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What this article is also missing is the fact that a lot of the existing data problems have to do with the cost of obtaining this data. When we move to a more universal collection of the data in a structured format that can then be used to further train the model, you actually end up with a better representation.

However, this all falls apart with the current access to healthcare. If the access is not universal, then you can’t expect the results to be anywhere near equal or at least similar. We really need to solve the healthcare access problem.

The other item I find questionable is the example with home-based rehab vs a facility. Sure, for better-off patients with a good home environment, transportation, good food, etc., being in that good/positive environment will likely lead to better outcomes. However, if the person doesn’t have that, is that still better than a facility? Would be great if we saw data adjusted for this disparity.




> When we move to a more universal collection of the data in a structured format that can then be used to further train the model, you actually end up with a better representation.

I'm not sure how that's true. Access to products and services that do this kind of collection is very much a class issue. Poor people, especially in the US, can't afford regular physicals nor personal health trackers like a fitbit. Additionally, personal health technologies with the best, most accurate data are the most expensive ones - e.g. Apple Watch vs. generic fitness band.

If we lived in a world where class was separate from race or gender, you might be correct. But that's not the case.




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