A top-down cost-allocation model averages out all of a hospital’s expenses based on its activities. This type of model provides a more accurate reflection of a hospital’s expenses when defining package rates. In this short film, Lalit Baveja provides a primer on top-down cost-allocation and discusses a Milliman project for the state government of Meghalaya, India, that used the methodology.
Read more about how Milliman consultants assisted India’s Meghalaya Health Insurance Scheme (MHIS) set up a top-down cost-allocation model here.
To read the video transcript, click here.
A top-down cost-allocation approach may help developing countries set appropriate bundled rates for providers to participate in universal healthcare coverage. Such an approach focuses on averaging the costs of current utilization and actual expenses for hospital groups. One advantage of this practical approach is that it is feasible in situations with limited data.
In this new paper, Milliman consultants discuss their experience utilizing this top-down approach under India’s Meghalaya Health Insurance Scheme (MHIS). The following excerpt highlights the scheme’s objective:
In its first phase of rollout, the Meghalaya Health Insurance Scheme (MHIS) had limited benefits. The government wanted to expand its scope to better serve the population by providing a wider breadth of procedures, including tertiary care specialist procedures in oncology, neurosurgery and cardiac surgery. However, to make its second phase a reality, the Meghalaya scheme needed greater participation by private healthcare providers offering such specialist services. The state needed to offer realistic pay rates to private healthcare providers to attract participants.
Milliman helped the state identify the potential demand and gaps in benefits by conducting an extensive review of hospital utilization data, publications about disease burden and disease registries in the state. This was the basis of recommendations for additional surgical procedures that needed to be included in the scheme to ensure comprehensive coverage.
Milliman was asked to develop indicative prices for recommended additional surgical procedures under expanded benefits. To determine rates, Milliman used a top-down cost-allocation approach to estimate the cost of each procedure, using local hospital utilization and financial information. We developed specific tools to collect data from a representative group of hospitals.
Here are the outcomes and important considerations:
Using the top-down costing approach, we were able to estimate the costs of the following:
• Per-bed-day department cost for the five hospitals in the study
• Cost of 20 common surgeries in MHIS Phase I as a reference point for comparison with existing package rates
• Cost of 160 surgical and 20 medical conditions for tertiary care benefit expansion in Phase II
Developing the final package rates involves additional parameters, making adjustments for inflation trend, capacity utilization, quality, profit margins and specific variations among the participating hospitals. MHIS will need to apply various adjustments for these parameters to arrive at the final cost of each procedure for the social insurance scheme.
If providers are not keeping reimbursements in line with their expenditures to manage a clinical condition, there will be a tendency to pass on the shortfall to the members and deny or avoid admissions for procedures, potentially compromising the quality of care. This makes it critical that frameworks for costing are regularly updated. These frameworks also need to seek wider participation from providers. Apart from recurring medical inflation, wider provider participation and cost impact of new practices should be consolidated in updates.
A new article in Modern Healthcare looks at the Healthy Indiana Plan, a Medicaid expansion program that has yielded some interesting results. Here’s an excerpt from the Modern Healthcare piece:
While the jury is still out on how well the health savings account and preventive-care incentive are working, analysts have looked at utilization trends among the newly insured and found that those signing up for the program are sicker and more frequent users of healthcare than those enrolled in commercial, employer-sponsored health plans.
The Healthy Indiana Plan “population used more care than the typical commercial population in Indiana with the same age and gender characteristics,” says Rob Damler, principal at Milliman, a consulting and actuarial firm. Damler is the consulting actuary to the state of Indiana on the health plan.
Childless adults enrolled in Healthy Indiana, for instance, had nearly three times as many inpatient services as private plan members in the first year. And pharmacy use was nearly 50% higher than a typical commercially insured population.
This newly enrolled group was also sicker than the general population. Their relative morbidity was 65% greater than their peers covered by private health insurance. The earliest enrollees to the program also proved to be the sickest, with the highest healthcare costs, Damler says.
This phenomenon is called anti-selection, where the least healthy population seeks healthcare coverage available to them, driving up the costs to insurers and the population covered.
The Healthy Indiana Plan offers some considerations for national reform, Damler says. “One of the issues that needs to be understood is pent-up demand,” he says. “We need to be prepared that the newly insured may cost more in the first 12 to 24 months than the insured population.”
Not surprisingly, insurance companies say that without a federal law requiring everyone to carry health insurance, national healthcare reform won’t work because the chronically ill will sign up for coverage in large numbers, driving up costs, while the healthy will stay on the sidelines.
“It only works if everyone’s covered,” says Alissa Fox, senior vice president of policy at the Blue Cross and Blue Shield Association.
What follows is excerpted from the new healthcare reform briefing paper by Tom Snook and Ron Harris, “Adverse Selection and the Individual Mandate.”
The idea behind a coverage mandate is to mitigate (or, ideally, totally eliminate) the effects of adverse selection on health insurance costs. If that mandate is so weak as to be ineffective, however, adverse selection will continue to be an issue and health insurance costs will increase as illustrated in the following example.
Consider a potential insurance population comprising three categories: Very Healthy, Moderately Healthy, and Unhealthy. For illustration’s sake, let’s say these groups have the following population sizes and expected average annual healthcare costs:
Average per capita healthcare cost
Let’s also say that a strong mandate existed and all 1 million of these lives would be enrolled into the health insurance pool. In this case, the average per capita healthcare cost would be $1,900. But under a weak mandate, the Very Healthy category has less of a financial incentive to participate, and would be more likely to opt out from coverage. The Unhealthy category still has an incentive to participate because of the relatively high costs it expects to have. If, for example, a weak mandate will cause only 50% of the Very Healthy, 80% of the Moderately Healthy, and 100% of the Unhealthy to enroll, then the average per capita cost of the resulting insured population is more than $2,400—27% higher than the strong mandate scenario.
It should be apparent from this example that the relative strength or weakness of a coverage mandate could best be measured by how many of the Very Healthy potential insureds wind up actually enrolling for coverage. The more healthy lives there are in the insurance pool to help bear a share of the costs, the lower the average cost for everyone.
Click here to read the full paper.
What follows is excerpted from the new healthcare reform briefing paper by Tom Snook and Ron Harris, “Adverse selection and the individual mandate.”
Community rating refers to a health insurance premium rating structure with limited or no variation in the premium rates among insureds. Under community rating requirements, health plans have a reduced ability to vary premium rates so as to be consistent with an individual’s risk characteristics, such as age and gender. Current industry practice in the individual and small group markets is to develop premium rates commensurate with an individual’s actuarially expected costs; for example, younger people have lower rates than older people. A community rating requirement would limit the degree to which a carrier can do this. Limiting the range of rates means raising the lower end and reducing the top end of the rate scale, so that rates are no longer proportionate to expected costs. This creates a cross-subsidy where younger individuals pay more for health insurance to reduce the premiums for older policyholders. The fact that community rating requirements will make insurance more expensive for younger and healthier individuals could serve to undermine the efficacy of the mandate, especially if the mandate is not highly aggressive in terms of penalties for non-compliance.
Click here to see the full paper.
What follows is excerpted from the new healthcare reform briefing paper, “Adverse Selection and the Individual Mandate.”
The purchasing or enrollment decision that an individual makes when deciding whether to obtain health insurance coverage and, if so, what plan of benefits to select, typically represents an exercise of consumer self-interest. It involves consideration of anticipated personal or family needs, price, doctors and hospitals available, other benefits or services, health plan reputation, and various other factors. Adverse selection is the natural process of individuals making insurance purchasing decisions that reflect their own personal circumstances and healthcare needs and desires. Such decisions are generally informed ones, leading to maximization of the cost/benefit tradeoff; and the decisions that maximize this tradeoff favorably for the individual consumer generally have the opposite impact on the insurance program (i.e., lead to higher costs relative to the premium level charged). In recognition of this informed consumer behavior, insurers have developed time-tested underwriting and rate-structuring techniques for mitigating and managing the resulting healthcare risks and costs.
A selection spiral is a worst-case result of adverse selection that can quickly make an insurance program insolvent. The dynamics of a selection spiral work like this: A health plan gets worse risks (higher-cost individuals) than it anticipated in its original rate setting, and so has to increase premium rates to provide adequate revenue to cover these higher costs. However, raising the rates changes the entire cost/benefit equation, and so the rate increase will cause some individuals to drop their coverage—and those who do drop are more likely to be the lower-cost individuals in the pool. As a result, the health plan winds up with a pool of risks even worse than the one it started with, with premiums that again need to be increased to cover the new, higher costs. This sort of spiral can quickly get out of control and lead to the collapse of the insurance pooling mechanism.
Click here to see the full paper.