CMS released the 2015 policy year risk adjustment and re-insurance payment notice on Thursday afternoon. Several billion dollars net are changing hands. Roughly 10% of total premiums is changing hands.
Larry Levitt has the smartest take on the cash flows:
The $ changing hands under risk adjustment reflects some insurers getting sicker enrollees, and some being better at operating the system.
— Larry Levitt (@larry_levitt) June 30, 2016
What does Larry mean by “operating the system?”
Experienced insurers know how to optimize their risk scores while inexperienced insurers are still groping forward with limited data. Insurers that have only ever operated in the small group and individual market are at a data disadvantage compared to insurers that operate in individual, small group, CHIP, Medicaid, Medicare, and large employer group markets.
We’ll talk through an couple of examples of how the system is worked.
The first case is pure data mining from only individual market owned QHP claims.
Let’s say hello to Mary. She is 48 years old with Type 2 diabetes that is under control, asthma that is well controlled by drugs, and she had a pacemaker inserted a couple of years ago. Each of those conditions needs to show up on a valid claim every year for her insurer to gain risk adjustment points.
This is the first area of differentiation on operating a system between a good operator and a naive operator. The experienced operator will take a look at a couple of years of claims history and identify that Mary had a Type 2 diabetes diagnosis submitted in 2014, the pacemaker V-code submitted in 2014 and asthma medication scrips get routinely filled on the 24th of the month every other month. This information will run through a scrub program and produce a targeted diagnosis form. Mary’s primary care provider will get the form in her chart. or it will be inserted into the electronic medical record. The next time Dr. Patel sees Mary, he’ll add the relevant for the visit diagnosis code (conjunctivitis/pink eye) as well as the three extra valid and legitimate well documented but not coded diagnoses (Type 2 Diabetes, asthma, pacemaker status). The insurer will then pay him $400 for the coding. The insurer will now receive credit for an extra $10,000 or $20,000 worth of risk adjustment points.
Naive insurers will not have the system set up to aggressively chase from recent claims nor do they have the staff set up to do the chasing. Insurers that have plenty of experience working Medicare Advantage will just add a couple of data geeks to that team and a dozen outreach folks to support Exchange HCC.
Now let’s take Mike. He had his left arm amputated above the elbow in 2007 due to an industrial accident. He was covered by employer sponsored coverage through March of 2015. He then goes on Exchange to get a new policy as he moved across state lines. He does not go to the doctor for the rest of the year. He gets a flu shot in October and visits an urgent care to get stitches in September. For the current year, he is a very low cost member.
If he chooses the local co-op for his Exchange insurance, they see him as a very low cost member and they do not know anything else.
If he chooses the same insurer that covered him in 2007 when he lost his arm, they are able to mine their data and identify that he does not have a left arm. That condition does not go away, it does not change, it does not get better. They flag this and a coding specialist is allowed to go through his chart and submit a retrospective risk coding request supported by the 2007 claim and medical chart data. CMS will accept the change and attribute a $3,000 risk adjustment bump to the incumbent insurer with deep data sets.
The incumbent with the deep data sets and experience at running risk adjustment is not paying any additional money in claims expense for Mike. He is just a bonus revenue center.
That type of data mining is legal, and it is common (it is my 11:00 meeting this morning). However it is a competency that requires a lot of experience, specific and expensive technical knowledge, and deep data sets.
Other factors impact risk adjustment. Narrow networks with restrictive gatekeepers that are priced very low will have large net risk adjustment outflows as they attract the healthy and the young. PPO’s with broad networks are magnets for sick people. Knowing how to maximize risk scores is a key component of the risk adjustment swings but inherent plan design is probably a stronger factor in most cases.