Who dictates in network rates?
Insurers!
2/x
THREAD - Doctors want to fix “surprise bills” more accurately called unpaid insurer bills but today’s “agreement” is NOT the right fix for patients
It’s just a big boost to insurer profits & will shutter independent physician practices & rural hospitals!
1/x
Just joined other House and Senate leaders in announcing a surprise billing agreement. Under this agreement, the days of patients receiving devastating surprise out-of-network medical bills will be over. https://t.co/HELY6OiPtS
— Rep. Frank Pallone (@FrankPallone) December 12, 2020
Who dictates in network rates?
Insurers!
2/x
This is just giving more power to profitable insurers
3/x
Insurers have total control over “median in network” rates w their monopolies
5/x
https://t.co/NfgMrWNoq5
Independent practices had to shutter their doors or join large health systems to survive. Consolidation drives up costs! Rural areas were hit hardest.
6/x
Tying arbitration to “median in network” rates gives insurers ALL the leverage
7/
“From 2015-2018, the Out of Network Law saved NY more than $400 mil in emergency services, reduced out-of-network billing by 34%, lowered in-network ED physician payments by 9%”
https://t.co/HhqkTkB4Fe
NY’s surprise bill saved consumers $400 mil per @NYGovCuomo @NYDFS
In CA consumer complaints re access to care skyrocketed after benchmarking implemented
9/x
HR3502 by @DrPhilRoe @Dr_RaulRuiz already has 110 cosponsors but House and Senate leadership continues to ignore!
11/x
https://t.co/c7t6qnsuXJ
This agreement though is unacceptable - benefits insurers only & will crush independent practices & rural hospitals. Will drive consolidation, ⬆️ cost, exacerbate access to care problems
12/x
@SenSchumer @SpeakerPelosi @senatemajldr @RepKevinBrady @RepRichardNeal
/end
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More from Health
No-regret #hydrogen:
Charting early steps for H₂ infrastructure in Europe.
👉Summary of conclusions of a new study by @AgoraEW @AFRY_global @Ma_Deutsch @gnievchenko (1/17)
https://t.co/YA50FA57Em
The idea behind this study is that future hydrogen demand is highly uncertain and we don’t want to spend tens of billions of euros to repurpose a network which won’t be needed. For instance, hydrogen in ground transport is a hotly debated topic https://t.co/RlnqDYVzpr (2/17)
Similar things can be said about heat. 40% of today’s industrial natural gas use in the EU goes to heat below 100°C and therefore is within range of electric heat pumps – whose performance factors far exceed 100%. (3/17)
Even for higher temperatures, a range of power-to-heat (PtH) options can be more energy-efficient than hydrogen and should be considered first. Available PtH technologies can cover all temperature levels needed in industrial production (e.g. electric arc furnace: 3500°C). (4/17)
In our view, hydrogen use for feedstock and chemical reactions is the only inescapable source of industrial hydrogen demand in Europe that does not lend itself to electrification. Examples include ammonia, steel, and petrochemical industries. (5/17)
Charting early steps for H₂ infrastructure in Europe.
👉Summary of conclusions of a new study by @AgoraEW @AFRY_global @Ma_Deutsch @gnievchenko (1/17)
https://t.co/YA50FA57Em
The idea behind this study is that future hydrogen demand is highly uncertain and we don’t want to spend tens of billions of euros to repurpose a network which won’t be needed. For instance, hydrogen in ground transport is a hotly debated topic https://t.co/RlnqDYVzpr (2/17)
Similar things can be said about heat. 40% of today’s industrial natural gas use in the EU goes to heat below 100°C and therefore is within range of electric heat pumps – whose performance factors far exceed 100%. (3/17)
Even for higher temperatures, a range of power-to-heat (PtH) options can be more energy-efficient than hydrogen and should be considered first. Available PtH technologies can cover all temperature levels needed in industrial production (e.g. electric arc furnace: 3500°C). (4/17)
In our view, hydrogen use for feedstock and chemical reactions is the only inescapable source of industrial hydrogen demand in Europe that does not lend itself to electrification. Examples include ammonia, steel, and petrochemical industries. (5/17)
You gotta think about this one carefully!
Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)
The test is 99% effective in detecting both sick and healthy people.
Your test comes back positive.
Are you really sick? Explain below 👇
The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through
You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.
👇
Here is what we know:
- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative
Assuming 1 million people take the test (including you):
- 100 of them are sick
- 999,900 of them are healthy
👇
Let's now test both groups, starting with the 100 people sick:
▫️ 99 of them will be diagnosed (correctly) as sick (99%)
▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)
👇
Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)
The test is 99% effective in detecting both sick and healthy people.
Your test comes back positive.
Are you really sick? Explain below 👇
The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through
Really doesn\u2019t fit well in a tweet. pic.twitter.com/xN0pAyniFS
— Dr. Lena Sugar \U0001f3f3\ufe0f\u200d\U0001f308\U0001f1ea\U0001f1fa\U0001f1ef\U0001f1f5 (@_jvs) February 18, 2021
You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.
👇
Here is what we know:
- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative
Assuming 1 million people take the test (including you):
- 100 of them are sick
- 999,900 of them are healthy
👇
Let's now test both groups, starting with the 100 people sick:
▫️ 99 of them will be diagnosed (correctly) as sick (99%)
▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)
👇
