Let us reflect that medical practice is one of the textbook examples for people making bad decisions due to Simpson's paradox. That the sheer number of highly opinionated people who are confident and wrong is staggering (take nutritional information, for example). That the risk of being embroiled in a good game of political or legal football is high.
On top of that, this is a field that historically has had poor exposure to good engineering practice or mathematics. A field where a researcher's have both the intelligence and lack of exposure to plausibly rediscover integration without being clued in by anyone in the know [1].
There is almost certainly more to be gained by working with evidence and solid statistical practice than there is by gut and guessing. It is hard enough just keeping everyone focused on the measurable evidence and weeding out stats mistakes without pretending that doctors should somehow diagnose based on their 6th sense. That isn't a healthy expectation for any party, including the doctor.
Efforts should be made to figure out what variables they are actually using to make decisions, if those variables are actually useful predictors and then how to systematise them. It is tautologically impossible that an experienced clinicians is detecting undetectable variables in their decision making process.
"the risk of being embroiled in a good game of ... legal football is high."
That's my personal experience as a parent, unless the pediatrician can absolutely rule out all possibility of any theoretical disease then all conversations with the pediatrician's office always end with an official recommendation to CYA by spending a day at the ER.
Two anecdotal heat exhaustion cases:
Me, like a quarter century ago, in the Army after setting a new personal record on a daytime hot summer windless humid PT test while stationed temporarily in the deep deep south; typical heat exhaustion, no energy, puke, dizzy, gray world, squadmates turned me in for "wobbling on my feet too much". PS and medic look at me, "well, you're still sweating, so ..." dump a canteen of water on my head and torso, force me to drink a canteen of water, wait to see I don't puke for a couple minutes, escorted by another soldier to sit in front of an air conditioner with him ordered to watch me drink another canteen of water for an hour. Treatment cost approx five minutes of medic labor and one hour of chaperone solder labor to observe me while I recovered, and three canteens of drinking water.
Preteen daughter gets heat exhaustion on the hottest most humid day of the year after a long day working hard outdoors despite being well hydrated, no energy, puke, dizzy, but still conscious and sweating, wife gets nervous resulting in call to pediatrician resulting in spending rest of day and most of night in the ER running EKG heart tests and blood tests and MRI the skull looking for tumors. She wasn't even dehydrated enough to need an IV, so they actually treated nothing, merely immense amounts of diagnostic screening to rule out every possible ailment that could cause dizzyness (which seems to be practically everything). Treatment cost in the five digits, paid by everyone else via insurance premiums. But, at least the pediatrician CYA from legal standpoint.
To some extent the real cost of "malpractice insurance" isn't directly paid as "malpractice insurance" but is funded by immense insurance premiums.
I looked it up for fun and the odds of a teen girl being diagnosed with a brain tumor is approximately one in ten thousand per year; the odds of a teen girl getting dizzy on the hottest sweatiest day of the year after working hard outdoors is somewhat higher, but legal CYA is very expensive.
As an anecdote, I recently went to the hospital suspecting a kidney infecting. I had all the signs (pain on both sides under the skin, smelly urine, a bit of fever that came and went) so I told the nurse I was pretty certain I had a kidney infection.
When the doctor saw me, she took my urine only and when it came back "perfect" she said I definitely did not have a kidney infection, that my pain was probably mechanical.
Two nights later and I couldn't move or think from the fever, the pain in my kidneys was so severe I could barely breathe, and I was fearing I was on the fast-track to sepsis (which I've had before).
Apparently the tech testing my urine sample fucked up, which is why the results came back perfect. The strange thing is, the doctor herself said she was surprised it was perfect, but took it at face value despite my insistence that I definitely had a kidney infection, especially considering I have a history of such infections. Also, when she said my pain was most likely "mechanical", I told her I hadn't done any intense activity lately, and she said I probably slept wrong, ignoring the fact I said I'd had a slight fever-- when they tested me at the hospital I had no fever.
It was a really weird experience. I understand skepticism against a patient's self-diagnosis, but I was claiming a kidney infection-- this is a common diagnosis for women with my symptoms and I've had many kidney infections before so I know what it feels like. Still, the doctor trusted that her three quick tests were perfect (urine sample, temperature, mechanical inspection) and her mis-diagnosis could have caused me permanent damage (kidney scarring or sepsis).
On top of that, this is a field that historically has had poor exposure to good engineering practice or mathematics. A field where a researcher's have both the intelligence and lack of exposure to plausibly rediscover integration without being clued in by anyone in the know [1].
There is almost certainly more to be gained by working with evidence and solid statistical practice than there is by gut and guessing. It is hard enough just keeping everyone focused on the measurable evidence and weeding out stats mistakes without pretending that doctors should somehow diagnose based on their 6th sense. That isn't a healthy expectation for any party, including the doctor.
Efforts should be made to figure out what variables they are actually using to make decisions, if those variables are actually useful predictors and then how to systematise them. It is tautologically impossible that an experienced clinicians is detecting undetectable variables in their decision making process.
[1] https://academia.stackexchange.com/questions/9602/rediscover...