I was discussing #healthcare cost variation (& #EHP #RBP models) across geography with a colleague earlier today. I have followed the work of @zackcooperYale and thought it would be interesting to, in very simple terms, visualize some of the cost variation. A short thread. 1/n
2/n I started looking for a datasource. While our #benefitconsulting and #insurance clients engage networks of private insurers, that data is subject to different rules... I wondered what variation "looks like" for Medicare claims. I landed here: dartmouthatlas.org
3/n First, there is a ton of #Medicare data. It can be downloaded by state (see below) as well as to a county level (which I pulled into Python.) To consider the amount of cost attributable to different segments, I looked at the largest categories with observations by county. ImageImage
4/n #Hospital & #SNF: I found it interesting to note the changes of median costs across these states, as well as the higher rates of variation in WV compared with PA and MD. Furthermore, amongst NY, NJ, CT, is it possible there is even greater variation in NY? Hmmm... ImageImage
5/n #Physician: So, I wondered how this would compare with physician reimbursements. Hmmm... The variation in cost seems quite different, in fact. ImageImage
6/n #DME: What about the smallest "sliver" of the cost "pie"? It appears the story has similarities and differences in term of the absolute levels of variation, trends over time, and differences in variation... ImageImage
7/n In our #benefitsconsulting practice we regularly advise clients based on observed variation in costs arising from plan design, utilization, and unit costs. Our industry has generated increasing interest in reference prices (i.e. Medicare) as an alt. to PPO constructs. #RBP
8/n I am hopeful that employers are getting clear (and accurate) counsel about both advantages AND challenges in #ReferenceBasedPricing strategies. We have surveyed results across clients who have adopted RBP, and validated financial impact. But, they are not for everyone!

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More from @jensholger78

12 Jan
#Healthcare is just an interesting business to be a part of. I saw a fellow post details from the PSU Health website that included a bunch of information in json format. I was curious to see what the data revealed. 1/n Image
2/n The most interesting surprise, aside from specific breakdowns of variation in reimbursement per service by payer, was the specific classification for "Amish/Mennonite OP Rate". Image
Read 4 tweets
28 Dec 20
I have been curious about the changes to ICU capacity capacity. The weekly data captured at healthdata.gov/sites/default/……… now shows data through the week of 12/11. At first blush, the data doesn't appear to have changed much since the prior week. 1/n Image
2/n The aspect I am most curious about is the reporting of the data. An example of this is in reporting of ICU beds available and used. See below for one PA example: It is confusing to see the beds at this facility change during the weeks of 10/2 and 9/18 #covid #hospitals Image
3/n In contrast, note the reporting at this alternative #Pennsylvania hospital: While the average total #ICU beds increase slightly during the two most recent weeks, in contrast with the prior example, this hospital shows a percentage used < 100%. #LehighValley #ABE #Bethlehem Image
Read 7 tweets

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