Algorithms, for life
Algorithms, for life
Learning how to do this stuff in real life is way harder.
But I think you can pick up a lot of interesting algorithms by intentionally working with, or reading about smart people in different arenas.
With months until I ejector seat out of clinical medicine — I want to share some broadly-applicable algorithms I've picked up from good doctors:
I Every conversation is a transfer of risk and why hierarchy is crucial.
II Knowledge ≠ power.
III How to be taken seriously.
Algorithms from Medicine
Algorithm I: Risk Transfer and Hierarchy
When I first started working, nurses would often tell me about patients I wasn't covering—
"Just to let you know... X's blood results are back".
Great. Not my problem... I would think.
Except it had become my problem.
And then I realised: This was not a friendly "just to let you know" conversation, but a transfer of risk.
In other words, whatever had been said was now my problem.
—
Similarly, I would discuss random patients with a senior registrar. Just wanting general advice—
"How fast do we give fluids to renal patients?".
Only to be bombarded with 20 questions, or a polite request to speak to my own team about this patient. Weird.
What i didn't understand was that — again, this conversation was not just me getting general information — it was actually a transfer of risk/responsibility to the registrar.
I think in high stakes industries: Medicine, engineering, law etc, information carries a heavy risk-burden with important implications.
1) it makes it a faux-pas to get advice outside of proper channels.
2) Flat hierarchies and beanbags are in vogue right now — and probably make sense if you're deciding which button colour will get the most ad clickthroughs.
But high-stakes industries necessitate hierarchies because it needs to be really clear who is responsible for what, and which way risk/responsibility is being transferred.
Algorithm II: Knowledge is not Power
Knowledge is not really power. In fact, it often confuses things — and can lead to worse outcomes.
Sounds weird. And millions of biohackers will disagree.
But if you view a patient encounter as a problem (i.e. finding out what's wrong and how to fix it) — conventional thinking suggests getting as much information about the problem as possible (bloods, scans, biopsies etc).
Medicine is somewhat unique, because these investigations can cause harm (pain, radiation or death). And the medicolegal landscape can mean an incidental finding along this journey can lead to a cascade of further harmful investigations, without much benefit.
But even in industries in which the above isn't true, information-gathering in itself can become procrastination (related: opportunity cost).
An important algorithm from clinical reasoning is really questioning how an investigation (further information gathering) will change your decision. If it won't, in most cases — best not to do it.
For example, in a patient hours away from death — maybe you could optimise management by taking a painful blood sample from the artery — but what's really going to change? What's the point?
Better to quickly establish if death is indeed irreversible, gather their priorities — and likely come to the decision that their comfort trumps more information gathering.
—
So much of life is explained by the midwit IQ meme. In my own health journey, I'm super guilty of midwit moves.
A friend recently turned me onto Exist.io — where I can collate almost everything I track.
But really this is all bullshit. It's procrastination.
Say if I added continuous glucose monitoring, monthly blood testing and one-off genetic screening to my biohacking stack. What would materially change?
If I have no predisposing cancer genes — should I pick up smoking?
If I'm prone to developing cancer, should I stop smoking?
If my LDL is high, should I stop eating takeout pizza?
If my glucose is in the normal range, should I eat more sweets?
The conclusion is always going to be: 1) don't smoke, 2) eat well 3) exercise regularly and 4) sleep a decent amount.
Provided I'm 1) young and healthy and 2) not an elite athlete — more information won't change anything.
Knowledge ≠ power. In fact, it's often a distraction from making hard decisions.
Good decision-makers recognise information is always going to be imperfect, and find the optimal striking zone between adequate information and swift action.
Algorithm III: Tone
When you start working as a doctor, you're a cockroach in the hierarchy. No one takes you seriously.
Naturally, you're taken more seriously as you climb the ladder. Like so: Junior Doctor -> Registrar -> Consultant.
As a Consultant, a quick introduction as 'X consultant' is like rocket fuel — shit will get done.
I remember nervously asking for a scan to be done for a deteriorating patient, only to be questioned with "Hmm. Doesn't sound urgent." and "Speak to your registrar and call back".
The patient was in critical care by the time the scan happened.
—
When you're at the top of the ladder, it's super easy to make shit happen. You can lean on hierarchy.
So the question becomes, how can someone on the bottom (of any hierarchy) make stuff happen?
I think 50% of it is tone.
Speaking with confidence, and avoiding weasel words (conversational hedging) makes people take you seriously.
"I think this patient is pretty unwell" -> "This patient is acutely unwell".
And here's the other part, ending questions with a low note instead of a high note is crucial. Ending on high notes is called uptalk or high rising terminal (video example) — and signals uncertainty.
There's some controversy surrounding it, and it is appropriate in many situations — but it's the perfect formula for people not to take you seriously.
There are interesting age and gender relationships, and the rise of uptalk can be partly blamed on US reality TV shows.
In every situation, there's a goldilocks tonal zone between being a d*ck and lacking conviction.
Podcast
End
Picked up any interesting algorithms from your life? Hit <reply> and let me know.
With warmest wishes,
Musty