4 ms·

Risk adjusted scoring is entirely viable in this age of data science, there is just no appetite for it. Doctors fight very hard against it because who actually

Risk adjusted scoring is entirely viable in this age of data science, there is just no appetite for it. Doctors fight very hard against it because who actually wants to be held accountable for outcomes? Insurance companies really aren't the villains in the US healthcare system, they're going to make money no matter what because they pass cost increases on to their subscribers and are capped in how much profit they can make via regulation.

rscho 5y ago
Risk adjusted scoring is currently absolutely not viable. You underestimate the messiness of the healthcare system by a huge margin. We don't even manage to record basic vital signs consistently, so believe me when I tell you that you can forget about any kind of nice statistical trick given the weakness of our data gathering processes. Plus it's not a matter of objectivity. Surgeons will subjectively assess that doing easy cases will be better. And in addition, they'll be correct. That's what really matters.
- wins32767 5y ago
  I literally was at a startup that could do this successfully, so I'm not underestimating the messiness.
  - rscho 5y ago
    I doubt we're speaking of the same thing, here.
AussieWog93 5y ago
>Risk adjusted scoring is entirely viable in this age of data science It's just not unviable, I don't think it's even possible. As soon as a metric becomes tracked, people are incentivized to game it. From my own experience as a very high-volume eBay seller, mandating a certain return rate led us to simply discourage customers from using the (convenient, well-designed) integrated returns systems. Mandating that only a tiny fraction of a percentage of items can be cancelled due to being out of stock leads to sellers sending either the wrong item or a fake tracking number (this gets us all the time on AliExpress). If data-driven software companies can't handle it for something as simple as eCommerce, I have no idea how the medical industry is supposed to get it right.
melq 5y ago
A good portion of the largest insurance companies are non-profits, look at Blue Cross Blue Shield and their affiliated companies. They still make tons of money, keep tons of cash on hand, enjoy the same high salaries as for-profits (not saying they necessarily shouldn't), and get special tax statuses/breaks. And the for profit ones are making plenty of money whatever regulations theyre subject to: >During 2010, Health Care Service Corporation, the parent company of BCBS in Texas, Oklahoma, New Mexico, Montana and Illinois, nearly doubled its income to $1.09 billion in 2010, and began four years of billion-dollar profits. I'm not saying they're villains, but "they're going to make money no matter what" isn't a compelling argument to me, and I have precisely 0 faith in the government to meaningfully regulate them.
Fomite 5y ago
Risk adjusted scoring is done in some areas where we have the data for this (healthcare associated infections and antibiotic usage). And this is a place where hospitals and doctors actively do want it to work, because there are financial penalties associated with it. It's still a fairly hard problem. I've had several very clever data scientists on teams who have gone "Oh, this is just an X problem..." and then 9 months later they're still trying to get a model to perform better than "Just take the average".

rscho 5y ago

Risk adjusted scoring is currently absolutely not viable. You underestimate the messiness of the healthcare system by a huge margin. We don't even manage to record basic vital signs consistently, so believe me when I tell you that you can forget about any kind of nice statistical trick given the weakness of our data gathering processes. Plus it's not a matter of objectivity. Surgeons will subjectively assess that doing easy cases will be better. And in addition, they'll be correct. That's what really matters.

wins32767 5y ago
I literally was at a startup that could do this successfully, so I'm not underestimating the messiness.
- rscho 5y ago
  I doubt we're speaking of the same thing, here.

AussieWog93 5y ago

>Risk adjusted scoring is entirely viable in this age of data science It's just not unviable, I don't think it's even possible. As soon as a metric becomes tracked, people are incentivized to game it. From my own experience as a very high-volume eBay seller, mandating a certain return rate led us to simply discourage customers from using the (convenient, well-designed) integrated returns systems. Mandating that only a tiny fraction of a percentage of items can be cancelled due to being out of stock leads to sellers sending either the wrong item or a fake tracking number (this gets us all the time on AliExpress). If data-driven software companies can't handle it for something as simple as eCommerce, I have no idea how the medical industry is supposed to get it right.

melq 5y ago

A good portion of the largest insurance companies are non-profits, look at Blue Cross Blue Shield and their affiliated companies. They still make tons of money, keep tons of cash on hand, enjoy the same high salaries as for-profits (not saying they necessarily shouldn't), and get special tax statuses/breaks. And the for profit ones are making plenty of money whatever regulations theyre subject to: >During 2010, Health Care Service Corporation, the parent company of BCBS in Texas, Oklahoma, New Mexico, Montana and Illinois, nearly doubled its income to $1.09 billion in 2010, and began four years of billion-dollar profits. I'm not saying they're villains, but "they're going to make money no matter what" isn't a compelling argument to me, and I have precisely 0 faith in the government to meaningfully regulate them.

Fomite 5y ago

Risk adjusted scoring is done in some areas where we have the data for this (healthcare associated infections and antibiotic usage). And this is a place where hospitals and doctors actively do want it to work, because there are financial penalties associated with it. It's still a fairly hard problem. I've had several very clever data scientists on teams who have gone "Oh, this is just an X problem..." and then 9 months later they're still trying to get a model to perform better than "Just take the average".