Challenge #1: Complexity
There’s no doubt about it – risk adjustment is complex. This is true whether the product line is Medicare Advantage, commercial insurance, or Medicaid. The complexity of the risk adjustment models and the challenge of collecting accurate encounter data drives many insurers to hire vendors to conduct risk adjustment work. Outsourcing, however, is an imperfect solution that sacrifices return on investment (ROI) for convenience. Plans can overcome the complexity challenge by investing in their institutional knowledge around risk adjustment, and they can augment subject matter expertise by investing in a risk adjustment analytics solution.
Building institutional knowledge is the most enduring investment a payer can make in its risk adjustment operations. Because risk adjustment is a specialized area of healthcare finance, risk adjustment professionals are difficult to hire externally. Developing several subject matter experts who know the details of the risk models and who understand what activities contribute to a meaningful impact on risk scores will empower a plan to optimize risk scores in the near term. Subject matter experts will also be able to anticipate long-term operational shifts in response to the ever-changing regulatory environment.
At a minimum, plans should invest in three areas of expertise: analytics, coding, and encounters. Even plans that vend out some or all of their risk adjustment work should develop this expertise because vendors that do work in these areas require plan oversight to ensure they’re delivering maximum value. Risk adjustment directors, managers, and analysts all need to have a grasp of risk adjustment models and some level of knowledge of coding. This understanding should encompass knowledge of product-specific risk adjustment models and the standard industry activities for capturing risk-adjustable diagnosis codes.
Analytics can be the most difficult area to bolster within an organization. HCC models pose a particular challenge. Model features such as disease hierarchies, interactions, and segment-specific demographic risk score components require sophisticated programming logic if they are to be replicated. If building this logic internally proves too daunting, plans should invest in a software solution that already incorporates model logic to facilitate risk score analytics. Good risk adjustment software can empower a plan to run analytics without having to rely on a vendor, and at the same time it can remove the need to maintain risk adjustment software internally.
Risk adjustment complexity is not an insurmountable barrier to building a robust risk adjustment program in-house. With the right tools and expertise, any health plan can build a solid vendor oversight or even save money, gain valuable insights, and improve member outcomes by performing the bulk of risk adjustment work internally. Analytics is undoubtedly the most critical area for investment. While analytics can be daunting, risk adjustment software enables plans to conduct this critical work without relying on vendors.
Director of Risk Adjustment Solutions
Peter Janelle has more than a decade of experience in health insurance, having worked both for payers and vendors. He has extensive knowledge of Medicare Advantage finance, including risk adjustment, stars, value-based arrangements, and healthcare economics. Before joining Vital Data Technology, Peter was in charge of risk adjustment operations at UCare, a regional Minnesota insurer with books of business in Medicaid, Medicare Advantage, and ACA.
His entrepreneurial approach to product development has been shaped both by this healthcare experience and by his leadership of The Liquor Cabinet, a digital content brand with an iPhone app and a comfortable social media following. In addition to traveling the globe in search of unique liquors, Peter enjoys photography and outdoor adventure. An annual highlight is his annual winter camping trip to northern Minnesota.
Peter holds a Master of Public Policy 0from the Humphrey School of Public Affairs at the University of Minnesota.