Quantifying the value of new-member HCC coding improvements to ACA plans 

Dan Becker, FSA, MAAA

Principal and Product Director, Curv

Open enrollment may prevent ACA plans from choosing which risks they take on, but advanced modeling can help to close the  “new-member gap”—improving both patient outcomes and risk transfers. The proof is here, in a formula your plan can use to estimate the bottom-line impact of targeted outreach.

ACA plans’ real-world data shows that the plan-liability risk scores (PLRS) of first-year members are, on average, about 18% lower than those of returning members.

That difference matters because in a typical year, up to 40% of ACA plan members arrive as unknown quantities. But since only about a quarter of the population has HCCs to code, plans face two related challenges: The first is to identify which new members should be targeted for expensive outreach activities like in-home assessments; the second is to assess the likely return on a prospective coding investment.

To help plans address these challenges, this paper discusses:

• The heavy costs associated with inaccurate coding of first-year members
• A practical technique for improving accuracy with a predictive model that uses deidentified data to segment new members into tiers according to the likelihood that they have HCCs
• A simple algorithm that plans can use to estimate their market-specific return-on-investment from using the model

Inaccurately coding new members amounts to leaving money on the table. Narrowing the new-member gap helps to ensure that risk transfers more closely reflect the risks plans have taken on and can help to close an associated revenue shortfall that may reach 3% of premium. The article concludes with a formula that plans can use to estimate their own likely return on investment.