ti

tictoc.health

Hacker News

tictoc.health

We are trying to address some problems with Insurance Companies' efforts to comply with Transparency in Coverage. https://tictoc.health The Transparency in Coverage rule was issued by the US Department of Health and Human Services (HHS) and aims to increase price transparency and promote competition in the healthcare industry. The rule requires payers to make clear, accessible, and accurate information about the costs of their healthcare services available to the public in a machine-readable format. There are some problems with what the payers (aka Insurance Companies) have done. They haven't made it very easy to get the data. We explain a little bit about how each payer makes it tricky. Check out the site to see the details for each insurance company. If you are interested in pulling down a whole lot of health insurance data yourself, we have collected every URL to every machine-readable-file from all of these payers. You can download this file and pull the individual in-network rate files yourself. We have a lot of ideas about taking this further. What do you think should happen next?

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57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: mac, plain · Missing: agents, macos, agent
27%27% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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