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Sep 15, 2026
Borrowed Certainty
Borrowed Certainty
00:00
24:42
Transcript
0:00
[gentle music] This is Harvest the Signal, where the smallest signals can change the biggest systems. And now, here's your host, Jodi Lynn Owen. What is the system trying to teach us?
0:17
That's the question behind every field note, and this one is Borrowed Certainty. I'm Jodi Lynn Owen, and this is Harvest the Signal. Welcome in.
0:30
I am going to start with a story, but it's a story about my own little LinkedIn journey recently because I followed a link in the comments section of a very popular LinkedIn post recently.
0:44
I love reading all of the replies to those posts, and I learn so much and get to learn about new people to follow. It's great.
0:52
Anyway, this one, it led to a maternal health technology company doing work I found genuinely interesting, the kind of innovation I really want to see succeed.
1:04
It is technology with real potential to improve access and expand especially what smaller independent and rural practices are able to offer.
1:14
So I went to the website, and I read about the technology, and they have a blog section, so I started reading the blog. One of the posts referenced recent research on gestational diabetes.
1:26
Something about the way the findings were described caught the corner of my eye kind of attention. So I clicked the reference that was listed there, and it wasn't referring to the actual research.
1:40
It was an article about the research.
1:43
So I went and found the original study and looked it up, and then I put all three next to each other, the research, the article reporting on the research, and then this health tech company's blog reporting on the article reporting on the research.
1:59
The distance between the first and the third was remarkable. Demographic terminology had changed. Findings had acquired explanations.
2:10
Things the researchers had been careful not to conclude were now stated with more certainty than they'd earned.
2:18
By the time the research reached the company blog, some of what I was reading was no longer what the original researchers had actually said at all.
2:28
No single leap was enormous, and that might be the most important part here. The information simply drifted.
2:36
And I would say this is one of the most apparent times where that word has become reality in something I'm looking at. It just drifted.
2:49
And we know most likely this is the result of AI writing kind of unchecked by human eyes and a human brain. But I messaged the company directly. I don't need to name them here.
3:00
I believe in the idea of what they're building, and I really do hope they succeed. And this isn't even really about them so much. So the problem isn't that the need isn't real. Maternal health disparities are real.
3:16
Access problems are real. Although how we define access is really worth digging into. And I had a lot of thoughts around access, and I broke it into these two episodes, eleven and twelve, of the field notes.
3:32
So if you haven't heard it, go back and listen to eleven on Access Has a Backdoor. And there you'll catch a bunch of my thoughts about that. So independent practices need better technology.
3:47
Our maternal health infrastructure has enormous gaps, and there are companies building things that may genuinely, I think will genuinely improve care.
3:55
So does it matter really if somewhere between the research paper and the investor deck the language gets a little loose? I think it does. A researcher spends years asking one particular question.
4:10
The research team defines the population, chooses categories deliberately, designs a methodology capable of answering some questions and incapable of answering others.
4:22
They distinguish what the data demonstrates from what might explain it, and they always name the limitations. Those distinctions are not academic clutter. They are the work of it. Then the research begins to travel.
4:39
It becomes an article, and the article becomes a blog post. The blog post becomes a statistic in a pitch deck. The statistic becomes a sentence on a website.
4:52
Eventually, it becomes something a founder says to an investor, or an investor repeats to another investor, or a health system uses to explain why a particular intervention is necessary. That's a long cascade.
5:08
And somewhere along the way, an association became an explanation. A demographic category gets renamed entirely. A limitation disappears.
5:19
A careful we don't yet know why becomes a confident paragraph about why. A real problem is good cover for an imprecise claim.
5:33
And nobody, including me, although I'm doing it right now, wants to be the one who interrupts a good cause to ask an inconvenient question.
5:41
And because the underlying problem is real, almost nobody stops to ask whether the sentence is.
5:49
So healthcare has developed a vocabulary that signals importance on its own, all by itself, health equity, disparities, social drivers or determinants of health. Underserved communities, access, maternal mortality.
6:05
These are not marketing terms. They carry weight because they describe real conditions affecting real people.
6:13
And this vocabulary has been built by generations of clinicians, researchers, epidemiologists, public health workers, community organizations, and patients who did the work required for us to understand what they mean.
6:29
I'm going to insert here that just because these vocabulary terms have been established over such a long time by such a diverse group of stakeholders doesn't mean that they should always stay what they were.
6:44
But I would expect those stakeholders to evolve the definition of these terms to meet the needs of the day. I would not expect the terms to be interchanged by a health tech company
7:01
or by a venture capital company promoting its holdings.
7:08
Now, increasingly, I see all of this language, and this is, you know, health equity, disparities, social drivers, underserved, access, maternal mortality.
7:21
I see those wrapped around healthcare products as evidence that the product itself matters, and sometimes it absolutely does, and that is what makes this uncomfortable.
7:34
I'm not interested at all in catching a startup using the wrong terminology in a blog post.
7:40
I'm interested in what happens when technical language starts functioning as borrowed authority, when the words themselves become part of the sales architecture. The product solves an equity problem.
7:53
The platform addresses disparities. The technology expands access. The solution serves underserved communities. Maybe. But those are claims. They're not decorations.
8:07
And if we're building companies on top of them, we should be willing to do the slower work of finding out what's actually true. There's another part of this that bothers me.
8:19
Much of the technology being built to transform healthcare is still designed inside the same paradigm that built healthcare in the first place. The patient's first need is not always the system's first priority.
8:33
We've all had experiences with that. Healthcare organizations have financial requirements. Technology companies need growth. Founders need capital. Investors need return. Health systems need efficiency.
8:47
Payers need cost containment. And none of that is inherently wrong. But none of it is the same thing as what the patient needs.
8:55
When we fail to distinguish between them, it becomes surprisingly easy to take the language of patient need and use it to advance something else: a market, an investment thesis, a product, a cause, even a good cause.
9:11
And this is where integrity becomes more interesting than accuracy. Accuracy asks, "Is this statistic correct?" Integrity asks, "What am I asking this statistic to do?" Accuracy protects the sentence.
9:28
Integrity protects the person the sentence is about. We are laser-focused right now on bringing technology and efficiency to healthcare, and healthcare deserves the benefits of both.
9:42
But healthcare itself is neither fundamentally technological nor particularly efficient. At its center is one human being trying to understand what is happening inside another human being and decide what to do next.
9:58
Everything we build exists around that encounter. And there is something predictable about what happens when we bring an efficiency mindset to a system this complicated.
10:10
We optimize the parts that are easiest to optimize. In this case, information became content. Research became a summary. A summary became a prompt. A prompt became a piece of thought leadership distributed on a blog.
10:26
The distance between evidence and assertion is becoming friction we are increasingly capable of removing. But some friction is really protective. Checking the primary source is friction.
10:39
Understanding why researchers chose one demographic category instead of another, friction. Reading the limitation section is friction.
10:48
Recognizing that a study demonstrates a disparity but doesn't explain its cause is friction. Saying we don't know is a friction.
10:57
And increasingly, those may be exactly the parts of the process we cannot afford to optimize away. AI makes this considerably harder.
11:09
We can now turn a research paper into a blog post, a sales narrative, a LinkedIn post, a white paper, an investor slide, and a patient education piece in less time than it takes to run a dishwasher load.
11:22
The cost of sounding authoritative is approaching zero. The cost of knowing whether what we're saying is true has not changed.
11:32
And maybe it's gone up because the volume of information we can produce has exploded while the human work required to interrogate it has stayed deeply and stubbornly human.
11:46
Our human responsibility, someone still has to click the citation. Someone still has to read the study. Someone still has to notice that the population changed between paragraph one and paragraph three.
11:59
Someone still has to ask, "Did the researchers actually conclude this, or is the feedback I'm getting from my AI agent reinforcing what I want to write about?" Fast is not the same as found.
12:14
Technology can help us find the signal. It cannot give us permission to stop caring whether the signal is true. I keep coming back to the company whose blog sent me down this path.
12:27
I didn't write to them because I wanted to catch them doing something wrong. I wrote because I want the work to be good. I believe the technology they're building matters. I believe it can increase access.
12:41
I believe technology like it can expand what smaller and rural practices are able to provide. And I believe that makes fidelity to the evidence more important, not less.
12:54
We don't have to exaggerate maternal health disparities to make them consequential. We don't have to assign causes a study did not establish to make access worth improving.
13:06
We don't have to borrow the authority of researchers while leaving behind the precision that made their work authoritative in the first place.
13:16
Perhaps one of the disciplines we need most in this area of extraordinary technological acceleration is an old and decidedly inefficient one. Go back to the source. Read what it actually says.
13:28
Know the difference between the evidence, your interpretation of evidence, and the story you need the evidence to tell.
13:34
This is the responsibility of builders, funders, consumers, and those of us adjacent to the product but in that space.
13:42
And here I'll add that maybe this is a really positive future role for humans and companies as AI changes all of our roles.
13:51
People who come out of academics or who are in the world of academics would make excellent employees at all of these levels, at a venture capital fund, alongside an angel investor, inside the build, so that you have somebody there who is confirming the research and translating it into the language that you're using to describe what you're vested in.
14:17
Truth doesn't become less important because the thing we're trying to build might help. It becomes more important. And here's one more signal.
14:27
After I wrote this, I wanted to just give it a couple of days to see if the people I contacted from the respected publication and the VC-backed company would respond.
14:38
I normally wouldn't even bother to share this, but here we are. Since I tried to break my current thinking about access into two episodes, of course, like just of course, it keeps showing up everywhere.
14:49
The trail that led me here had started with a maternal health tech company.
14:53
And when it took me to the research synthesis article in this respected clinical publication, as I was reading it, I saw a couple of weird, small grammar errors.
15:05
It was like spelling and grammar errors, really, really basic, in the published article. And this is very unusual for a publication like this.
15:13
So I sent a private note to the editor just quickly like, "Hey, I found these two things in this article, and there's a popular site sending readers here, so I thought you would want to know."
15:23
And within fifteen minutes, I had a reply. "Thank you. This happened under a previous editor. I'm fixing it now." And then he did. So okay, no news is no news.
15:32
But I also reached out to the person whose company had written about that research on their blog. My concerns there were more substantive because the company's blog had shifted demographic language.
15:43
They actually assigned the wrong populations to the study.
15:47
They attributed findings to the research that were not actually there and blurred some important distinctions between what the evidence showed and the story being told about it in service of promoting a remote patient monitoring technology.
16:02
So this is where this all came from. Now you see where it all came from. I wrote essentially really the same kind of note. This work matters.
16:09
The technology is crucial to the safety of mothers and babies, and I'm supportive of your work, and I want it to get there.
16:16
And the standards for communicating the evidence should be as rigorous as the standards for building the product. I never heard back.
16:24
And I keep seeing links to this company being dropped into the conversations about maternal health disparities on LinkedIn. That bothers me, not because someone didn't answer my email. I don't care about that.
16:38
But the blog added on to what is already true about maternal health, and maternal health does not require embellishment. The signal is already deafening. I was listening to a podcast recently. I'm so sorry.
16:53
My dog is snoring. If you're still listening, you get me and my dog snoring. So this podcast had an interview going on with a VC executive, and he was describing a story that had really, like, profoundly affected him.
17:06
And the story is that an EMS team arrives to help a woman giving birth at home. She spoke Swahili. They spoke English.
17:14
And somebody opened Google Translate, and through a phone and through Google Translate, they managed to communicate with one another and safely navigate the birth. If you've ever used this, you know what it is.
17:25
You can switch quickly back and forth between English and the other language, and you can either talk into it or type into it and just give your phone to the other person, and you can hand that phone back and forth to communicate.
17:37
And his voice was cracking telling the story. I think he was in tears. He was so moved by how tech had leveled up this healthcare experience and kept a mother and baby and the EMS team safe.
17:52
I do not want to diminish that.
17:55
In fact, being moved by another person's story is one of the ways we begin to see problems that we haven't had to live ourselves, and we really begin to understand them, and you could hear that in his voice.
18:06
But I was struck by something else because what sounded extraordinary in that story was for much of my clinical career and is for so many independent women's health providers absolutely normal day-to-day ops.
18:21
One clinic day can mean navigating a half a dozen different languages.
18:26
Interpretation under the best of circumstances turns a twenty-minute encounter into a forty-minute one, and normally it stretches even longer than that.
18:36
Professional interpretation services are not covered by insurance and rarely covered by Medicaid, and they are extraordinarily expensive for a small practice to bear.
18:46
The cost of one hour far exceeds the reimbursement for that entire visit.
18:52
And the independent clinics serving communities with the greatest language diversity are often the organizations with the fewest resources available to absorb either the additional time or the additional cost.
19:06
There was no need to search for an edge case. That particular problem is sitting in waiting rooms and birth suites across the nation.
19:14
In fact, this morning, The Boston Globe just published an article about the abhorrent time it takes to move from the emergency department to a bed in a unit with the specialist you need inside hospitals.
19:27
I saw nurses commenting on LinkedIn about this article that the language barrier creates its own unique bottleneck within that already bottlenecked patient journey,
19:39
even in that much better resource system than the one I'm referring to.
19:44
This idea that in maternal health we do not have to generalize or exaggerate or wonder how to identify a problem and how to name it and name how big it is, is the part I want investors, philanthropists, health systems, and anyone funding the next generation of maternal health technology to hear.
20:06
And I am trying to make a plea to you, okay? So here's what I want you to do. Push harder on the story. When someone shows you a disparity, ask where the data came from.
20:18
When a company tells you its technology addresses an outcome, ask whether that outcome was actually measured and where and with whom.
20:27
When an extraordinary anecdote demonstrates the need for a solution, ask the people doing this work every day whether the anecdote is extraordinary at all. That may sound difficult, but it isn't.
20:41
Ask the founders and designers you are funding to make those introductions for you. Take the time to hear from the people on the ground. It will make you better at talking about your investments.
20:55
And that matters because you are in rooms all of the time that those providers may never be invited into. You will have access to people they may never meet.
21:06
What you learn from them can shape how you use that access to shift policy, influence thinking, and bring attention to problems much larger than any single product can solve. I guess what I'm asking is this.
21:21
Take seriously the acquisition of knowledge and take equally seriously your responsibility for what you do with it. I actually hear so much in the VC world that it's just this predatory place.
21:37
But when I meet the people behind these companies, people who have built enormous VC-based companies, people who hold the capital for those companies, I meet really good people who really want to do good in the world.
21:54
And this is one of those places where you have influence that is unique to your positionality. You don't need to make maternal healthcare's problems bigger to make the opportunity compelling. They are big enough.
22:10
You don't need to borrow certainty, and you don't need to go fishing for dramatic examples of inequity when clinicians and patients are navigating them every day.
22:21
And those stories will also have you in tears, as they should. And they will teach you how it feels to be inside the system experiencing the problems you are considering investing your dollars in.
22:34
This is a small investment of your time that I suspect will have a lot of very important ripples. We need innovation desperately.
22:43
But if we're going to build technology in response to maternal health disparities, then intellectual integrity isn't an academic nicety. It's part of the infrastructure.
22:55
Because eventually that story becomes a pitch, and the pitch becomes an investment, the investment becomes a product.
23:03
And that product eventually lands in front of a patient or a policymaker or a healthcare executive itching to say yes.
23:11
We owe them a straight line between the problem we said we were solving and the evidence that told us it was there.
23:17
This is where I encourage you to noodle through these ideas while you are walking the dog or I don't know if you're hanging out and he's just snoring really loudly next to you like they like to [chuckles] or if you're driving home.
23:29
If today's field note changed the way you think about how we use evidence, decide which healthcare problems are worth solving or design technology around all of the people it's meant to serve, please let me know.
23:42
I do read every reply personally. In fact, the next Harvest the Signal may begin with something you've stopped accepting but haven't yet put into words.
23:51
You can connect with me on LinkedIn by my name, Jodi Lynn Owen, or on Threads at Harvest the Signal, and let's keep this conversation moving.
24:00
And speaking of moving, I would love to be the voice in your ear while you get where you are going. We can explore patterns hiding in plain sight together. So please hit subscribe, and we'll do it again.
24:11
I'm Jodi Lynn Owen. Until next time, keep looking for the signal. This has been Harvest the Signal with Jodi Lynn Owen. The smallest signals can change the biggest systems.
24:22
Be sure to like, follow, and share so you never miss an episode or the opportunity to compare your field notes with your colleagues.
24:31
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