Risk adjustment in the ACA at both ends of the spectrum

The Centers for Medicare and Medicaid Services released the 2023 risk adjustment report earlier this week.  Risk adjustment is intended to make insurers risk agnostic.  Transfers are paid within state between insurers with no additional federal dollars entering the market.  The one exception is an insurer funded catastrophic risk pool that covers a portion of claims over $1 million dollars. My friend and colleague Wesley Sanders combined the data with a few different sources of enrollment data to produce a per member per month (PMPM) transfer calculation. 

I have a few observations that I want to pull out from the Top 10 recipients and payers of risk adjustment.

	Largest Reciepents of RA PMPM		Largest Payers of RA PMPM
	MM	PMPM Transfer	MM	PMPM Transfer
1	68,267	738.62	45,130	-209.73
2	53,801	722.59	878,706	-184.31
3	62,943	522.43	306,751	-178.48
4	19,085	465.33	619,292	-169.04
5	334	375.05	76,501	-124.72
6	5,079	352.79	1,975,515	-116.11
7	5,555	290.13	1,489,464	-106.33
8	99,663	254.91	109,178	-103.41
9	30,680	219.52	7,331,524	-102.75
10	98,887	213.65	13,659,170	-102.37

First, look at the difference in the size of the transfers. The #1 payer pays $209 PMPM into risk adjustment.  This is smaller than the PMPM of the 10th largest recipient.  The tails are very different in the PMPM size of the transfers.

More interesting to me is the sheer difference in member months.  An enrollee can have anywhere from 1 to 12 enrolled months in a year.  Dividing total member months by 10 or 11 probably gives a decent approximation of total unique individuals enrolled.  The Top 10 net recipients are SMALL to TINY insurers.  I am trying to figure out how insurer #5 with 334 enrolled member months generates enough revenue to justify hiring an actuary to file their rates.

The net risk adjustment payers range from small-ish to really BIG.  This is interesting.  It is not unusual to see extremes of a distribution to be heavily composed of smaller entities because each enrolled individual has more variance in a small cluster than a big cluster.  But we don’t see that here.  Instead, we see some national and regional carriers having substantial payables.

We probably should think about the market dynamics where the big companies are actively seeking to enroll low risk enrollees and the small companies get extreme tail risk.