing new ideas to the group discussion. Instead, the results demonstrate that the mere presence of socially distinct newcomers and the social concerns their presence stimulates among oldtimers motivates behavior that can convert affective pains into cognitive gains.”

Another team of researchers had people invest money in simulated stock markets in Southeast Asia and again in North America. The paper “Ethnic diversity deflates price bubbles” appeared in the Proceedings of the National Academy of Sciences in 2014. The authors found that “market prices fit true values 58% better in diverse markets. In homogenous markets, overpricing is higher and traders’ errors are more correlated than in diverse markets .. our findings suggest that diversity facilitates friction that enhances deliberation and upends conformity.”

The results point to a common mechanism: Diverse teams, and also individuals in diverse environments, work harder to avoid groupthink, which makes them more adept at solving problems.

So it works in the lab – but what about the real world?

Real World Results

There is also a raft of literature analyzing real world results and reading tea leaves to find the secret of success.

If you’re an academic who wants your work to be read and cited, you might be interested in the 2018 piece in Nature Communications, “The preeminence of ethnic diversity in scientific collaboration.” The authors analyzed millions of papers to look at the effect of ethnicity, discipline, gender, affiliation, and academic age on academic impact. The remarkable finding: “While these findings do not imply causation, it is still suggestive that one can largely predict scientific impact based solely on average ethnic diversity.”

The story is the same on the corporate side of the world. The 2012 paper “Gender diversity within R&D teams: Its impact on radicalness of innovation” in the journal Innovation, Organization, and Management studied more than 4,000 Spanish companies. “The results indicate that gender diversity is positively related to radical innovation.” The 2013 paper “Cultural Diversity, Innovation, and Entrepreneurship: Firm-level Evidence from London” in Economic Geography found similar results across thousands of British companies.

Okay, okay, but has anybody done a really wonky deep dive on how this works?

A Theoretical Approach

There’s a very old joke about a mathematician who asks “sure, your experiment worked, but what does the theory say?” All the experimental data seems to point in the same direction, and fortunately the theory agrees: In 2004, Lu Hong and Scott E. Page published a paper in the Proceedings of the National Academy of Science titled “Groups of diverse problem solvers can outperform groups of high-ability problem solvers.”

The title pretty much gives away the punch line.

Let’s ask some consultants what they think.

Arguments from Authority

In 2015, Mckinsey published one of their trend-setting reports, which says: “More diverse companies, we believe, are better able to win top talent and improve their customer orientation, employee satisfaction, and decision making, and all that leads to a virtuous cycle of increasing returns. This in turn suggests that other kinds of diversity—for example, in age, sexual orientation, and experience (such as a global mind-set and cultural fluency)—are also likely to bring some level of competitive advantage for companies that can attract and retain such diverse talent.“

If you’re not into consulting firms, perhaps you’ll listen to a four star general and commander of the US special forces instead. Allow me to commend General Stanley McChrystal’s book “Team of Teams.” While not explicitly about diversity, one of the book’s major themes is that breaking down group identities and avoiding groupthink is critical to innovation and high performance.

Put simply, the argument that building a diverse team is not worth the effort flies in the face of decades of research and experience, from multiple fields, all over the world. Failing to do the work (and it is work, more on that below) makes your company less innovative, your work less impactful, and your investors less profitable.

No Free Lunch

Let’s be blunt: Nobody is saying that this is easy.

When I let google autocomplete from a prompt of “diverse teams,” “Feel less comfortable” is higher in the list than “make better decisions.” That was a lesson learned from many/most of the studies above. The benefits of diversity come at a cost. It takes effort to recruit and retain a diverse team. The benefit is that such teams play at a higher level.

We will also encounter active resistance along the way. Let’s talk about that for a moment.

The Resistance

There is a particular sort of nay-sayer who protests that any effort to increase diversity is all about lowering standards and letting unqualified people slide. They’re the ones who insist on talking about diversity efforts as if quotas based on race or gender are the only tool in the toolbox. It’s well known that raw quotas are a pretty terrible practice, as spelled out in the Harvard Business Review piece from 2016, “Why Diversity Programs Fail.“

An earlier HBR piece, from 2013, “The Trouble With Gender Targets” describes the phenomenon of resistance well: “Targets are like a red flag to a bull for these men and women. They experience it as an affront to their deeply held meritocratic principles.”

Did you catch the “men and women” in that quote? That’s going to be a big theme in the next post.

This behavior isn’t rational, but once people are “enraged” and “affronted,” they don’t tend to spend much time with the literature. Even when research, experience, and professional consultant advice all point in the same direction, resistance and confusion persist.

So we have work to do.

Fixing Problems at the Root

I’m a consultant. I solve problems for hire.

As part of my practice, I try to go beyond the superficial technical problems, and also address the underlying social pathologies. Done well, this can radically empower and accelerate teams. It’s also much more challenging and fun than merely speeding up yet another computer program.

For example: I often get brought in to help with some supposedly thorny and opaque technical problem, only to find that the team already knows the solution. In this case, finding and bridging the gap that prevented leadership from hearing, understanding, and trusting their team’s own expertise is the subtle work that leads to lasting change and improved performance. There are plenty of other “people” challenges that can hold teams back, and bias is a really common one.

As organizational pathologies go, bias is pretty easy to diagnose. If you’re paying attention, you can literally see it when you walk into the room.

That will be the meat of the next post: How to detect and measure bias.

For now, I will close with an invitation: I am -very- interested in your thoughts on this topic. If you have thoughts, please comment below, shoot me an email, message me on Twitter, or whatever. I’m easy to find.

Earlier this year, I purchased Dante Labs “whole genome Z” service, which includes 30x sequencing of every base pair of my DNA, plus an additional 100x on the protein coding regions.

I mostly did this for the raw data. I work in this space and I like to tinker. Using my very own data shields me from any concerns about privacy, consent, and appropriate usage. It’s also super useful professionally: I’m an advisor to folks who are responsible for health and genetic data from hundreds of thousands of patients and research participants. I find that handling my very own information has a way of clarifying my thinking around privacy, consent, and other topics related to good data stewardship.

My experience thus far with personalized genomics is that there’s not a huge amount of diagnostic or clinical value there unless you’re dealing with cancer, the risk of inherited conditions, or a challenging undiagnosed disease. I’m in my 40’s now. I would already be aware if I carried any of the most readily diagnosed genetic disorders.

The joke is that 23andme told me that I’m probably male, most likely of northern European ancestry, sorta average height, probably brown eyes, likely brown hair … you know … all things you could tell at a glance by looking at me.

Aspirin

My expectations were low when Dante Labs sent a note inviting me to check out their new “wellness and lifestyle” report. I was 100% surprised to see that the first item on the list was a “high risk” for “Aspirin.” That’s new for me, and I was sort of hoping that the new data had unearthed some heretofore un-observed risk factor.

Spoiler alert: It had not.

I clicked in to see details, and got this rather opaque wall of generated text, which had obviously never been edited by a human. Maybe that’s what they meant when they claimed to be revolutionary in their use of artificial intelligence in these reports.

I didn’t know the word “urticaria,” so I googled it. It’s hives: red, raised, bumpy, itchy skin. Millions of people have it, it’s irritating, completely self diagnosable, and eminently self treatable.

I got curious. I take daily low dose aspirin because I’ve read about a constellation of positive effects. The question is, should I stop?

The really simple clarifying question would have been “do you break out in hives when you take aspirin?” The answer to that is “no,” but bear with me, I’m telling a story here.

Nerding out on genetics

The first question with any kind of genetic diagnosis is whether the data is correct. Fortunately, I’ve been a genomics fanboy for a while, and I was able to crack open my raw data from 23andme. Yes indeed, at position 179,220,638 on chromosome 5 I am heterozygous – with an “A” on one of the copies and a “C” on the other.

> grep rs730012 genome_Christopher_Dwan_v1_v2_v3_Full_20170926071925.txt  
> rs730012 .  5 . 179220638 . AC

After verifying data quality, the next question is “how sure are we about this?” There is a lot of truly tenuous associative research out there, and a naive approach is almost certain to lead you astray.

I took a look at ClinVar, a remarkably powerful and well curated database of the clinically actionable variants. It said that yes indeed, there is an association between this variant and an allergic reaction to aspirin.

I skimmed the abstracts of the three publications, and while it’s a clear association, it’s not the strongest of signals. The three studies were pretty small, with case and control groups of around 100 people each. Importantly, all three studies asked the question “is this genetic variant more common in people who break out when they take aspirin,” rather than asking the deeper and much more challenging question of -why- such people might have such a reaction.

Short version: It turns out that the reaction to aspirin is more common among people with a “C” at that position at either or both copies of your chromosome 5. In industry parlance, I’ve got one copy of the “risk variant.”

One really important question when looking at this sort of thing is to determine how rare this genetic variant is. My friends at SNPEdia have done a great job of parsing a bunch of different resources to show the answer. In this case, the answer is that among caucasians, my genotype is actually the most common type. It’s pretty rare in other populations, but for white folks like me – most of us have either one or two copies of the risk variant.

So what you have here is a super common genotype that’s associated with a minor, self diagnosable and self-treatable condition.

So should I stop with my daily aspirin? The answer is probably not.

Other genes, other diseases

SNPedia is my go-to for quick reads on genes and variants. I did a little poking around on aspirin and found a ton of interesting stuff. As just a single example, we’ve got Rs6983267 over on Chromosome 8.

Don’t look at me like that. All interesting people have at least one odd hobby where the nerd-o-meter reads “extreme.” This is one of mine.

There’s a study of more than 3,000 caucasians with the exact kind of cancer that killed my grandfather, and I have the risk variant (‘GT’) here too. The middle red box on the right, next to ‘GT’ says “aspirin reduces the risk of colorectal cancer.”

Sadly, this one didn’t make the cut for Dante Labs. According to them, I’m 100% free of colon cancer markers.

So what’s the point?

The point is this: This stuff is complicated and it is important. I’ve written before about how the risk averse culture in American medicine holds us back. This is a counterexample. A naive person might have looked at that report and said “oh hey, I’ll stop taking aspirin, I’ve got a risk factor.” The simple fact is that the risk is for a minor, eminently detectable condition, and there’s good data to suggest that taking aspirin (specifically, for me) reduces my risk of dying painfully of a kind of cancer that runs in my family.

I don’t want the FDA to shut Dante Labs down, but I do want Dante to get their act together and stop just yammering about “AI.”

A side note

In the course of writing and editing this, I have noticed a confounding factor.

Over the past couple of years, I -have- in fact noticed a couple of reddish patches on my torso. I’ve treated them with antifungals, but it didn’t have an effect. They don’t itch and they aren’t terribly visible, so I don’t worry about them.

So, just now, here at the end, I’m thinking that I might cut out the aspirin for a month and see if those patches fade. In that case, I will have learned something. After that, I will 100% resume the aspirin, because duh.

What are the most important pieces of professional advice you’ve ever received?

I remember one of mine clearly: It was in late 2004, and my colleague Bill told me that it was “time to have an idea.”

I had hired in as the first employee at a small consulting company in early summer. The founders had been handing me pre-specified projects for a few months. These early projects appeared on my desk ready-made, with the Statement Of Work (SOW) already written, the scope negotiated, and the customer interested mostly in when the resource (me) could be scheduled.

Now it was fall, and it was time for me step up my game and spec my own work. I realize now that they were tired of carrying me.

In the spirit of “learn by doing,” they dumped me on the phone with a prospective customer, the IT department for Stanford.

That, in itself, was an incredible opportunity.

Rookies look down on “sales.” I know now about the grinding work that leads to calls like that. The series of interactions with gatekeepers whose only options are to say “no” or else to continue the conversation. The people on the other end of this call could say “yes.”

Also, their “no” would end the conversation entirely.

At the time, I wasn’t even savvy enough to be nervous.

I know now that we practiced a variant of “spin” selling, which focuses on understanding the customer’s pain points as the first part of the conversation. It’s not “our floor cleaning machine is great,” but rather “do you have any irritation connected with dirty floors?” Our model was characterized by a triangle of needs, features, and benefits. If your offer (the features) addresses the customer’s needs, and if the benefits to them (the perceived value) are greater than the cost, the deal pretty much closes itself.

I was prepped with the need: Stanford had recently done an audit and determined that they employed more people in computer support roles outside of IT than within it. Further, they had found at least 20 instances of an on-campus closet with a ton or two of recently added cooling to support a feral compute environment.

IT needed to justify their continued investment in scientific computing. The user community was routing around them.

The conversation went back and forth for about 20 minutes, introducing ourselves, re-hashing the situation, doing the human part of the meeting. Somewhere around that 20 minute mark, Bill, my colleague / boss / and co-owner of the company popped into the group chat:

Time to have an idea, Dwan.

I was stumped. What did he mean?

Conversation continued, my teammates carrying me. Bill pinged again.

Dwan, write yourself a job.

So I went for it. Broke into the conversation and suggested that maybe it would help to have me … um … fly to California to spend a week with them? Yes. Having me onsite was totally part of it.

They were curious but unconvinced. What did I have in mind?

Maybe the need was that folks on campus were unaware of the resources available within central IT. So I would come out and give a series of talks on batch computing and how scientists might use the central IT compute cluster (the feature!). That would draw prospective users to the resources of central IT (the benefit!).

They dug it. There was a brief digression to fill in the details.

Bill texted again:

Keep going. There’s more. Go for it. You got this.

So I kept going. I suggested that I would also talk to the various user / stakeholders and ask them what they needed. With prompting from Bill, this turned into an offer to author a report describing the “capability gaps” between central IT’s offerings and the needs of the community. We would use my talks as bait to draw an audience with legitimate value, and leverage those connections to help central IT better align its services against its stakeholder needs.

Sorry for the consultant-speak. It’s what I do for a living.

On that call, it was enough. We got the work. I still sort of marvel that my words on that phone call created a trip to California.

As a mentor and friend would say about a different project, a decade later: “You spoke it into being.”

Knowing what I know now, I should have gone further. I could have helped more. My proposal was tactical rather than strategic. I should have offered to help with the root cause rather than just going after the symptoms. There should have been check-in and follow-up to make sure that I didn’t just drop a consultant report and leave, but instead fixed the problem for good.

How, exactly? Well that depends on a lot of other questions.

Did you have a “have an idea” moment?

If you’re further along in the career journey, can you give such a moment to a person on your team?

Climate change is real, human activity is driving it, it’s an emergency. We need to slam the brakes on carbon emissions right now to have any hope of a smaller scale catastrophe. That’s the choice. Doing nothing or putting it off is a crime against humanity and a moral failing.

Trump should be removed from office for high crimes and misdemeanors. Yes, other people have done bad things too. I’m focused on the current president of the US and his unprecedented blatant self-serving disregard for law, rule, and basic norms.

Abortion must remain safe, legal, and broadly accessible. Lots of reasons for that. I cannot believe we’re having to re-fight this.

Unrestrained capitalism, as practiced in the US, blocks meaningful access to medical care, housing, and education for enough people that it qualifies as a human rights violation.

No one person needs, deserves, or is ‘worth’ a billion dollars. We should stop idolizing these parasites and start taxing their wealth.

Churches and other religious institutions should be taxed and governed according to how they run their business. If they qualify as a nonprofit or charitable organization, great. If not, tax-em at the corporate rate.

Financial markets should run at a human tempo, not a machine tempo. Markets should close and settle one large batch of orders per day. We would keep all the benefits of publicly trading shares of large companies, and lose only the casino culture of the trader-bros.

Government has no business in the personal relationships between consenting adults. None, zero, zip. Love is never a crime, and marriage is available to all.

A universal basic income would eliminate, at a single stroke, many of the worst problems of poverty, homelessness, hunger, and so on.

Finally, and most important: A person’s responsibility to act scales with that person’s wealth and social power.

We are living through an uncomfortable period in the practice of medicine.

The dialogue between patient and physician is critically underserved, both in terms of tools for patients and physicians, and also in terms of the data context where that conversation takes place. This is unfortunate, because those are the moments of human to human care. Whether it’s a clinic visit, a lab test, a counseling or physical therapy session, the patient / provider meeting is when the full breadth of the caregiver’s experience and training can be brought to bear. At these moments, the subtle observations and pattern recognition that constitute diagnostic expertise come into play. These are are also the times when the nuance and detail of the patient’s lived experience can be shared to influence the course of diagnosis and treatment.

Population health turns into personal medicine at the bedside.

That conversation between patient and physician ought to be a first class citizen in terms of tool development, but it is not. It is within our reach to build a clinical care environment that retains high standards of data integrity and privacy while also focusing on empowering the human beings in the room rather the interests outside the door.

Due to the misaligned incentives that I’ve written about previously, the development of tools to support a data-rich conversation between patient and physician has generally taken a back-seat to software for billing, regulatory compliance, and mitigating risks to the care system. Recently, we have begun to instrument the clinic to support data gathering for research purposes. While this is a great idea on the face of it, it can have the unintended effect of leaving still less time for that critical conversation. Unless we can close the loop and bring the benefit of that instrumentation back to either physician or patient, it will be felt as friction, yet another loss.

I believe that we can have our data and do research on it too – and also that the clinical interaction is vastly more important than research use of the data we might gather along the way.

Research at no benefit to the participant

On the topic of research.

I’ve participated in a number of clinical research projects, mostly around genetics and genomics. The usual routine is to sit in a plastic chair and fill out a piece of paper using a pen tied to a clipboard. Some projects let me do the (still manual) data entry using a tablet. I used to gripe to the staff that this is a terrible, terrible way to gather data, but these days I just let them do their job and then blog or tweet about it later. The moment of truth comes with a needle stick, a swab, or a collection cup. Sometimes there are juice and cookies. Usually not.

Later, some anonymous lab will re-measure values that I’ve likely already got on my laptop. The math is churned for a few months, and perhaps somebody publishes. I usually won’t find out. I’ve stopped asking about that, because I’m bored with people who use HIPAA as an incantation to ward off further questions.

There are notable exceptions to this pattern of research’s stony indifference to the well being of the participants. The Coriell Personalized Medicine Collaborative stays in touch, nearly a decade after I spat in a tube for them. I get regular emails sharing the research results derived from my data. They also provide a crufty-but-effective web interface to allow me see curated and IRB approved subsets of my results along with risk scores and background reading. For all the well-deserved flak we give (and should continue to give) 23andme for selling our data to the highest pharmaceutical bidder without asking first – they too give me useful and regular value.

All of Us is saying the right words about citizen researchers and “partners rather than subjects,” but the proof will be in the pudding. Their involvement with the likes of Google leaves me a bit cold.

In nearly two decades of energetically engaged participation, I have yet to encounter even one research project that offered to close the loop on the data they collected by making it available to my physician in the context of my clinical care. Nearly two decades after we completed the Human Genome Project, this basic courtesy to research participants is still not on the menu.

We are left to fend for ourselves, to separate the useful offerings from the snake oil in the direct to consumer marketplace.

Personal Data

I’ve written, more than once, about my ongoing attempts to get out in front of the curve of personalized / precision medicine. I can see where we’re going, and I want to live there as soon as possible. Early 21st century medicine is, by and large, reactive. Nobody wants to hear, “I wish we had caught this earlier,” but that’s what you get when the protocol is to wait for visible symptoms before testing for disease. Risk officers exacerbate this by steering physicians away from data, citing the risk of incidental findings and HIPAA violations.

I’m still irked about the physician who tried to refuse to screen me for the colorectal cancer that killed my grandfather, despite genetic and symptomatic evidence that indicated that it might be worth an extra look.

In the future, patients will have conversations about their care in the context of a well structured repository of personal data. That data will come from multiple sources, most of them nonclinical. Our data will be available, with appropriate localization for education and language, directly to the patient. We will be able to share it with our in-home caregivers and with a care team that includes both physicians and other health and wellness professionals.

In the future, nobody will ask for my previous doctor’s FAX number.

Put another way, our physicians should have the same data-driven advantages that we already see in retail sales, in entertainment, and in finance. Our doctors should have the kind of integrated data that data monopolies like Google, Amazon, and Apple already use to influence everything from our buying to our voting.

Of course, that will require changes to – without exaggeration – nearly every aspect of the clinical data environment. We should start now if we want to see it in our lifetimes.

Mercury Retrograde

A company named Arivale has been a partner in my personal data journey for the last year. Through them, I could get clinical-grade laboratory bloodwork every six months. The Arivale dashboard showed me my data in context, along with information from my self-monitoring devices (pulse, weight, sleep, and steps per day), as well as notes from online self-evaluations and conversations with a “wellness coach.”

We were a year in, and it was just getting good when they shut down. They cited operational costs, implying that this sort of service is too expensive to provide – at just about any price. I wish I could see the math on that.

I have written before about my elevated mercury levels and how I was able to do a personal experiment to see whether changing my diet to omit fish rich in heavy metals would reduce them. Here’s a full year plot of the data. It worked.

Of course, over the same year, my cholesterol shot up. Here’s a graph of my LDL levels and particle count over the same period:

• 

• 

My first reaction to these plots was to ask “what changed?” One obvious thing that changed was my diet. I had mostly stopped eating mammals and birds around the year 2001. When I cut out mercury rich fish, I re-introduced a bit of red meat. On reflection, I was probably looking to replace the celebration meal-centerpieces that had formerly involved high-on-the-food-chain fish. Also, a slow-cooker roast on a Sunday is pretty wonderful.

The experiment over the next six months will be to dial back down on the red meat and see what happens to the cholesterol. My other grandfather died of heart disease. It’s something I keep an eye on.

Presentation

When I showed these plots to an experienced computational biologist whose PhD includes the word “statistics,” she had a strong reaction. To paraphrase: “What are they thinking, drawing straight lines between those points? That’s incredibly misleading. You got tested three times in a year. Three. This plot gives no insight into the underlying biological variability or the accuracy of the test! This is a gross oversimplification!”

I tried to make a case that the simple picture was accessible enough to spark curiosity and bring a novice like me into a data driven conversation. I told a story about different visualizations that would be suitable for everybody, including patients, data scientists, and also clinicians, all rendered based the same underlying data. She was unimpressed: “It doesn’t matter which of those categories of person we’re talking about, this plot would be misleading to all of them.”

I trust my statistician friend, and I can see the importance of making sure that the data presentation is as accurate as possible. I’m bummed out that I didn’t get to write the feature-request note to Arivale.

The clinic of the future

I will end on a hopeful note: I recently had the opportunity to visit a clinic from the future.

When you walk into Lab100 at the Mt Sinai School of Medicine, it feels more like an Apple store than a medical establishment. Everything is smooth curves, laminate, and frosted glass. Even though the data that they gather is more accurate, better calibrated, and more natively digital (no manual data entry here). The experience is also more personal and human than I’ve previously experienced in a clinical context.

You know how the restaurants and vendors at Disney resorts already know your preferences before you speak up? Imagine that but at the doctor’s office or in the hospital.

A visit to Lab100 begins by sitting down with your caregiver, side by side on a couch. You and the clinician talk while looking at the same pane of glass, a large flat-panel display that shows your medical history and current complaints. Instead of being separated by technology – the flat panel monitor between me and my doctor – here technology brings people together to facilitate that all-important doctor / patient conversation.

The beginning of the visit is a review of your chart to make sure that it’s accurate, complete, and relevant. You move through stations to measure blood chemistry, balance, cognitive function, grip strength, and more. At each station there are video presentations explaining what is being done and why. Your results show up on the screen immediately, including a longitudinal view of how you tested before.

At the end, there is another sofa and an even larger screen where you see yourself in context. Your data is shown along with a cohort of other real people, matched to you by gender and age. Then you and the provider talk and make a plan together.

It’s compelling. I hope that the idea takes off.

It felt like rich people medicine, but the founders of the lab assured me that it is built out of commodity components and designed to be replicated without undue expense. In 2019, the Apple aesthetic is certainly high-end, but for all that, there is an Apple store in every major city in the country. It is apparently possible to have that rich-people feeling while still keeping the coss to shopping mall levels – provided long as you’re selling consumer electronics and not health care.

Lab100 and whoever follows in Arivale’s footsteps are not the whole picture. There is a lot of work still to be done, and many entrenched interests to be appeased. We’ve spent decades building and empire tuned for billing, risk mitigation, compliance, and a weird and stilted flavor of data privacy. It’s going to take years to dig out of this hole.

For all that, the path is clear: Radically empower patients with access and control over their data, and make the physician/patient conversation a 1st class citizen in terms of tool development.

Let’s get on with it.