Net talk at AI #bythebay: "AI For Good: Fighting Health Insurance with AI" by Holden Karau.
Starting with CWs around surgery, hospitals, trans references, and broken bones.
Discussion
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What's the problem? Health Insurance in America frequently denies medical care, Insurance _might_ be using AI to deny claims, and appealing claim denials is hard
And they have a lot more money than us!
We have a social problem, and we're going to try and duct tape it with computers!
"Making healthcare leak a little less fast"
How? AI to take denials and produce appeals.
This includes:
- An ML model to take denials and produce appeals
- Frontend interface for people to access the model
- Library to remove and put the personal data back in
First problem: training data!
- Insurance companies won't give the data
- Doctor's offices have the data, but it's hard for them to give it out
- Reddit has some data! But there's sample bias
- Insurance commissioners!
Once we have the data, how do we pick a model to fine tune?
- Make sure the model will fit in memory
- Check the license. Then check it again.
- Then consider good base performance, that's got good scores on best-related tasks
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How do we do the fine-tuning?
- Correct data structure
- Pile o' shell scripts
- Axolotl! this was clutch. https://axolotl.ai/
How do you improve on this?
- Find more data sources
- Filter out bad data, both via humans and regexes
- RAG!
Now we're seeing a demo of https://fighthealthinsurance.com/, what this talk has been all about.
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