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Loren Adler

@LorenAdler · Washington D.C. · joined 31 Jul 2011

Fellow & Associate Director, Brookings Institution Center on Health Policy | Associate Editor, Health Affairs Scholar | health policy, budgets, Knicks

10 419Followers
1 056Following
22 588Posts total
13.8KViews on collected posts

Últimas publicaciones

@LorenAdler We agree here! Don’t like your insurance premium, look no further than the large hospital corporation and what they charge… 183 views · 1 likes · 0 reposts · 0 replies Open on X →
Helpful look at loss ratios across different health insurance markets & and how stable they’ve been over time in the large-group market. Health insurance premiums tend to grow broadly in line with the amounts insurers pay out for health care claims. https://t.co/MNnNDbChnI
6.2K views · 15 likes · 5 reposts · 3 replies Open on X →
The @KFF analysis: https://t.co/AGS8noXSxo Also includes a look at the magnitude of MLR rebates paid in different commercial markets. https://t.co/tKZXC6CRJY
329 views · 1 likes · 1 reposts · 0 replies Open on X →
If allegations like this about non-payment (or delayed payment) of arbitration awards are true, that's a big friction impeding the high awards from translating to higher prices beyond arbitration ... for now. Non-payment issues are bound to get fixed. https://t.co/XUoLLicBVg 798 views · 0 likes · 0 reposts · 3 replies Open on X →
Whew, nothing to worry about then 713 views · 2 likes · 0 reposts · 0 replies Open on X →
TX's model appears to be functioning better than the NSA, but that's a low bar. Still beset by unnecessary admin costs. And it makes no sense to consider the 80th percentile charges, a unilaterally set list price subject to near-zero market constraint for affected specialties. 2.7K views · 5 likes · 0 reposts · 3 replies Open on X →
This is a great point by @TomOliverson. The QPA was always the issue in the No Surprises Act debate. Texas did come up with a great model for this. I have a podcast coming out in a few weeks with @MccandlessPati that discusses this in-depth. #IDR 1.7K views · 6 likes · 2 reposts · 0 replies Open on X →
.@MattAFiedler and I submitted comments on CMS' CY2027 OPPS proposed rule. We focus on two issues: 1) site-neutral payment for imaging services 2) how to make hospital price transparency data more useful 1.1K views · 4 likes · 1 reposts · 1 replies Open on X →

Frente a cuentas del mismo tamaño

8 publicaciones de los últimos 90 días, junto al rango de 10K–100K seguidores. llega a mucha gente, pero pocos de esos espectadores reaccionan.

Visualizaciones medianas930esta cuenta924mediana de 10K–100K
Alcance, %8.93%esta cuenta3.62%mediana de 10K–100K
Interacción, %0.42%esta cuenta1.52%mediana de 10K–100K
MétricaEsta cuentaMediana de 10K–100KProporción
Visualizaciones medianas por publicación9309241.01×
Alcance (visualizaciones ÷ seguidores)8.93%3.62%2.47×
Tasa de interacción0.42%1.52%0.27×

Otras cuentas de este rango →   Comparar con otra cuenta →   Cómo se construyen estas referencias →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

1.1K1 Sep
1.7K4 Sep
2.7K
7139 Sep
798
32910 Sep
6.2K
183

Last 8 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.56%1 Sep
0.46%4 Sep
0.29%
0.28%9 Sep
0.38%
0.61%10 Sep
0.37%
0.55%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes51.5%34 in total
Reposts13.6%9 in total
Replies15.2%10 in total
Bookmarks19.7%13 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

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