Skip links

On-Demand Webinar: To Learn How Payers Can Automate Prior Auth at Scale with AI. View Now.

Case study

Capital Blue Cross

Fast Facts: Second-Pass Coding

  • 33,500 charts reviewed
  • +5% RAF score increase between 2016-2018
  • 100,000 ACA Lives

About the Program

  • Second-pass chart reviews for MA & ACA members behind first-pass vendor coding
  • Using Apixio’s HCC Identifier, an AI-powered coding application, to perform reviews with internal coders
  • After a successful pilot project, has used Apixio’s technology since 2016

Program Background

Capital Blue Cross is a regional health plan based in Pennsylvania that provides Medicare Advantage (MA), Medicaid Managed Care, and Affordable Care Act (ACA) plans. Dr. Andrew Bloschichak, Senior Medical Director, Government Programs, oversees a small but dynamic team that manages risk adjustment for Medicare Advantage and ACA plans. Capital Blue Cross’s primary corporate strategy on risk revenue is accurate and timely HCC code submissions to CMS and HHS. To achieve these goals, they employ a chart retrieval and coding vendor to manage first-pass coding. “Medicare Advantage and ACA risk adjustment is complicated, frequently changing, and under increasing government scrutiny. We rely on outside assistance to bring additional expertise to our organization,  says Dr. Bloschichak.

While Capital Blue Cross relies on an external coding vendor for first-pass reviews, Dr. Bloschichak’s team performs a secondary review of coded charts to ensure that every code they submit meets a high level of quality and accuracy. At first, the team manually reviewed every first-pass chart to assess whether there were any missing new codes or potential deletes, but this process was time intensive and laborious for a small team to perform for two government programs.

Apixio’s Solution

Fortunately, the solution to Capital Blue Cross’s risk adjustment challenges was close at hand. While researching technology solutions to supplement his risk adjustment activities, Dr. Bloschichak discovered that Capital Blue Cross had previously contracted with Apixio to perform AI-assisted coding reviews. “We renewed our relationship with a pilot project, and the results were outrageously good. After that, the program took off like a rocket,” says Dr. Bloschichak.

The team has been using Apixio’s HCC Identifier, an AI-powered coding & QA application, to support second-pass chart reviews since 2016. This solution has helped their team work more efficiently, ensuring that their code submissions are supported by the clinical documentation without spending hours manually reviewing patient charts. “Apixio’s machine learning is great. I’ve seen from personally reviewing records that the platform is spot on with what page and paragraph we need to look at, and the application is incredibly easy to use,” says Dr. Bloschichak.

The Capital Blue Cross team views Apixio as a true risk adjustment partner due to their positive experiences working with the team. “Apixio’s customer service far exceeded all expectations. Not only are the people excellent at what they do, they’re wonderful to work with,” says Dr. Bloschichak.

Program Impact

  1. Increased RAF scores by more than 5% between 2016-2018
  2. Improved accuracy of HCC coding submissions for Medicare Advantage and ACA programs
  3. More efficient QA of first-pass coding decisions
  4. Increased confidence that submitted codes are well supported by evidence in charts

Results

In the last two years, Capital Blue Cross has increased RAF scores by 5%, and had a significant turnaround in risk revenue due to more accurate coding submissions for both Medicare Advantage and ACA programs. Their team is able to perform QA reviews more efficiently using HCC Identifier, and feel confident that their coding decisions are well-supported. “I can sleep at night because I’m confident in the quality of coding reviews that have been performed with Apixio,” says Dr. Bloschichak.

For their ACA plans, improved risk capture had a huge impact on results for the program—they’re now capturing transfer payments that reflect their population’s true health needs instead of overpaying into the risk pool.

🍪 This website uses cookies to improve your web experience.