“The goal of healthcare is to keep people healthy and failing that to fix or alleviate the problem.“
This is our mission statement so to speak. The actual actions we take to get there, the verbs of medicine if you will, are:
When we look under the hood, we find that each of these actions are computational problems. This means they consist of data collection and systematic processing to arrive at a solution.
Let’s look at Diagnostics as an example. Diagnostics is the task of classifying illness, classification being a classic computational problem
A patient arrives in my emergency room…
In the opening scenes of the sci-fi thriller Gattaca, no sooner isour doomed protagonist, Vincent Freeman, born than we hear his life’s outlook icily summarized: “Neurological condition — 60% probability, manic depression — 42% probability, attention deficit disorder — 89% probability, heart disorder — 99% probability. Early fatal potential, life expectancy — 30.2 years.”
Perhaps it’s fortunate that we’ve yet to achieve this level of predictive precision. But medical prognostics is developing at an intrepid pace.
Prognostics is the art and science of predicting the course of disease.
In medicine, accurately anticipating the course of illness is often as important…
Autonomous cars are coming. Rideshares aren’t going anywhere. Ergo, rideshare drivers will lose their jobs. What should they do? Retrain, upskill, become data scientists. Yes, yes. But what are their actual alternatives?
I asked Rafael, an Uber driver I met in New York last week. He said, “I’ll buy a driverless car and have it make money for me on Uber.”
I’ve heard this before. He seemed earnest. So I decided to look at the numbers. How viable is a driverless car as an investment vehicle…vehicle?
Here’s the spreadsheet if you don’t want to read my words.
The scenario Rafael…
developer, trauma doc, researcher, etc