Logrolling and market power in contract talks

Typically, full service health insurance companies will have several clusters of business. There is the low cost government cluster of Medicaid and SNP managed care. There is the medium cost government supported sector of Medicare Advantage, CHIP and low cost Exchange plans. And then there is the higher premium cluster of employer sponsored fully insured (ESI) plans and Administrative Services Only (ASO) self-insured employer plans. I worked at a full service firm. I was on the Medicaid geek team for the last three years.

Recently, I brought up the Rogers, Chernew and McWilliams Health Affairs paper on the impact of market power on local level provider and insurer pricing. They were only looking at employer sponsored insurance market power. They found what was to be expected. Entities with high relative market power got “better” rates from their point of view compared to entities with low market power. This was a good set of results as they were able to math up the trade-offs and attach some real numbers to the intuition.

My question though is how to account for the results if we are to assume that log-rolling in negotiations occurs?

An insurer might have a low market share for the high premium ESI/ASO market in a region. That same carrier could have a very high market share of the Medicare Advantage market. If that carrier is talking with a hospital that has never been in network to sign a comprehensive, all products contract, does the negotiation’s plausible agreement region get defined by each line of business’s relative market share or is the plausible agreement region defined solely by a blended dollar weighed market share?

More practically, does a hospital say that in order to get a stream of the Medicare Advantage money they’ll take lower than anticipated by RCM commercial rates or the carrier offer slightly higher Medicare Advantage rates to buy access for the employer side plans?

My intuition is that this type of log-rolling happens a lot. So how does it get measured and evaluated?

I don’t know.

ACA Element inventory

The ACA is a complicated law.  It has a lot of moving and interacting parts in it.  It also has parts that can be severed from the rest of the law without significant operational impact.  I want to conduct an inventory of the major elements that we will need to be familiar with during the second round of healthcare and health finance reform debate.  A basic understanding of what the different parts of the law do and how they play nicely with the other parts of the law will put you in good shape over the next couple of months.

I will break things down to the broadest stand alone structure and make comments as needed.

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Scholars seek new ways to track impact

The Chronicle of Higher Education had a piece yesterday on the emerging field (movement?) of altmetrics, or alternative means of measuring the impact of research outside of citation counts and journal impact factors.

I accidentally wandered into this discussion late last year when I posted a link to a piece by Jason Priem, a graduate student at UNC Chapel Hill in Library Science on the use of twitter by academics that led Austin and I to write a response of sorts noting that peer review journals cannot be replaced by twitter or blogs. This stance certainly wouldn’t put us at odds with the goal of altmetrics to try and better capture the impact of scholarship in ways other than peer review journals. I followed up with my suggestions about changes to peer review, that focused on ending blind review and making information on the negotiation between author, reviewer and editor more open after the fact.

Two weeks ago, I wandered in deeper when I attended the ScienceOnline 12 conference after being emailed a newspaper story on it by Austin, asking questions about it on twitter, finding out it was being held at NC State University and that the organizers were from Duke and UNC. I went and can honestly say it was the most interesting conference I have been to in 10 years, mostly because I was constantly confronted with new ideas and new interpretations about what constitutes evidence. Afterward, I think I have more questions than answers.

While attending, I ended up meeting most of the people noted in the Chronicle piece (here is one of my posts on the conference that focuses on altmetrics; here is a link to my peer review papers run through total-impact, run for me by Heather Piwowar (@researchremix) an alpha cite that aims to track non traditional measures alongside traditional ones).

A quote from Priem sums up the motivation of the altmetrics movement I think:

“I’m not down on citations,” Mr. Priem says. “I’m just saying it’s only part of the story. It’s become the only part of the story we care about.”

That’s where altmetrics comes in. It’s a way to measure the “downstream use” of research, says Cameron Neylon, a senior scientist at Britain’s Science and Technology Facilities Council, and another contributor to the manifesto. Any system that turns out to be a useful way to measure influence will tempt the unscrupulous to try and game it, though. One concern is that someone could build a program, for instance, that would keep tweeting links to an article and inflate its altmetrics numbers.”

I will keep watching the altmetrics debate, tools and developments closely.