The Centers for Medicare and Medicaid Services (CMS) sends out its annual playbook for the ACA marketplaces in December or January or most years. The draft has a range of policy changes. Some are housekeeping. There are usually significant policy changes that CMS wants to do and needs public comment on. I just sent my comment letter in on three subjects that CMS is trying to address. CMS thinks that the choice environment is messy. They are proposing standardized plans as an add-in feature. I don’t think this will do much given the evidence we see in Washington State. CMS is also worried about automatically re-enrolling folks into dominated plans. This has been my most interesting research published in 2021. I think that CMS is mostly right but needs to think about networks more. Finally, CMS is proposing a significant model change to risk adjustment. I think that the proposed changes are orthogonal to all other policy goals of the Centers as I outlined in an earlier comment letter. There are a bunch of other things that CMS wants to do including changes to Special Enrollment Periods (SEPs) that I think invite adverse selection and insurer game playing, but I’m not commenting on those matters due to time and attention constraints.
My comment letter is below the fold:
Dear Secretary Becerra,
I am a health policy scholar writing in in regards to the proposed rule Patient Protection and Affordable Care Act: Benefit and Payment Parameters for 2023 plan year. My expertise is in the individual health insurance markets. My statements here do not speak for my employers, funders, or any other entity, organization or individual. I address the current literature on the following subjects:
- Dominated plan choice and automatic re-enrollment proposals that would change the hierarchy to place individuals into objectively superior plans.
- Proposed changes to risk adjustment.
- Meaningful difference and standardized plans
Dominated Plan Choice and automatic re-enrollment (155.335)
We solicit comments on whether factors such as net premium, MOOP, deductible, and OOPC should be reflected in a revised re-enrollment hierarchy for all Exchanges, with consideration for the potential impact of the actuarial value de minimis guidelines proposed in this rule at §§ 156.135 and 156.140 on cost-sharing. For example, HHS could consider re-enrolling a current bronze QHP enrollee into an available silver QHP with a lower net premium and higher plan generosity offered by the same QHP issuer. Additionally, HHS could consider re-enrolling a current silver QHP enrollee into another available silver QHP, under the enrollee’s current product and with a service area that is serving the enrollee that is issued by the same QHP issuer that has lower OOPC. We also solicit comments on additional criteria or mechanisms HHS could consider to ensure the hierarchy for re-enrollment in all Exchanges takes into account plan generosity and consumer needs beyond merely the retention of the most similar plan available.
I appreciate the creative and consumer-centric thinking that is embedded into this solicitation for comments. Dominated choice is common in the ACA marketplaces. Rasmussen and Anderson showed that dominated choice is expensive in terms of both premium and cost-sharing obligations relative to superior options on the Covered California marketplace.(1) Anderson, Rasmussen and Drake (ARD) simulated the effects of automatic re-enrollment under smart defaults with both the enhanced subsidies for the 2021-2022 plan year and current law subsidies that currently apply to the 2023 plan year.(2) They found 5.8% of Covered California enrollees are defaulted under current policy to a dominated plan with $100 per month in additional premium and almost $2,000 in additional deductible.
The ARD criteria to determine a smart default was a comparison between a current policy automatic re-enrollment plan and all other plans offered by an insurer in the service area. An individual was deemed to be defaulted to a dominated plan if all of the following criteria was satisfied.
- Same insurer
- Same provider network
- Same service area
- Same or lower net of subsidy premium
- Less cost sharing
- Improved metal level or CSR variant eligibility
The ARD criteria is more restrictive than the proposed criteria of “net premium, MOOP, deductible, and OOPC” as ARD also includes provider network considerations. Many insurers offer multiple networks within a service area. The choice of a provider network reveals critical information about a persons’ preferences and values when they made an active choice. This revealed preference should be respected in any future changes to the hierarchy.
Additionally, the differences in out of pocket spending is a function of both an individual’s health care utilization, provider reimbursement level, and benefit design. Assuming the changes to de minimas variation that are also discussed elsewhere in the proposed rule are fundamentally adopted as proposed, the differences in outcomes within metal level are highly variable. I strongly recommend that the Department do not make changes in the automatic re-enrollment hierarchy for within-metal level changes.
Risk Adjustment
- Repeal of Risk Adjustment State Flexibility To Request a Reduction in Risk Adjustment State Transfers (§ 153.320(d))
I am supportive of the proposed rule to repeal state flexibility to request additional reductions in risk adjustment transfers. States that believe that there is a significant flaw or imbalance in their local markets always have the option to operate their own risk adjustment program which is where these flaws should be precisely corrected. Large, across the board reductions merely create incentives for insurers to select or screen for favorable or unfavorable risk.
3. Risk Adjustment (§§ 153.320, 153.610, 153.620, 153.700, 153.710, and 153.730)
Beginning with the 2023 benefit year, we propose the following model specification changes to the HHS risk adjustment models: (1) To add a two-stage weighted model specification to the adult and child risk adjustment models, (2) to remove the existing severity illness factors in the adult models and add interacted HCC counts factors to the adult and child risk adjustment models, and (3) to revise the enrollment duration factors for the adult models. By prioritizing simplicity and limiting the number of changes to the current model structure, we minimize administrative burden for HHS, and as HHS runs risk adjustment in all 50 states and the District of Columbia, we do not expect these policies to place additional burden on state governments. These proposed model specifications would result in limited changes to the number and type of risk adjustment model factors; therefore, we do not expect these changes to impact issuer burden beyond the current burden for the risk adjustment program. (393) To further assist issuers in understanding the potential impact of these changes on risk adjustment transfers, we released the 2021 RA Technical Paper and conducted an EDGE transfer simulation that estimated the impact on risk scores and transfers with and without these proposed changes using 2020 benefit year risk adjustment data. (394) Based on results from this simulation, we estimate the impact of these policies on risk adjustment transfers to be relatively minor. (395)
I would like to refer to my comment letter submitted to the Centers for Medicare and Medicaid Services on November 21, 2021 with reference to the October 26, 2021 HHS Operated Risk Adjustment Technical Paper on Possible Model Changes. I am attaching the comment letter as Appendix A. The key point is that risk adjustment serves a specific purpose: to reduce and mitigate the effects of adverse selection incentives.
As you are well aware, risk adjustment serves to reduce insurer incentives to risk select. Risk adjustment is a group level adjustment to gross revenue of an insurer so that the net revenue should, on average be sufficient to make insurers risk agnostic.(3) All risk adjustment programs are, at the individual level, imperfect. Residuals will be created where individuals with certain diagnostic categories will have notably lower or higher actual expenditures relative to the risk adjusted predicted expenditure. These residuals can create incentives for insurers to either actively seek out and select risk or screen for risk if and only if these residuals are both reasonably predictable and of significant magnitude to be worth the administrative investment.(4–6)
The two stage weighted model specification, as described in Chapter 2 of the technical paper is designed to improve prediction for the least expensive enrollees. This is not a worthwhile goal of risk adjustment model improvements in the current operational, policy and legal environment. Elsewhere in the proposed rule, the Department has expressed significant concerns about network adequacy (156.230) and de minimas variation for levels of coverage (156.140). The markets have seen a proliferation of narrow networks and lower actuarial value plans since its inception. The marginal buyer of insurance is fundamentally feature insensitive and purchasing insurance only on premium.(7) Current incentives for insurers are to aggressively compete for individuals with no HCCs by offering the lowest potential premium possible on networks that may be extremely narrow and pragmatically non-functional, with restrictive formularies and gatekeeping requirements to suppress utilization. Further optimizing risk adjustment to transfer more money, or more relevantly, minimize transfers from no HCC members to HCC members will exacerbate these other policy considerations.
The superior option is the interacted HCC count models discussed in Chapter 4 of the technical paper. High risk enrollees are both highly expensive, and identifiable in claims data. Insurers can identify members and potential members who are likely to be, net of risk adjustment and reinsurance, not profitable and avoid these members by offering regulatory adequate but pragmatically non-functional networks, excessive administrative burden of claims denial and prior authorization mazes and strategic formulary changes among other tactics. Improving risk adjustment so that high risk members are profit agnostic for insurers will lead to a more competitive and functional market.
Meaningful Difference and Standardized Plans
I applaud the recognition by HHS of the challenging choice environment which consumers face. My research suggests that 45% of consumers that used Healthcare.gov were exposed to at least fifty choices in 2021.(8) Recent research by ASPE has shown even longer menu lengths in 2022.(9) Choice quality diminishes as the choice menu lengthens. Researchers in the Medicare Advantage space found significant choice quality degradation after fifteen choices.(10)
However the proposal to add standardized plans will not address the menu length problem. Research from Washington State’s experience with Cascade Care showed that standardized plan designs, in and of themselves, do not lead to shorter menu length.(11) The currently proposed standardized plans will not be price competitive relative to plans that have de minimas allowable actuarial value that are using the same network, plan type and offered by the same insurer.
An improved meaningful difference regulation is possible. Assuming that the incremental consumers are purchasing primarily on the basis of net premium, the department should be humble in its regulatory approach to improve the choice space for consumers. An alternative rule should be that, within any metal level, insurers may offer any number of options as long as the Essential Health Benefit component of each premium is at least 3% different than any other plan offered by that singular HIOS ID. This will reduce the proliferation of “silver spamming” and give consumers truly meaningful difference on the measure that they can interpret; premium. At the same time, if insurers believe that there is explicit value in offering silver plans that are identical in cost-sharing and plan type but are on wildly different networks, they can do so.
Sincerely,
David Anderson MSPPM
Appendix A
I am an individual health insurance policy researcher with significant expertise in the operations of the ACA individual health insurance marketplace. My research has focused on automatic re-enrollment,(2,12) dominated plan choices,(1,2) consumer responses to zero premium health plans,(13,14) plan availability,(15) and information availability and impact in these markets.(16–18) Risk adjustment is critical to all of these aspects as risk adjustments shapes profitability possibilities and thus strategies of insurers.
As you are well aware, risk adjustment serves to reduce insurer incentives to risk select. Risk adjustment is a group level adjustment to gross revenue of an insurer so that the net revenue should, on average be sufficient to make insurers risk agnostic.(3) All risk adjustment programs are, at the individual level, imperfect. Residuals will be created where individuals with certain diagnostic categories will have notably lower or higher actual expenditures relative to the risk adjusted predicted expenditure. These residuals can create incentives for insurers to either actively seek out and select risk or screen for risk if and only if these residuals are both reasonably predictable and of significant magnitude to be worth the administrative investment.(4–6)
Risk adjustment should only be used to correct for anticipated adverse selection as outlined in Section 1.1.2 “The purpose of the risk adjustment program is to reduce the influence of risk selection on plan premiums as well as to reduce the incentive for plans to avoid enrolling higher-than-average risk enrollees.”
Adverse selection is fundamentally a challenge of information asymmetry.(19) Acute, unanticipated events are not subject to adverse selection. Only when an individual has knowledge of a disease conditions and severity of a given condition at the point of the decision to purchase or not purchase health insurance does the possibility of adverse selection exist. This perspective is in conflict with the inclusion of acute medical events like HCC-02 which can drive up significant costs (p.21)
The most expensive enrollees tended to have severe acute illness HCCs. These enrollees were often hospitalized, received ICU care, and were frequently among individuals exceeding the $1 million high-cost risk pool claim threshold.47 For example, we found that 50 percent of enrollees reaching the $1 million claims threshold have HCC 2 (Septicemia, Sepsis, Systemic Inflammatory Response Syndrome/Shock).
Very few, if any, insurance purchasers have pre-knowledge that they will be septic in the upcoming policy year. Including acute HCCs in risk adjustment rather than re-insurance or high cost risk pooling systems perverts the purpose of risk adjustment to minimize adverse selection. Any other policy objective should not be pursed by risk adjustment. Other tools such as reinsurance and regulation should be utilized in these circumstances.
CMS should consider implementing the interacted HCC counts and HCC-contigent EDF model as discussed in Chapter 4 while abandoning efforts to improve model fit for non-HCC enrollees if there is any trade-off in model accuracy for individuals who drive the vast majority of plan claims liability.
Improving the Predictive Accuracy for the Very Highest Risk Enrollees
Chapter 4 is critical to the functionality of the markets. The highest risk individuals are likely to have high variance in medical spend relative to averages that are derived from fairly small samples. A few outliers can significantly alter the mean. Outliers, either positive or negative, are often fairly predictable from the combination of claims history and non-claims clinical information. In the context of hemophilia, outlier status can be predicted with a high degree of reliability if an individual beneficiary has documented inhibitions to blood clotting factor infusions. From this reliable predictor, insurers will alter strategies to avoid individuals with high net of risk adjustment and catastrophic high cost risk pool payment costs. If only one chapter is to be improved upon, it is critical that it is this chapter.
“enrollees with 7 or more HCCs account for only 0.2 percent of adult enrollees, they make up more than 8 percent of adult silver plan liability”
One factor that may be relevant to the exponential growth of costs for individuals with numerous HCCs and serious illness is that plans selected by individuals with known high cost, high complexity chronic illness may differ from plans that are selected by individuals with low expected medical utilization. Recent research has found in California, using the IHA claims data warehouse, that individuals with serious illness history were far more likely to choose PPO plans, which have out of network benefits and potentially more expansive and expensive provider networks than individuals without serious illness.(20) Some component of the differences in cost may be a matter of the price level per unit of service in the networks that seriously ill patients select relative to the lower unit prices in networks that are attractive to low utilizing no HCC members. Broad networks and PPOs are likely to be more attractive to seriously ill individuals and these plan features are likely to be more expensive on a per unit basis due to either unique attributes of the star hospitals or the lack of a credible threat by the insurer to exclude high cost providers.
The interaction terms for severe illness and transplant history status are a logical, and reasonable adjustment to the model. Significant level and number of illness can produce clinically meaningful complexity of care.
P.58 highlights the model improvement:
In particular, we found that the adoption of the proposed interacted HCC counts and proposed HCC-contingent enrollment duration factors approach in the adult models would improve prediction for enrollees at the highest percentiles of plan liability, particularly in the 10th decile, 5%, 1%, and especially 0.1%. For example, using the adult silver plan model in the 2018 enrollee-level EDGE dataset, the adoption of the proposed interacted HCC counts and proposed HCC-contingent enrollment duration factors improves the PR for the top 0.1 percent of adult silver plan enrollees from 0.91 to 0.98 and the PR for enrollees with 10 or more HCCs from 0.83 to 1.00.
While I have reservations with the use of the predictive ratio (PR) method of model assessment as discussed below, improving model fit for individuals at the right hand tail of the distribution is a critical policy goal.
Policy Recommendation: Continue to improve risk adjustment for the individuals with high HCC counts and focus model improvement efforts on these populations.
Appropriateness of Predictive Ratio Measures
Section 1.4 makes the claim that the appropriate measure of model performance is the predictive ratio measure:
The predictive accuracy of a risk adjustment model is typically evaluated using predictive ratios (PRs), calculated as the ratio of predicted to actual weighted mean plan liability expenditures. The predictive ratio represents how well the model has done on average at predicting plan liability for that subpopulation. If prediction is perfect, mean predicted expenditures will equal mean actual expenditures, and the PR will be 1.00.
I disagree that this is a pragmatically useful measure. Insurers are profit seeking and will seek to attract members whose net of risk adjustment payments and premiums are greater than realized costs and insurers will use multiple mechanisms such as network design and formulary design to minimize enrollment of individuals whose net revenue is less than realized costs.(4,6,21) As we know, the spending distribution in the United States for medical care has an extreme right hand skew.(22) The modal individual in the United States has no reported claims while the average spending for individuals in the bottom half of the US national healthcare expenditure distribution is under $500 per year (<$42 per member per month (PMPM)). The top decile of spending per MEPS in 2014 had a mean of $31,203 (~$2,600 PMPM). Some of these costs are from one-off acute conditions such as an extraordinarily complicated pregnancy, or prescription of a Hepatitis-C antiviral. However, many high-cost individuals are persistently high cost over time and thus are predictable and potentially avoidable by unscrupulous insurers. (23)
Per data from ongoing work in progress, the first six deciles of HCC scores are composed of individuals who have no HCCs and thus their risk score is composed of only metal specific demographic co-efficient. These individuals are likely to have low costs. An individual with $200 in expected expenses and $400 in actual expenses will have an individual predictive ratio of 0.5. The risk adjustment system would be underpaying this 0 HCC individual by $16.67 PMPM. If there is significant signal in claims, enrollment, geographic and external data, insurers may balance the cost of the risk adjustment miss with the cost of strategically avoiding enrollment individuals who are statistically similar enough to this individual. However, this is a broad category and external predictors are highly unlikely to be specific and actionable enough for insurers to select this precisely.
However, if an individual has hemophilia (HCC 066) (2021 silver HCC co-efficienct 69.705 and average national premium in 2020 of $578/month) their expected annual risk adjustment transfer before geographic and actuarial value factors is $483,474. Hemophilia has highly variable costs. Some individuals will have a good response to factor replacement therapy and good luck so that their realized costs are low. However, some individuals with the same incremental risk adjustment value will have inhibitors to standard treatments and could readily cost millions of dollars.(24) Even excluding the extreme example of an individual with multiple million dollar months, an individual with hemophilia who experiences $966,947 in claims has the same predictive ratio of 0.5 as the very low cost individual in the paragraph above.
P.21 notes the underprediction of the highest cost individuals:
Very Highest-Risk Enrollees. The current models also underpredicted plan liability for the very highest-risk enrollees (that is, those in the top 0.1 percent risk percentile and those enrollees with the most HCCs). As seen in Figure 1.2 above, the current models underpredicted adult silver plan enrollees’ plan liability in the top 0.1 risk percentile by 9 percent.
A single high cost individual or a small cluster of high cost individuals may be readily predictable through the combination of claims and non-claims data.(6) Given the large swings in profitability of an insurer successfully identifying the small cohorts who are likely to be significantly underpredicted within a group that has high costs, the incentives to screen by network exclusions, formulary exclusions or hassle costs are high and actionable.
The predictive ratio is scale-less. It equally weighs small misses in overpayments and small misses in underpayments. It equally weighs misses in the same direction of the same percentage basis on a small PMPM base and a large PMPM base despite the fact that the dollar value of the miss could differ by orders of magnitude. The predictive ratio has is a group level metric where the average of the entire group is considered even through individual level experience within the group may be highly variant.
P.29 reinforces this incorrect view:
Overall, we considered this to be an acceptable trade-off because across all age and sex factors, most PRs were within a tolerable threshold of +/– 5 percent (e.g., 0.95 to 1.05) and as seen Figure 2.5, the two-stage weighted approach had the major benefit of more accurately predicting the age-sex factors for the enrollees without HCCs, which is a much larger population than the enrollees with HCCs
This view prioritizes enrollment count weight rather than claims incurred weight. Risk adjustment should prioritize claims incurred weights rather than enrollment weights. The use of Mean Absolute Error in chapter 5 is a modest improvement to model appropriateness.
A better metric should be used. And we have such a metric. We can assess the PMPM standard deviation of each percentile or subpercentile slice and then seek models to minimize the standard deviations for the entire population. This is a model which will place more attention at accurately predicting or at least minimizing the absolute variance for high cost and high HCC scoring individuals.
Policy Recommendation: Adopt a measurement process that incorporates information at the individual level and reflects the PMPM difference in the cost of an error at various points in the HCC and expenditure distribution.
No HCC Enrollees and Lowest Risk Enrollees
Chapter 2 of the white paper is concerned about predicting risk for individuals with no HCC co-efficient other than age-sex demographic characteristics and enrollment duration factors. I think that this is misguided.
Risk adjustment should only be concerned about mitigating adverse selection. Adverse selection is the presence of information on one side of the contract and concealment of that information to the other side of the contract. Individuals with full year enrollment, no HCCs, much less individuals with no chronic HCCs have no information to conceal. Individuals with no HCCS but short enrollment spans may have hidden information. This may lead to adverse selection which may justify risk adjustment as a mitigating policy.
More importantly, CMS has identified significant concerns about narrowness of networks, significant pre-authorization requirements, and the high cost sharing of specialty drugs in other documents. These are symptoms of a risk adjustment system, combined with a price linked subsidy system that currently rewards insurers for minimizing premium by taking actions to actively avoid risk and claims. Marginal active shopping enrollees are highly likely to purchase plans only on the basis of premium.(7) If CMS is to take further steps that further make low risk enrollees more profitable by increasing their relative weight in risk adjustment, insurers will reasonably respond to this market signal by further narrowing networks, and designing plans that are fundamentally repugnant to individuals with significant risk all in the quest to lower premiums.
The policy justification offered in the white paper to increase relative HCC scores for individuals with no RxC or HCC factors is also misguided:
By addressing the underprediction of costs associated with lowest-risk enrollees (enrollees without HCCs and low-cost enrollees) in the risk adjustment models, we expect to encourage retention and entry into the individual and small group (including merged) markets by plans that enroll a higher proportion of this subpopulation of enrollees.
In 2017 and 2018, insurers left the marketplaces due to the combination of significant accumulated losses and policy uncertainty.(25) That trend has notably changed.(15) Insurers have returned to the marketplaces. The combination of silver loading and the temporary decrease in applicable percentages for low income enrollees has dramatically reduced the net premiums that marginal, healthy buyers face.(26,27) Many individuals are now exposed to zero premium Bronze plans.(13,28) Low net premiums are relevant to individuals with low risk as they are fundamentally benefit design indifferent. Insurers have reported low MLRs for the 2018-2020 time period and profitability is not a current concern.(29)
Figure 2.3 is extraordinarily concerning. The two-stage weighted model markedly improves predictive value for individuals with 0 HCC who have low PMPM while making the predictive ratio no better and often marginally worse for individuals with two or more HCC. Individuals with two or more HCC have significant claim dollar weight. Table 4.1 shows that the bottom 7 deciles of risk scores constitute ~16.3% of total claims liability. Adjusting the models to shift resources to these populations that are primarily purchasing plans on the basis of premium will further encourage insurers to compete by avoiding risk even more aggressively.
Enrollment Duration Factors
While we are not certain why enrollment duration is only inversely related to monthly costs among adults with HCCs, the most plausible hypothesis is that medical treatment for many HCC diagnoses likely has a “fixed cost” element that does not vary with the number of months of enrollment.
There may be two factors at work that may explain the observation that very short duration enrollments with HCCs may have higher PMPM in addition to the very logical proposal that most medical expenses are incurred in short time windows.(30) First, many individuals may be automatically re-enrolled into plans that in the first year had zero net premium and then a positive sum net premium in the second year. This may create an administrative friction that leads to disenrollment after medical expenses are incurred in the first month. (14) Secondly, the inclusion of acute conditions in the model may dissipate some of the enrollment duration factors as these acute conditions may absorb most of the variance that the model needs to make a stable estimate.
Common Data Resource
In section 3.4 Obtaining Diagnosis Codes for Partial-Year Enrollees there is a concern that insurers have differential abilities to collect and submit relevant diagnosis on the basis of enrollment duration interacting insurer sophistication, size or business model. CMS may be able to lower the barrier to entry to markets by creating a list of individuals with truly chronic conditions (HCC-018 for instance) where insurers will receive credit for the medical history of individuals with these conditions even if claims are not submitted. This would require significant revamping of the data sharing systems and would not immediately lower barriers to entry nor immediately increase competition but over the long run, assuming privacy concerns can be addressed, this would improve the markets’ functionality.
Scale of Administrative Carve-outs
Per the 2018 NBPP:
Reducing the statewide average premium: We finalized a 14 percent reduction to the statewide average premium in the state payment transfer formula, beginning with the 2018 benefit year, to reflect the portion of administrative costs that does not vary by claims
Insurers have distinct business models and plans that vary in scale. Large insurers have higher degrees of administrative complexity and control than insurers with low membership. Some elements such as exchange user fees and risk adjustment fees are constant across all insurers. However, other operational considerations such as the cost of maintaining a claims payment system or contracting such a system out to a third-party vendor will vary by insurer size and sophistication. The reduction in state wide average premium may be disproportionally beneficial to insurers that seek to minimize their exposure to risk by narrowing networks, instituting prior-authorization requirements and offer restrictive formularies as well as offering low premium plans with the lowest allowable actuarial value for each metal band.(31)
Policy Recommendation: Re-analyze the size and scope of the reduction of average state wide premium by insurer size and market competitiveness. Adjust this on a state or regional basis as justified by the re-analysis.
Risk Adjustment and Section 1332 Waivers
One area that was not mentioned in the white paper is the interaction of risk adjustment and reinsurance waivers. This is a critical challenge as Section 1332 reinsurance waivers have proliferated since the initial approvals for the 2018 plan year.(32) Reinsurance has been critical in reducing gross premiums.(33,34) States have adopted several methods. Most notably some states have adopted disease specific models where the reinsurance pool pays claims for individuals with certain, pre-specified diseases that are likely to be high cost. This model reduces right hand tail risk. Most states that have adopted a reinsurance waiver has instead used a caliper model where the state reinsurance funds are used to pay some percentage of claims between an attachment point and a ceiling. Both of these models likely cover significant portion of costs that are currently accounted for in the risk adjustment model and this may lead to double counting.
CMS has partially recognized the challenge of double counting with the operation of the national high cost catastrophic reinsurance program where CMS pays a portion of an individual claims in excess of $1,000,000. CMS has truncated the incremental value of these claims in the calculation of disease category coefficiencts so that insurers are compensated for the portion of risk that they bear rather than the gross risk.
However with state reinsurance programs, there is no state specific models which accounts for the portion of predictable high cost claims that the insurer receives credit for in terms of risk adjustment transfers but also receives funding from the state reinsurance pool. This problem is likely to be particularly acute in states, like Colorado and Georgia,(35) with multiple reinsurance models operating concurrently over various sub-state geographies.
Policy Recommendation: CMS should require states that operate 1332 reinsurance programs to adjust disease specific coefficients to minimize double credit of both risk adjustment and reinsurance payments.
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sab
This stuff is so far over my head I had to look up ‘orthogonal’. But it still affects people even if we don’t understand it. Thanks.
Lobo
Please explain how this works and is better than standardized plans:
An alternative rule should be that, within any metal level, insurers may offer any number of options as long as the Essential Health Benefit component of each premium is at least 3% different than any other plan offered by that singular HIOS ID.
Thanks
Eljai
Thanks David. I’m going to have to peruse this one. Even though you decided not to comment on proposed changes to SEPs, I’d be interested in hearing your further thoughts on that at some point, especially if they go ahead with any changes.
@sab: Me too!
David Anderson
@Lobo: If the policy problem that CMS thinks it needs to address is menu length and choice complexity, there are a couple of options.
I think politically, there is no chance in hell of a full California and low chance of standard plan plus one or two non-standard variants on Healthcare.gov. A few states have Standard + a few and that reduces choice length. I think that #4 AND #5 are nearly effectively the same as the Obama era regs were very easy to get around and introduce 13 plans priced within 10 bucks of each other from the same insurer. It involves a de minimas amount of creativity but it is no big deal.
We have pretty good evidence that the marginal buyer is buying almost entirely on premium. So let’s make premiums meaningfully different instead of just adding another plan design that makes the choice menu longer or creating regulations that can be easily gamed.
StringOnAStick
@David Anderson: People, especially people operating without a huge amount of knowledge on something, always tend to buy based on price. It doesn’t matter what the item is it seems, but something as opaque to most people as health care and health insurance and being something they hope to not have to use, seems especially likely to be chosen on price alone. Excellent idea you have here and thanks for spending the time to send such a well reasoned missive to TPTB.
Lobo
Thanks! Your proposal has premiums roughly correlated to the quality of the plan. If I have that right, then by behavioral economics makes it makes it a much more straightforward approach.
David Anderson
@Lobo: I’m iffy to use the word “quality” as that can mean many things… but yeah, within the same insurer/network for the same person in the same zip code, premium is mostly going to be correlated to benefit richness with potentially residuals due to different incentive effects.
More details tomorrow or Thursday.