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by phren0logy
2944 days ago
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Probably. The following applies to the US: It seems to me that some of the biggest barriers involve getting access to enough data to make a good training set. Some of this is related to HIPAA and related privacy laws, which are in place for a good reason but are still a major barrier. The other big factor is the fragmentation of the data across different, non-interoperable, non-standard formats from different vendors who have intentionally made interoperability difficult. That part bothers me much more. Many patients have data spread across a dozen or more paper charts, lab systems, EMRs, pharmacy databases, etc. As with most data science tasks, the "data munging" is the hardest part. I care about this mostly from the perspective of treating patients. Beyond training sets for AI, I need those records for the same reason: to make appropriate and informed treatment decisions. From the outside looking in, it's the kind of situation that begs companies to over-promise on results to get enough money to even give it a shot. |
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I was at a talk recently by an executive in charge of managing data for a massive health system. Think tens of millions of patients. He said they dont share data because if people learn their real quality metrics it would give payers, the public, anyone with an interest that isnt aligned w the hospital system ammunition against them. Have heard this sentiment echoed by many. Open data is their enemy because then patients could choose based on outcomes, not on market power or how nice the lobbies are
The EMRs designed a closed garden into their systems DNA. If data is shared freely then it's easy to switch systems, and they will have to compete based on cost and quality and couldn't charge hundreds of millions for installations and maintenance contracts
Call me cynical but this is all based on conversations with people well placed in the industry