2023 ACA Risk Adjustment Transfer Report - Part 3

This post is a bit of diversion from the 2023 transfer report itself, but as carriers wrap their 2025 rate filings, I thought it would be interesting to share some of the insights that can be found in those filings and why I believe risk adjustment is the cause of and solution to all of life’s problems. Next week, we’ll look at how the 2023 transfers tended to shake down by plan type, but in today’s post we are going to do a deep dive into how risk adjustment can affect the strategy of a carrier, including the kinds of plans they offer.

In the vault?

One of the things that makes being in a regulated industry interesting is that there is a lot of information publicly available about carriers’ products. Aside from the Public Use Files discussed in the prior post, each state also makes available information about the rate filings, since insurance is primarily regulated by the state. The level of detail provided varies by state: some provide almost no useful information, while others make available almost all of the rate filing materials submitted by the carriers without any redactions.

The more detailed filings can provide some meaningful insight into the “why” of a carrier’s strategy, as the details can include such things as actuarial memoranda, detailed explanations and reports in response to regulator objections, or redlined policy documents which give a clear view of what is changing from one year to the next. One of the new requirements for plan year 2025 is that a carrier who wants to offer more than two unique plan designs can only do so if it’s going to benefit individuals with specific chronic conditions. And the carrier can’t just assert that it will have a benefit: if they want to offer one of those plans, the carrier needs to provide an actuarial justification showing how the plan will reduce the cost sharing for those members by at least 25%.

This brings me to some interesting data found in a particular carrier’s 2025 rate filings. Here I will note that I am only inferring what this carrier’s strategy might be based on the publicly available data I discuss here; this carrier is not a client of mine and there may be other considerations that the carrier had that aren’t in the rate filing. Given this, I obscure the identity of the carrier, but all of the information is from a real rate filing for 2025. For the rest of this post, we’ll call the carrier “Vandelay Insurance.”

Vandelay Insurance wants to offer a plan targeted at members with diabetes and certain pulmonary conditions. Let’s call this “The Bubble Boy” Plan. The Bubble Boy plan is a modified version of another plan (which we’ll call the “Serenity Now” Plan – since you may not be able to afford the copay for therapy, your best bet is to repeat the phrase “Serenity now!” when you get angry). The Bubble Boy Plan offers benefits like $0 copays for PCP or specialist visits related to these conditions, $0 behavioral health copays for behavioral health visits related to their condition, and preferential copays for maintenance meds that treat the condition. Everything else (other specialist copays, RX copays, deductible, out of pocket, etc.) is more or less the same between the two plans. Vandelay’s actuaries estimate that a member with one of the targeted conditions would pay about $9,000 less in copays/coinsurance per year on the Bubble Boy Plan than the member would have were they enrolled in the Serenity Now Plan.

Here's where it gets interesting… if the member is paying $9,000 less in copays, then we must assume the plan is paying the difference – so we might imagine that the premium for the Bubble Boy plan is going to be a lot higher than the Serenity Now Plan so they can make up that $9,000 difference. But when I looked at the premiums for the Serenity Plan vs. the Bubble Boy Plan, the Bubble Boy Plan is only about 0.5% more, or less than $3 per month on average. Thus, even if the member is enrolled for a whole year, Vandelay insurance is only getting about $30 more in annual premium. So why is the Bubble Boy plan priced basically the same as the Serenity Now plan? The answer, of course, is risk adjustment!

The Math

Let’s start with some basic math. Remember from our earlier posts that if a health plan has higher than average risk, they pay into the statewide risk pool, whereas if they have lower than average risk, they’ll receive money from the statewide risk pool. This assumes risk adjustment works perfectly, which it doesn’t, but let’s grant this assumption for now. But even though risk adjustment is calculated at the plan level, you can reasonably approximate how much an individual contributes to the transfer by plugging their information into the risk adjustment formula. I won’t bore you with the details, but I’ve done the math using some basic assumptions about statewide risk and the results begin to explain why a carrier might try to attract higher risk members.

The plan I looked at is a silver plan. If we assume that a 40 year old male is eligible for Cost Sharing Reduction subsidies and he has no chronic conditions, then the health plan will receive about $470 per month in premium, but has to pay in about $300 per month into the risk adjustment pool. This means they’re only left with $170 a month to pay administrative costs and cover the member’s claims expenses. Vandelay’s rate filing says they expect to spend about $65 per member, per month (PMPM) in administrative costs, so for a member without an eligible chronic condition, the health plan is left with just $105 PMPM in revenue to pay for medical costs – not much margin to play with if the person has an acute condition like a broken bone come up (which wouldn’t have a risk adjustment value), or even if they get a few preventive services like an annual wellness visit and a flu shot.

But if the member has COPD, that number swings from a $300 risk adjustment payable to a $107 risk adjustment payable, or an improvement for the carrier of $193 PMPM (more than $2,300 per year). In other words, that $100 PMPM they had for medical costs now goes up to $300 PMPM. If they also have major depression, the risk adjustment transfer improves by another $305 per month. And, in many cases, conditions are additive. So if someone has both COPD and depression, the health plan receives $198 PMPM in risk adjustment, compared to paying in additional $307 PMPM in risk adjustment if someone has no chronic conditions – a difference of $6,000 per year. If the member also has diabetes, it’s a difference of more than $10,000 per year.

Of course, someone with chronic conditions is likely to have health care costs that are higher than someone without any chronic conditions, but Vandelay is likely hoping a few things might happen here: 

Improved Targeting

Vandelay is likely hoping that it will be easier to figure out who has these chronic conditions and get them documented (since risk adjustment only pays for documented conditions supported by a physician visit). The Bubble Boy plan is clearly the better choice if you have one of the conditions they’re targeting, but if you don’t have one of those conditions, the Bubble Boy plan has a few things that are slightly worse (for example, higher ER coinsurance) than the Serenity Now plan, making it a worse option for people without those conditions. So, buying the Bubble Boy Plan likely is a strong signal that you have one of the conditions that it is designed for.

Why does that matter? One of the hardest things about doing risk adjustment well is figuring out what health conditions someone may have that the health plan may not be aware of. Only about 20-25% of people enrolled in ACA plans have a chronic condition eligible for risk adjustment, and figuring out who those 20-25% of people are is a huge part of any risk adjustment department’s job.

Someone who has well-managed COPD may not be on regular meds for their condition, and perhaps they just had their annual visit with their PCP before enrolling in the Vandelay plan (information Vandelay would likely not be privy to). If the member fills an inhaler at the end of the year, but they hadn’t had another PCP visit since enrolling in the plan, Vandelay doesn’t get credit for that condition and the member’s risk score looks the same as if they had no chronic conditions – meaning Vandelay is losing out on the revenue they would have gotten if they had made sure to get the member in to see a physician and get their condition properly documented. Vandelay had some claims exposure to this risk, but they didn’t get the benefit of the improved risk adjustment transfer.

To try to solve this, Vandelay could try to cast a wide net by getting every one of their members into a physician, but that can be costly, and they may end up generating a bunch of extra claims cost and administrative expense that only yields additional risk adjustment a small percentage of the time, and doesn’t have much of a benefit to the member. There’s also more attention being paid to these kinds of “code fishing” expeditions – see the recent WSJ article about this practice in Medicare Advantage. Even if you disagree with the WSJ’s conclusions, it seems inevitable that regulatory scrutiny might make these approaches less effective in the future.

Rather than the wide net approach criticized by the WSJ (and others), the Bubble Boy Plan might help Vandelay more effectively choose who to target to make sure they get credit for the conditions they’re taking on the risk for. If a person enrolls in the Bubble Boy plan, that’s a strong indicator that they likely have one of the conditions that the plan is targeting, so Vandelay can focus some of its efforts on those members when trying to make sure someone gets connected with a physician, instead of casting a wide net across their whole population.

Financial Barriers to Care and Induced Demand

People are more likely to get care when it’s free. So there’s two kinds of patients that Vandelay may be hoping the Bubble Boy plan might attract. The first is someone who has the chronic condition but it’s poorly managed because they can’t afford to go the doctor or fill their meds. This person might cost Vandelay more in year one than they would have otherwise because suddenly they’re getting lab work, prescriptions, etc. that they weren’t getting before. But in the long run, we would expect (or at least hope) that the person’s overall spending goes down, since they might be less likely to end up in the ER or end up hospitalized with complications that could have been prevented. If Vandelay can retain the member for the long-term, this could be a real win-win situation: the health plan has a long-term, profitable member, and the member has better health outcomes. These kinds of incentives are why I love risk adjustment.

There’s another kind of member that this plan might attract, though. Often, someone with a chronic condition (especially if they’re on a CSR silver variant) is going to meet their out of pocket maximum no matter what – which means part way through the year, all covered care is free to them – even if it’s unrelated to their chronic condition and potentially unnecessary. The MOOP really matters!

With a plan like the Bubble Boy plan, the member can get free care all year-round for stuff that hopefully helps them manage their chronic condition better. Again, the hope is that this ultimately leads to lower costs – and since more stuff is free, they may not meet their out pocket maximum at all, potentially reducing the kinds of unnecessary care that may happen when they meet their out of pocket maximum under the Serenity Now plan. 

Other Options: Network Pricing Advantages, Silver Spamming

Another option might be that the health plan has partnered with a specific hospital or physician group with expertise in treating these conditions. The physician group may have agreed to give preferential rates to the carrier in exchange for offering this plan because they know they’ll have less bad debt to chase under this plan design. Although there’s a slight adjustment for geographic cost in the formula, risk adjustment factors don’t vary based on local variations for treating a particular condition, only local variants of the overall costs of care. So, if a carrier is able to lower their costs of treating diabetes by 20%, their risk adjustment still compensates them as if their diabetic costs were the same relative to the national average. This can be just a pure arbitrage play - but if a carrier has some advantages in their contracted network rates against others that favors specific conditions, then the carrier has an incentive to try to attract individuals with those conditions.

There’s one other, more insidious possibility for why a carrier might offer a plan like the Bubble Boy plan: by having another plan option available, they can “silver spam” their competitors to page 2 of HealthCare.gov. Dr. David Anderson explains this very well from a post several years ago over at Balloon Juice so I won’t rehash it. The reason carriers now must provide a justification to add additional plans beyond 2 is because of the problems that silver spamming causes. One fear that I (and others) have is that even with this limitation, there are still too many outs for carriers to get around the plan variant limitation. Offering a variant like this if you are the price leader lets you push competitors off the front page and out of sight of consumers, so the cynic in me wonders if some health plans might be offering these conditions as another angle to try to monopolize the front page. We’ll have to wait a few years to see what the plan level enrollment data shows. However, so far the new rule reducing the plan options available does seem to be having some effect – most 2025 carrier rate filings I’ve looked at are offering fewer pans on exchange than they did in 2024. This isn’t a representative sample, though, so we’ll have to wait for the public use files to come out to really dig in more systematically.

Conclusion

Phew…. If you’ve made it this far: congratulations! You probably now know more about risk adjustment than a lot of health plan executives. But hopefully this demonstrates what I always tell my clients when they ask about what they can do to receive more in risk adjustment: it really starts at the plan design stage. There are things about a plan that are going to attract a higher risk member, and if a plan only starts thinking about risk adjustment several months into the plan year when they’re trying to book financial accruals, they’re almost sure to be behind. Further, the health plan has to be thinking about whether they really want the high risk adjustment receivable. If the health plan wants to sell the Bubble Boy plan but has a bad contract with the pulmonology group, they might be in a world of hurt when the plan enrolls lots of members with respiratory conditions. On the other hand, if a plan wants higher risk members, it takes more than just willing it to be so. Whether it’s network, plan design, distribution, branding, etc., if a plan wants to target a population, they need to understand the member’s needs.

We’ll see this in action next week when we look at some of the trends in who are the receivers and payers of risk adjustment: one consistent trend is that Blues plans are usually receivers. Is it the Blues brand? Is it their network? Is it a common strategy? I have some theories, but what do you think? Let me hear your thoughts in the comments below!

Giddy up!

2023 ACA Risk Adjustment Transfer Report - Part 2: Putting it in Context

In our last post, we looked at why risk adjustment exists to begin with, and why looking at the raw transfer alone doesn’t tell you much. In today’s post, we’ll look at different ways you can make sense of the data and what it tells you about the companies that are receiving and paying into risk adjustment.

Normalize it: Per Member, Per Month (PMPM)

First, we should understand the concept of “per member, per month” or PMPM. PMPM is a key measure for any health insurance company, and it’s used in all sorts of metrics - you’ll see claims, premium, utilization, administrative costs, etc. all expressed as a “per member, per month” measure across the industry. Lots of industries try to normalize measures this way: airlines looks at revenue by passenger seat-mile, hotels by bed-night, etc. The math for PMPM is relatively simple - whatever metric you’re looking at (usually a financial one) divided by the number of members for a given month, (or number of member months for a given period). So, if you had $500,000 in premium for a month, and you had a thousand members in that month, your premium is $500 PMPM.

This brings us to a second, related concept: member months. A member enrolled for 12 months of a year is different than a member enrolled for only one month. Thus, a member enrolled for January to December has 12 member months for that year, and if they were enrolled only from July - December, they only have six member months. This seems obvious enough, but it matters because sometimes publicly available information just gives you an enrollment count, which can be misleading if the carrier’s membership isn’t consistent from month to month (which it usually isn’t). Often, you can get close enough by just assuming the enrollment was consistent throughout the year (multiplying membership at a point in time by 12), but this can miss some nuance given seasonality of enrollment and disenrollment patterns. If you can get your hands on member months data, that’s even better. How do you get hands on such data, you may ask? Great question!

PUF: The Magic Data

One of the nice things about working in a regulated industry is that there is a lot of data out there, even from privately held companies. CMS publishes various Public Use Files, or “PUFs” which provide information about things like enrollment, rate filings, plan designs, etc. about health plans regulated by the ACA. These can look a bit different depending on whether the plan is sold on HealthCare.gov or on a state Exchange, but generally the kinds of information you can get is consistent even if the format isn’t always the same. PUFs that I use regularly include:

1) The Plan Data PUFs: this has detailed information about the rate and plan filings that carriers submit to CMS. This has things like what rates are being charged, detailed on the plan designs, what areas a carrier sells its plans in, and even fun administrative data like denial percentages. These usually come out in late October for the next year - i.e. the 2025 plan data will come out in October, 2024. This page is for all the FFM carriers, but there’s another page for State-Based Marketplace (SBM) states here. The SBM one has a less consistent release schedule.

2) The Rate Review PUFs: this has detailed information from the carrier’s rate filing. This is also called the “URRT PUF” - for the Unified Rate Review Template. The URRT is an Excel template that every carrier has to fill out and submit to CMS and state regulators. This is different from the plan data PUFs, although some of the data overlaps. I think of the plan data PUFs as administrative data templates. They have the information you would need to display a plan on HealthCare.gov, like what’s the copay for a specialist visit, and what is the rate for a 42 year old in Broward County Florida. The Rate Review PUF, on the other hand, shows you data around how a carrier came up with that rate (or at the very least, what data they shared with regulators to justify the rate they’re charging). It has both what they project their costs to be in the next year, as well as what their actual costs were in the prior year. It also includes information on off exchange plans, both individual and small group. The PUF is a massive flat file aggregating all the Excel templates that every carrier submitted together, so I find it helpful to look at one of the URRTs from a carrier when exploring this PUF. Here’s an example of one for the 2025 rate cycle for Ambetter’s Georgia plan, obtained via an Open Records Request from the Georgia Office of the Commissioner of Insurance. Georgia releases the PY 2025 templates earlier than a lot of states, so for most states, the PY2025 templates aren’t available. The first tab has carrier-wide information about claims experience, broken down by benefit category; the second tab has plan-design level information but at a less granular level, and the third tab has the geographic factors for each rating area. Spend a little bit of time in this file and you’ll see how much information there is.

This PUF is really useful for looking at things like overall performance for a health plan - but the data is a bit lagged. The rate review PUF is typically released in October for the following year, based on data filed with CMS and state regulators in the summer. So 2025 Rate Review PUFs will be released in October. The most useful information, in my opinion, comes from the carrier’s “experience period” data they present. The experience period data will be 2 years before the “projection period” - the period for which the carrier is filing its rates. So when the 2025 Rate Review PUF is released, the experience period data will be for the 2023 plan year. The data is lagged because of the timing cycle for rate filings - initial rate filings are typically due in May or June, so carriers wouldn’t have much claims data for 2024 yet to use in their filings, while the 2023 plan year should be mostly complete, including claims run out. To come up with their 2025 rates, carriers take their 2023 data and project it forward into 2025. The rate review PUF gives us a treasure trove of data about the experience period: claims expense (both how much the plan paid and how much the member paid), premium, member months, state reinsurance, utilization (how many hospital visits, doctors visits, prescription fills, etc. the plan had) and risk adjustment transfers. Put it all together, and you can see how well a plan did and infer what the drivers of that might be. For most of these measures, this is down to the individual plan design level - so you can see not only what a carrier’s overall experience is, but also what their bronze, platinum, etc. experience looks like. This is my go-to PUF for statewide market analysis, but it isn’t perfect. First, as mentioned, it’s lagged by a year. Second, it’s also limited to carriers still planning to sell their products in the next year - so it sometimes misses some key data points. For example, the 2024 rate review PUF doesn’t have any data for Friday or Bright since both of those carriers exited the market by 2024, but these two carriers had a meaningful amount of claims experience for 2022. Luckily, there’s another PUF that can help fill that gap… the MLR PUF!

3) The Medical Loss Ratio PUF: this PUF has information carriers submit to CMS to show how they’re complying with the ACA’s Medical Loss Ratio (MLR) rule, which requires them to spend 80% of their premium dollars on medical claims (85% in the large group market). Like the Rate Review PUF, the data is a bit lagged: it’s usually released in the fall for the prior year (e.g. 2023’s MLR PUF will be released sometime this fall), but last year it was quite late - not until May of 2024 was the 2022 MLR PUF released. It’s useful because it includes all of a carrier’s medical business, including transitional or grandfathered plans which may not be reflected in the rate review PUFs. It also will include a few other nuggets like how much in prescription drug rebates a carrier received, how much they spend on admin, and how much they spend on certain taxes. However, the inclusion of non-ACA regulated plans like transitional and grandfathered plans can also muddy the waters depending on how you are using the data. It’s also all at the statewide level, and doesn’t have any plan design level information like the rate review PUF does. If I want to drill down to the county or plan level, there are two PUFs that provide some useful information.

3) This brings us to the Open Enrollment PUF. This is the PUF that typically provides the most recent information: it’s published a few months after OE ends and tells you about the current year’s enrollment. It doesn’t have any carrier-specific data, but it does have some county-specific data that’s sliced in lots of useful ways. It has total enrollment by county and ZIP - sometimes redacted for very small counties or ZIP codes… I’m looking at you, Talliaferro (inexplicably pronounced “Tolliver”) County, Georgia. This enrollment information is sliced by metal level, demographic (age, race/ethnicity, gender, etc.) and poverty level. You also get information about average premium at the county/ZIP level and average APTC. It’s useful for looking at market trends, and if you work at a carrier and have access to your own data, you can infer your market share.

4) Lastly, there’s the Issuer Level Enrollment Data PUF. This PUF provides enrollment data by issuer at the county level, and by plan design at the state level. It used to be lagged by a few years, but it’s been getting more up to date as of late: in June of this year, CMS published this PUF for plan year 2023 for both SBMs and the FFM. This is a great data source for looking at market share, and it tells you a lot about enrollment trends and which plan designs are attracting members. One thing that stands out to me: Even though there’s been a massive proliferation in the number of plan designs out there, enrollment still ends up concentrated in just a handful of plan designs. In every FFM state but Illinois, more than 10% of enrollment was concentrated in the top two plan designs. Even in Texas, which has 943 unique plan designs available across the state, the highest enrollment plan (an Ambetter silver plan) has 7% of the entire state’s exchange population enrolled in it. And, in almost half the state (15 out of 33), the top carrier had more than a 50% market share, and in all but two states (Florida and Ohio), the top two carriers combined had more than a 50% market share. One limitation is that it doesn’t include off exchange data, but usually this isn’t a major factor (except in Arkansas, where their Medicaid expansion uses off exchange QHPs).

The Nuts and Bolts: How I used the data

As you can see, there’s tons of info out there. This doesn’t include other sources like SERFF, NAIC filings, and state-specific PUFs produced by different State Exchanges. But it does give you an idea of the type and depth of data you can find. So how do we use this to make sense of the risk adjustment data? We can start by figuring out what the PMPM transfer is at the carrier level. This isn’t entirely straightforward, as there’s not a single PUF that consistently answers the question of how many total ACA-regulated (on and off exchange) member months a carrier had a given plan year. So CMS, if you’re reading this: consider sharing issuer level member months in the transfer report. Issuers will probably cry foul and say its proprietary, but most of that data is already out there or will be out there in one of the other PUFs.

Until they do that, we have to make some educated guesses, so in my analysis I went about it a few different ways. First, I decided to just focus on the individual market. Second, I established a hierarchy of data sources. The best is the Rate Review PUF, because it has actual member months at the carrier and subsidiary level. This only works for PY 2022 since the 2025 URRT data isn’t out yet. But if a carrier was still selling their product in 2024, I used this as a my data source. For those not selling in 2024, I then defaulted back to the Issuer Level Enrollment PUF, multiplying the average plan enrollment column by 12. I then normalized it to the number of member months For the 2023 data, it was a bit trickier. First, I used the issuer level enrollment PUF since it has 2023 member months, but this excludes off exchange plans, so as a backup, I used the 2024 Rate Review PUF. For plans that filed in 2024, they have to report their current enrollment in the URRT PUF, so I took this value and multiplied it by 12.

For both years, I then compared the resulting member months to the statewide total member months reported in the risk adjustment report (CMS doesn’t give you issuer level member months, but they do give you statewide member months). If it was within 5% or so, I just redistributed the difference pro rata to each remaining carrier. For larger discrepancies, I dug a bit deeper and made manual adjustments as needed - for example, in Arkansas, I went to the state Medicaid agency where they had reporting of enrollment in the off exchange Medicaid expansion QHPs and augmented the data from there.

As you can see, this is not an exact science, but it got me pretty close. Now, I’m ready to analyze the data with some real context! So in the coming days, I’ll look at some of the findings - in my next post, I’ll look at how risk adjustment tends to look different for different types of carriers. My plan is to divide them into Blues (other than Anthem/Elevance), publicly-traded carriers, provider-sponsored plans, and “other,” but would love any feedback on whether this is the right way to slice them. Should I look at the carriers whose primary business is Medicaid as their own category, irrespective of their ownership category. In other words, would it be more meaningful to look at Centene, Molina, AmeriHealth Caritas, and CareSource, etc. as one category, even though Centene and Molina are publicly traded, AmeriHealth is a subsidiary of a Blues plan, and CareSource would fall into the “other” category? What categories do you think are meaningful?

2023 ACA Risk Adjustment Transfer Report - Part 1

On Monday, July 22, 2024 CMS issued the 2023 ACA Risk Adjustment Transfer report - 3 weeks later than usual as a result of an extended submission deadline granted in light of the Change Health Care outage. The report is available here, and appendices can be found on the Premium Stabilization Page of the CCIIO web site.

This report provides information on the risk adjustment transfer receivables and payables for carriers selling in the small group and individual Affordable Care Act markets. The ACA risk adjustment program is zero sum - carriers receive or pay risk adjustment to each other within a state. Unlike Medicare Advantage, which is an upside-only risk adjustment program, carriers can either receive money from the lower risk carriers in the state, or if they are a lower risk carrier, they pay into the pool. The aggregate transfer of funds is $0 - this is not a direct taxpayer subsidy of the carriers, but a mechanism designed to change incentives for health plans.

Though these reports were much anticipated in the early days of the ACA when carriers had little information about the market risk, these reports make less of a splash ten years into the ACA. However, although we are ten years in, it seems a lot of folks who report on these results in the media (and many of the industry insiders they quote) don’t understand the significance. Articles typically frame the ACA receivables / payables as if receiving RA money is good and means you won, and paying RA money is bad and means you lost. But it’s far more complicated than that!

Evensun is doing a deep dive into the 2023 and 2022 report, and in this post, the first of a series, we explain what these numbers actually mean and how you should think about them. You can take a look at some of the data we pulled together here, but if you don’t know what these numbers mean, we’ll try to unpack it in this series.

In today’s post we’ll provide some background on risk adjustment and try to explain why looking at just the raw numbers alone doesn’t tell you much. In the weeks to come, we will explore some of the ways you can glean insights from the reports and what the data might tell us about the individual ACA market.

Why Risk Adjustment?

First, why does the ACA have risk adjustment to begin with? No such mechanism existed in commercial insurance before - but the guaranteed issue nature of the ACA creates a need that didn’t exist in the “underwritten” world. Before the ACA, an individual buying coverage could be denied if they had particular health conditions, which greatly reduced the risk of “adverse selection” where a carrier gets a non-random distribution of risk. Now that carriers must sell to any individual who wants a policy, carriers have to think about who might choose their plan over another plan. If a carrier gets a randomly selected set of membership, then the premium pricing is pretty easy - just figure out what the average health care costs are in your area and price accordingly.

Of course, people don’t make their health plan decisions based on random chance. There’s lots of considerations that go into it - like what providers are in network or what drugs are on the formulary. If you’re healthy, premium pricing is probably the biggest driver, but if you have health conditions you’ll be looking at other things. So, if carriers have to sell their plans to anyone who wants it, people with health conditions may make the very rational choice to purchase plans that have the better network, even if the plans are more expensive. Here’s where the problem comes in in a world without risk adjustment: all the healthiest individuals would purchase the cheaper plan with a narrower network, while the higher risk individuals would be likely to purchase the more expensive plan with a broader network. And likely, the premium pricing for the broader network plan wouldn’t be nearly high enough to compensate for the costs of getting higher risk members. In that world, carriers would have a strong incentive to try to only attract healthy individuals.

Fixing the incentives

Risk adjustment tries to fix that incentive - carriers can still try to target healthy individuals, but if their population is healthier than the other carriers in the state, they’ll have to pay those carriers to compensate for that risk. But this isn’t necessarily bad! Let’s look at some examples. If a carrier’s average premium is $500 per month, but they only pay out $300 in claims, that leaves them with a $200 gross margin, or a 40%. But the ACA’s Medical Loss Ratio rule also required carriers to spend at least 80% of their premium revenue on claims. In this scenario, then, the carrier would have to pay a $100 rebate to its members. However, if their population was lower risk than the market average, they might instead pay $100 into the risk adjustment pool, which would go to the carriers who had higher risk members.

Paying into risk adjustment doesn’t hurt the carrier in this example - it just redistributes money. Without risk adjustment, in the long run, the carrier would probably lower their premiums, but because of risk adjustment, the carrier will price their plans assuming that they’ll have lower claims expense, but knowing that they’ll also have to give back some of that premium money through the risk adjustment transfer.

The practical effect of this is that people who are healthier are paying higher premiums than they would for plans in a “no risk adjustment” world and people with health conditions are paying lower premiums than they otherwise would in a “no risk adjustment” world. Many of the critiques and challenges to the ACA have centered around this challenge - there’s no question that a young, healthy individual pays more for an ACA plan than they would have before the ACA - and that’s an intentional policy choice. Whether it’s the right one or not we’ll leave to the politicians.

The risk adjustment transfer amount alone, then, is pretty meaningless. If a carrier’s claims expenses are $100 million for the year, but their premium was only $80 million, they likely wouldn’t be happy with a $15 million risk adjustment receivable - they would have paid more in claims than they received in premium plus risk adjustment. On the other hand, if a carrier’s claims expenses were $65 million and the carrier’s premium was $120 million, if the carrier only had to pay in $15 million in risk adjustment transfer, they’d probably be thrilled - this might mean they can lower premiums in future years and expand their market share, because their medical loss ratio is below 80%

Now you know why the transfer alone doesn’t tell you much. So what does the report tell you? We’ll explore that in the weeks to come!