This essay made me reflect on how often we are treated as averages, not individuals. I’m curious: what’s one moment when you realised you weren’t the ‘typical case’ the system assumed you were?
My husband - he had his first group of heart attacks in 2009, blood tests revealed hidden MGUS syndrome as well. Oh, and since that first MI he's had elevated Troponin levels. Every medical incident (many), illness, drug prescription, since then has confirmed he is Atypical. His physiology presents/reacts/responds differently. We have to make medical professionals aware of this all the time.
Bec, what you describe is exactly the burden families carry when someone’s physiology refuses to fit the template. You end up doing the pattern recognition the system hasn’t built for. The emotional and cognitive load of repeatedly ‘reminding’ clinicians shouldn’t fall on you, yet it so often does.
If you’re open to sharing, I’m curious. Has any clinician ever acknowledged his atypical pattern early on, rather than after the fact? I’m always trying to understand what makes those rare moments of recognition possible.
It's usually us that point it out. It's tricky though because he's seen so many different people. Even his GP; although he's awesome, multiple qualifications, and *listens* to our concerns, there's so much to keep track of. Actually come to think of it, this year! Husband had two major MIs then a cardiac arrest - the original cardiologist who was overseeing his case in 2009-11 is still head of Dept at local public Hospital. I think he took on Project Management for husband's treatment because he knew him/us/history.
I’ll assert that precision fitness will become a thing too. Love this discussion about the exceptions to the norms—you write so eloquently about the gender gap in research!
I agree. Precision fitness is coming, and sooner than people think. Once we stop treating the body as ‘standard issue,’ the entire landscape shifts. Thank you for seeing the gender-research angle so clearly. It really is the heart of why these exceptions matter.
This sounds like the ecological fallacy. https://en.wikipedia.org/wiki/Ecological_fallacy. AI models applied to individuals is the latest example of this dangerous logical/statistical error, one that doctors (and social scientists) make all the time. Thank you for highlighting this problem. I love your work.
Yes, exactly. The ecological fallacy is the perfect framing here. We forget how often systems lean on population logic that collapses the individual. What worries me is how invisible that collapse becomes once AI automates it. Thank you for bringing this lens in. It’s such an important part of the conversation
The old med school adage "When you hear hoofbeats, think horses, not zebras," too often translates to "Look for conditions common among young white males." Old women like me are all zebras.
Exactly. You are naming the deeper truth here. When the baseline patient is defined so narrowly, whole groups end up treated as outliers. That adage says it all!
When I was a little doctor I remember one of the attendings saying that the pediatricians in the group understood how to actually dose medication since we do it by weight until they reach an "adult" dose. A tiny elderly woman is not a 30 year old male weight lifter! And a tiny elderly woman is also not the same as a tiny elderly man! Well done.
This sounds like what Nassim Taleb calls the fat tail problem, in which the mean is close to the fat tail, which leads to underestimating risk (because until the fat tail happens you don't know the mean). Taleb's solution is "skin in the game", so that decision makers suffer costs when risk is underestimated. The more general principle is antifragility, in which contrary information makes the system stronger. In the case of the medical system, we need to address blind spots resulting from hierarchy.
Love this Taleb connection, Chris. What strikes me is that in medicine the “skin in the game” is almost entirely borne by the patient, especially those in the fat tail whose data were never centred in the first place which means the system can stay fragile for a very long time. The real test for AI in health will be whether we design it to learn from those tails (at all), rather than treating them as noise.
The “general rule” has failed me over and over and over in the medical treatment of my life with migraine. I have tried so many treatments that even the neurologist at Mayo started our consultation with “You’ve tried everything. I’m not sure how I can help you.” Specifically, I am extremely sensitive to medications. Over the years, I have tried explaining this when seeing a new doctor. I would ask to start with a lower dose with any new medication, only to be told either that the dose they’re prescribing is already a low dose or to just be dismissed. Only to end up having the doctor later admit, yeah we should have started with a lower dose, or even act like I should have told them as if I didn’t already.
Kristin, thank you for sharing this. It captures exactly what I meant by ‘the average patient’ being a myth. Migraine care already pushes people into the margins, and to then have your sensitivity dismissed on top of that is exhausting. I’m so grateful you added this here!
Reading this article back, I keep coming back to one question: where in your own life have you noticed a moment when ‘the general rule’ failed to capture your reality? I’d love to hear the exceptions you've lived.
I couldn’t agree more with your piece, Maryann! I strongly agree that precision doesn’t come from data at scale; it comes from data that’s the right shape, and that shape is always individual when it comes to health. That’s exactly what I’m working on with my Swiss-based start-up, Fig: a system that doesn’t start from population data or inherited assumptions, but from the person in front of it. Fig builds an individual baseline first (your patterns, variations and responses) and only then makes meaning. No more forcing people into generalisations that were never designed for them. Thank you for all your amazing insights; I'm truly enjoying your newsletter.
This essay made me reflect on how often we are treated as averages, not individuals. I’m curious: what’s one moment when you realised you weren’t the ‘typical case’ the system assumed you were?
My husband - he had his first group of heart attacks in 2009, blood tests revealed hidden MGUS syndrome as well. Oh, and since that first MI he's had elevated Troponin levels. Every medical incident (many), illness, drug prescription, since then has confirmed he is Atypical. His physiology presents/reacts/responds differently. We have to make medical professionals aware of this all the time.
Bec, what you describe is exactly the burden families carry when someone’s physiology refuses to fit the template. You end up doing the pattern recognition the system hasn’t built for. The emotional and cognitive load of repeatedly ‘reminding’ clinicians shouldn’t fall on you, yet it so often does.
If you’re open to sharing, I’m curious. Has any clinician ever acknowledged his atypical pattern early on, rather than after the fact? I’m always trying to understand what makes those rare moments of recognition possible.
It's usually us that point it out. It's tricky though because he's seen so many different people. Even his GP; although he's awesome, multiple qualifications, and *listens* to our concerns, there's so much to keep track of. Actually come to think of it, this year! Husband had two major MIs then a cardiac arrest - the original cardiologist who was overseeing his case in 2009-11 is still head of Dept at local public Hospital. I think he took on Project Management for husband's treatment because he knew him/us/history.
Another point: I'm usually proactive in detailing history & issues because there *IS* so much. Most clinicians are amazed he's still alive.
I’ll assert that precision fitness will become a thing too. Love this discussion about the exceptions to the norms—you write so eloquently about the gender gap in research!
I agree. Precision fitness is coming, and sooner than people think. Once we stop treating the body as ‘standard issue,’ the entire landscape shifts. Thank you for seeing the gender-research angle so clearly. It really is the heart of why these exceptions matter.
This sounds like the ecological fallacy. https://en.wikipedia.org/wiki/Ecological_fallacy. AI models applied to individuals is the latest example of this dangerous logical/statistical error, one that doctors (and social scientists) make all the time. Thank you for highlighting this problem. I love your work.
Yes, exactly. The ecological fallacy is the perfect framing here. We forget how often systems lean on population logic that collapses the individual. What worries me is how invisible that collapse becomes once AI automates it. Thank you for bringing this lens in. It’s such an important part of the conversation
The old med school adage "When you hear hoofbeats, think horses, not zebras," too often translates to "Look for conditions common among young white males." Old women like me are all zebras.
Exactly. You are naming the deeper truth here. When the baseline patient is defined so narrowly, whole groups end up treated as outliers. That adage says it all!
When I was a little doctor I remember one of the attendings saying that the pediatricians in the group understood how to actually dose medication since we do it by weight until they reach an "adult" dose. A tiny elderly woman is not a 30 year old male weight lifter! And a tiny elderly woman is also not the same as a tiny elderly man! Well done.
This sounds like what Nassim Taleb calls the fat tail problem, in which the mean is close to the fat tail, which leads to underestimating risk (because until the fat tail happens you don't know the mean). Taleb's solution is "skin in the game", so that decision makers suffer costs when risk is underestimated. The more general principle is antifragility, in which contrary information makes the system stronger. In the case of the medical system, we need to address blind spots resulting from hierarchy.
Love this Taleb connection, Chris. What strikes me is that in medicine the “skin in the game” is almost entirely borne by the patient, especially those in the fat tail whose data were never centred in the first place which means the system can stay fragile for a very long time. The real test for AI in health will be whether we design it to learn from those tails (at all), rather than treating them as noise.
The “general rule” has failed me over and over and over in the medical treatment of my life with migraine. I have tried so many treatments that even the neurologist at Mayo started our consultation with “You’ve tried everything. I’m not sure how I can help you.” Specifically, I am extremely sensitive to medications. Over the years, I have tried explaining this when seeing a new doctor. I would ask to start with a lower dose with any new medication, only to be told either that the dose they’re prescribing is already a low dose or to just be dismissed. Only to end up having the doctor later admit, yeah we should have started with a lower dose, or even act like I should have told them as if I didn’t already.
Kristin, thank you for sharing this. It captures exactly what I meant by ‘the average patient’ being a myth. Migraine care already pushes people into the margins, and to then have your sensitivity dismissed on top of that is exhausting. I’m so grateful you added this here!
Reading this article back, I keep coming back to one question: where in your own life have you noticed a moment when ‘the general rule’ failed to capture your reality? I’d love to hear the exceptions you've lived.
*This is the same mistake medicine makes when it assumes uniform metabolism*
Reductive design enthusiasts squirm if you suggest designing for metabolics.
The debate around what they think is a "Control" is very telling.
I couldn’t agree more with your piece, Maryann! I strongly agree that precision doesn’t come from data at scale; it comes from data that’s the right shape, and that shape is always individual when it comes to health. That’s exactly what I’m working on with my Swiss-based start-up, Fig: a system that doesn’t start from population data or inherited assumptions, but from the person in front of it. Fig builds an individual baseline first (your patterns, variations and responses) and only then makes meaning. No more forcing people into generalisations that were never designed for them. Thank you for all your amazing insights; I'm truly enjoying your newsletter.