Optimize MA product design by reducing information gaps and execution complexity

The Medicare Advantage (MA) market is growing rapidly and ever more competitive. In 2022 the average Medicare beneficiary had access to 39 MA plans—creating an environment wherein health insurance companies must be more creative in how they compete and differentiate their products. One of the key levers they can pull is to tweak product design to reduce the cost to MA members or add supplemental benefits that cover healthy food, transportation to medical appointments and home care. Benefits once seen as novel and extravagant are now considered table stakes in the drive to attract new members. The challenge is that plans have limited rebate dollars to fund supplemental benefits and must go head-to-head with carriers introducing an ever-increasing list of benefits.

This situation calls for a higher degree of benefit package optimization for health insurance companies working to design plans that are differentiated—from a supplemental benefits perspective—and financially responsible. In this article, we analyze how reducing information gaps and execution complexities help to optimize MA product design.

Close information gaps

Plan design becomes easier with information on what the ideal geographical footprint for a plan should be, the lift in membership a new benefit might produce, the expected utilization of a specific benefit and the improved health outcomes from including a specific benefit. Health plans can develop and operationalize advanced analytics models to help answer these questions to reduce the information gap.

The key gaps or questions plans typically must address include:

• Identifying the next-best county. For many health insurance companies, the first step in the product design process is to determine the geographic region for expansion during the benefit design period. Carriers can identify the next-best geography or county by building an advanced analytics model based on the past performance of similar plans in the first three to five years following launch. The model can then be refined by layering in plan-specific information on the plan’s ability to build provider networks and proportion-of-target-population segments in the new geography.
• Identifying the next-best benefit. Three pieces of information are essential to gauge whether a benefit should be added: 1) estimated utilization; 2) estimated membership lift; and 3) estimated impact on health outcomes. A combination of internal and third-party claims data, past data on benefits and enrollment and advanced analytics can help to bridge information gaps in these areas.
• Predicting competitor moves. While knowing the entirety of a benefits plan competitor’s launch is improbable, understanding the direction of key plan parameters such as premiums, out-of-pocket costs and the prevalence of supplemental benefits, especially the ones that are offered more commonly or gaining traction, might suffice. Simple trending models built on plan benefit data reported by the Centers for Medicare & Medicaid Services (CMS) can yield good insights on competitor or market trends to support plan design decisions.

Reduce execution complexity

While more time ideally would go into analyzing and designing plans than executing the plan design process, that often is not the case. For larger plans, the number of bids to assess and redesign is an operational challenge, particularly when accounting for the overhead of entering data into CMS bid pricing tools and the documentation required to publish those plans.

Here are a few ways to streamline execution:

• Conduct a benefit package health check. Assess existing plans to see if and where a benefit package change is warranted. This health check process can be automated by creating a dashboard to assess key performance parameters such as CMS Star Ratings, medical loss ratios, membership growth performance and product share at the bid level. The resulting score can help product designers more readily identify plans that require attention. The scoring mechanism also will help them learn from plans that are performing well and apply those learnings to the plans that need adjustment.
• Evaluate plan scenarios. Health plans can either build or buy simple scenario planning tools that provide side-by-side analysis and assist in decision-making. These tools can help carriers with plan design by modeling variables such as benefits combinations, out-of-pocket costs and allowed frequency of services and by quantifying their impact on key outcomes such as projected per-member-per-month costs and membership uptake.
• Use a plan design workflow manager. A comprehensive workflow management tool guides product designers through the decision-making journey and then helps in preparing required documentation for CMS and other stakeholders, including agents and internal sales reps.

Closing information gaps and reducing execution complexity becomes even more critical to plan design optimization as CMS sunsets “better of” rules for Star Ratings calculations and associated rebates decline for some plans.

In summary, health plans should assess their maturity in using data analytics to reduce information gaps and automation to minimize execution complexity in the MA plan design process. Based on the maturity assessment results, health plans should create a roadmap that will help them advance on these two dimensions, in a step-by-step process, with nominal investments that can be recouped by efficiency gains. Continuous improvement in reducing information gaps and execution complexity will no longer be an option but a necessity for health plans to stay competitive in a crowded—and growing—MA market.