I’m Jonathan Nankivell, an undergraduate in my last year studying Mathematics. My interests are in ML and collaborative epistemics.
I had to discover EA twice before it stuck. My first random walk was ‘psychology → big five framework → principle component analysis → pol.is → radical exchange → EA’ and my second was ‘effect of social media → should I read the news? → Ezra Klein on the 80,000 hours podcast → EA’.
Self-Improving Healthcare
Biorisk and Recovery from Catastrophe, Epistemic Institutions, Economic Growth
Our healthcare systems aren’t perfect. One underdiscussed part of this is that we learn almost nothing from the vast majority of treatment that happens. I’d love to see systems that learn from the day-to-day process of treating patients, systems that use automatic feedback loops and crowd wisdom to detect and correct mistakes, and that identify, test and incorporate new treatments. It should be possible to do this. Below is my suggestion.
I suggest we allocate treatments to patients in a specific way: the probability that we allocate a treatment to a patient, should match the probability that that treatment is the best treatment for that patient. This will create a RCT of similar patients, which we can use to update the probabilities that we use for allocation. Then repeat. This will maximise the number of patients given the best treatment in the medium to long-term. It does this by detecting and correcting mistakes, and by cautiously testing novel treatments and then, if warranted, rolling them out to the wider population.
This idea is still in it’s early stages. More detailed thoughts (such as where the probabilities come from) can be found here. If you have any thoughts or feedback, please get in touch.