musingsonwomenshealth.com

Reflections on 40 years as a doctor in Women's Health

Some must watch, while some must sleep


I’ve been retired from active medical practice for a few years now; I can’t say I miss the hours or the stress, but what I did enjoy, however, were the patients and the adventure of solving their problems. It was always a journey, and like visiting a city for the first time, there was a feeling of heightened awareness when everything seemed new and clues were scattered like unusual architecture that surfaced if you paid attention. I do miss that.

And sometimes it was not so much the pattern I was reading, as the sense that something was amiss -something in the story the person told, say, or the way they looked across the room when certain questions were asked. It was the sense of engagement or of fear, that often tipped the scales as a clue. People are not patterns so much as aggregations of smaller things, and unless doctors are attuned to the circumstance, the problem may stay hidden. Disguised.

But life is often like that isn’t it? So I suppose it was only natural for us to turn to Artificial Intelligence and it’s algorithmic lists for help. And as long as we have managed to include the appropriate things for it to consider, it might certainly be beneficial. But sometimes what is really important is only revealed by context and that, of course, will vary with the situation; it may not be predictable beforehand.

Still, I can’t help but wonder if this little Jeremiad is just sour grapes, the sotto voce ramblings of an old man who wonders why the way he approached things in the past suddenly needs improving -needs the help of AI. Equally, though, it could be just the desire for a quick solution to things that are more complicated than they initially appear -or worse, multifaceted, and the algorithm merely attends to one of the more obvious facets, leaving the rest to fester.

No matter how many data points are fed into a computation, it still requires a degree of experience -or wisdom, perhaps- to know which ones are important, and which are just random noise.

I was struck by the example of mushroom foragers given in an essay co-written by Anna Harris, an anthropologist of medicine at Maastricht University, and Lisa Herzog, a political philosopher at the University of Groningen:  https://psyche.co/ideas/what-might-mushroom-hunters-teach-the-doctors-of-tomorrow

While seemingly unrelated to medicine, the mushroom gatherers and doctors alike, benefit from being hyperaware of the circumstances and weltanschauung of their subject matter. For doctors, it is both the emotions and lived-experiences of their patients, for the foragers, the ecology and physical properties of the mushrooms. The observational skills of both, are crucial to the success of their missions. ‘Whether in the forest or in the clinic, noticing means a full sensory engagement with the sounds, images, feeling and general atmosphere of an encounter. It also means paying attention to, and trying to make sense of, what is not typical, capturing what might at first seem to be inconsequential details. These skills differ markedly from algorithmic pattern recognition.’ 

This is not to detract from the value of algorithms -they can spot patterns and often ‘create predictive models linking symptoms with associated diagnoses, which are used to analyse fresh cases. The process lets software ‘see’ things that humans can miss, limited as we are by assumptions, biases and restricted processing power.’ But they can miss things, too. As the economist Peter Spiegler writes in his 2015 book Behind the Model, ‘From a model’s perspective, its objects of analysis are ‘stable, modular, and quantitative, with no qualitative differences among instantiations of each type’… But the world isn’t always like that, and so it’s easy for models to lose touch with the reality they’re supposed to describe.’

And, ‘Even when algorithms are ‘free’ to pick up their own patterns in the data, humans still need to make numerous decisions: which data sets to use, how to curate them, which quality standards to put in place, how to measure whether or not there’s a ‘fit’ between model and reality. For this, the software’s developers must grasp the context and meaning of the data; they need to understand what’s ‘normal’ and what’s a deviation. In short, they need to decide what matters.’ That’s the human task; and that, in coordination with their patient, is the doctor’s task.

*

“I shouldn’t have asked anything,” my friend confessed to me on the phone yesterday. She sounded worried.

“Asked who, what?” Was all I could say in reply. It’s sometimes hard to pick up context over the phone.

“About my dizziness,” was all she replied, so I waited for her to continue. But she didn’t; I suppose she thought I’d know who she’d asked, for some reason.

I took a deep breath and asked the question differently this time. “And what did she say about it?” It seemed like a reasonable follow-up to ask what her GP had diagnosed.

There was a pause, as if she was thinking about it. “I don’t know if it was a ‘she’…” I could almost hear her mumbling to herself. “More likely an ‘it’…”

“Uhmm…” It’s hard to carry on a conversation with Janet sometimes -she thinks whatever’s turning over in her head is doing the same in mine.

“Oh for goodness sakes, G, I Googled it.”

I rolled my eyes; she couldn’t have seen them, obviously, but Janet knows me too well.

“You don’t have to roll your eyes each time I do something you don’t approve of, you know…”

“Janet,” I said in my most patient voice, “Just tell me what advice you managed to get.”

I could hear her sighing even over the phone -I think it’s the way she holds it, or something.

“Well, it came up with a whole bunch of possibilities…” She paused for dramatic effect. “But it told me that since I’m a woman in my sixties, I really should see a doctor about them.” A short, meaningful pause. “That’s why I phoned you.”

I sighed quite loudly into the receiver to let her know it wasn’t the same thing. “Janet, I was an obstetrician, for goodness sakes -and the dizzy-part is miles away from the district where I used to work… And anyway, I’ve been retired for years now…” I thought I should remind her.

She ignored my warning. “So what do you think? High blood pressure? A mini-stroke? A brain tumour…?” She waited for a second or two before she added “And it only occurs if I get out of bed too quickly… And only sometimes… No headaches, I feel well… And it was only a few months ago that my usual GP -who’s now retired, by the way- said I was in great shape! I mean, you and I still go for runs, right? Can’t be that bad…” Janet likes to cover all her bases before I can ask anything.

“Go see a real doctor, Janet… Just to be safe.”

“You don’t sound like you’re worried, G.”

“I’m not…”

She sighed into the phone again. “I always say: talk to someone who really knows you, eh?  We still on for tomorrow morning?”

“The run, you mean?”

“Of course, G -wouldn’t miss it for the world.”

“I’ll meet you on the usual corner at 9:30.”

She giggled and then ended the call. For some reason, I just wasn’t worried about her…

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gozzter

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