price-transparency-guide
The technical implementation guide for the tri-departmental price transparency rule. (by CMSgov)
polars
Dataframes powered by a multithreaded, vectorized query engine, written in Rust (by ritchie46)
price-transparency-guide | polars | |
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14 | 144 | |
341 | 26,514 | |
1.5% | 3.9% | |
5.9 | 10.0 | |
20 days ago | 6 days ago | |
Ruby | Rust | |
- | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
price-transparency-guide
Posts with mentions or reviews of price-transparency-guide.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-03-17.
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Are there any websites making the "Transparency in coverage" data available?
The problem is that its all released in files in JSON code. The file from the above link has links to 2374 other JSON files that are compressed into multipart JSON.GZ files and some of these are 25 gigabytes and larger. Literally terabytes of data.
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Analyzing multi-gigabyte JSON files locally
Neither of these are implemented via HL7 or FHIR. CMS has defined a new "machine readable format" to implement the regulation: https://github.com/CMSgov/price-transparency-guide
- Dataset needed!!!!! Well I've worked on some datasets that I took from Kaggle. No satisfaction. I request you to provide an excellent Dataset to perform Exploratory Data Analysis.
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Help hosting trillions of rows of new health insurance public price data
CMS was very particular in the format they required payers to use. You can even check out the spec yourself on GitHub. Unfortunately their requirements don’t make a lot of sense from a data engineering standpoint.
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Curious if this community saw this - [OC] The ridiculously absurd amount of pricing data that insurance companies just publicly dumped
You can review the provided example schemas here: https://github.com/CMSgov/price-transparency-guide/tree/master/schemas
- [OC] The ridiculously absurd amount of pricing data that insurance companies just publicly dumped
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I analyzed 1835 hospital price lists so you didn't have to
The health insurance companies are now required to publish this and in fact the rule went into effect July 1 2022.
Look up price transparency by CMS (the data will be published in this format: https://github.com/CMSgov/price-transparency-guide)
Note: the data being published by payors in machine readable format (MRF) is MASSIVE - terrabytes of data. Example: https://transparency-in-coverage.uhc.com/
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How Much Health Insurers Pay for Almost Everything Is About to Go Public
This may be useful in figuring out how to parse the contents.
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Hospitals and Insurers Didn’t Want You to See These Prices. Here’s Why
FYI, there is a similar effort going on that requires all health plans to host machine readable files containing negotiated rates by provider and procedure code by 2022-01-01: https://github.com/CMSgov/price-transparency-guide
- Instructions around the usage of meta robot tags and robots.txt files
polars
Posts with mentions or reviews of polars.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-01-08.
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Why Python's Integer Division Floors (2010)
This is because 0.1 is in actuality the floating point value value 0.1000000000000000055511151231257827021181583404541015625, and thus 1 divided by it is ever so slightly smaller than 10. Nevertheless, fpround(1 / fpround(1 / 10)) = 10 exactly.
I found out about this recently because in Polars I defined a // b for floats to be (a / b).floor(), which does return 10 for this computation. Since Python's correctly-rounded division is rather expensive, I chose to stick to this (more context: https://github.com/pola-rs/polars/issues/14596#issuecomment-...).
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Polars
https://github.com/pola-rs/polars/releases/tag/py-0.19.0
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Stuff I Learned during Hanukkah of Data 2023
That turned out to be related to pola-rs/polars#11912, and this linked comment provided a deceptively simple solution - use PARSE_DECLTYPES when creating the connection:
- Polars 0.20 Released
- Segunda linguagem
- Polars: Dataframes powered by a multithreaded query engine, written in Rust
- Summing columns in remote Parquet files using DuckDB
- Polars 0.34 is released. (A query engine focussing on DataFrame front ends)