Medicare Advantage For Health Plans

Retrospective risk adjustment involves several administratively heavy processes, from chart retrieval to coding to supplemental data and submissions. The ever-changing regulatory environment requires continual updates in processes and technology.

Join expert Greg Pastor to discover ways to streamline retrospective risk adjustment processes and develop a strategic, multi-faceted approach to addressing industry changes.

About Our Guest:
Greg is the Managing Director of Risk Adjustment Operations, leading a team of over 350 risk adjustment professionals to drive client execution, customer value, and plan revenue optimization.

While prior authorization serves as vital checks and balances, ensuring clinical quality and preventing fraud, the administrative burden it imposes on providers and payers alike has led to an industry-wide reevaluation of certain codes and an increased push towards technology for auto-approvals and Gold Carding.

Now, it's up to payers to encourage provider adoption of the technology by offering platforms with user-friendly interfaces, intuitive design, and seamless workflows.

Streamlining prior authorization improves the overall experience for payers, providers, and members to ensure timely care and a more efficient healthcare system.

Tune in to discover:

• Current shifts in the industry to reduce administrative burden while maintaining clinical quality and medical necessity
• How plans are leveraging technology to gain insights and refine prior authorization processes
• What CMS is doing to ensure guidance and appropriate timeframes serve members' best interests

About Our Guest:
Chris Hugenberger has been in healthcare software for nearly 20 years, working on operations, implementations, and product development for both the provider and payer sides. He has niche expertise in utilization management and prior authorization software.

As payers adopt artificial intelligence (AI) technologies in different aspects of healthcare operations, there is a need for AI governance and the careful vetting of vendor AI practices to safeguard patient welfare.

AI solutions can offer valuable decision support to create efficiencies at scale, timeliness, and accuracy. However, AI solutions should not run autonomously, nor should the final result go unquestioned. It is essential that all stakeholders understand how AI solutions draw their conclusions, what data sources inform the models, and the potential sources of biases that can occur. This level of critical thinking via human oversight is the crux of responsible AI principles: transparency, accountability, and safety.

Tune in to this episode to hear the latest on:

• Current challenges using AI for decision support
• Responsible AI principles
• The vital information needed for all stakeholders
• Ways to implement best practice processes for AI oversight
• The AI algorithm lawsuit that's shaking up the payer space

About Our Guest:
Sam Keith is an expert in data science, marketing, and analytics. He has over 18 years of experience working in the technology product space, leading product development teams and initiatives to support consumer engagement, user experience, digital experience, and operations. Sam has worked in healthcare, higher education, pharmaceutical, and network security industries and is particularly interested in digital accessibility practices.

As more and more provider organizations enter into risk-sharing agreements, provider engagement programs are experiencing a surge in participation. Provider engagement programs improve the collaborative relationship between plans and providers to keep documentation up-to-date for CMS submission. It's essential for plans to offer a variety of delivery methods to suit the provider's practice. Some practices enjoy an in-person, on-site method to receive personalized guidance for education and to maximize documentation opportunities, while other practices enjoy an EMR-integrated solution for a highly efficient digital workflow. Providers with an already established process for responding to queries may prefer a remote option via fax. The important aspect of a healthy provider engagement program is not necessarily the delivery method but rather the timely and continuous communication between plans and providers to close gaps on addressable conditions.

Tune in to discover the best practices for running a successful provider engagement program.

About Our Guest:
Michelle Calagaz is an expert in prospective risk adjustment programs specializing in provider engagement tactics. She has over 30 years of experience working in healthcare with a focus on Medicare Advantage initiatives and has an array of experience across risk adjustment, business operations, program implementation, product development, and client relations.

NLP is an AI technology that is being used in healthcare IT for clinical documentation and medical coding. For medical coding, the program identifies diagnoses codes for HCC risk adjustable categories and flags it for a medical coder to review.

In robust medical charts that span up to thousands of pages in length, this enables coders with an automated way to identify diagnoses codes for review, hence increases speed, efficiency, and output. Academic research has found NLP increases medical coding productivity by 15-20%.

After the medical chart is reviewed by a medical coder, the chart goes through a pre-submission QA process for accuracy and compliance review. In some cases the chart will go through the NLP program for a second pass to identify additional insights and potential missed opportunities.

Tune in to this episode to learn:

• Additional opportunities and limitations of NLP
• Why medical coders are needed now more than ever
• How organizational goals influence the way plans customize their NLP engine

The intention for developing RADV audits was to develop a checks and balances to ensure reimbursement payment accuracy for Medicare Advantage Organizations (MAOs).

There’s a history of CMS addressing payment accuracy in the Medicare space that dates back to the 80’s with the prospective payment system, PPS, and in the late 90’s with the Balanced Budget Act. The first RADV audit for MAOs wasn't performed until 2007. The initial audits determined that MAOs were being significantly overpaid which justified the 2011 proposed rule that suggested overpayments should be extrapolated, in other words, overpayments should be returned to the Centers for Medicare and Medicaid Services (CMS).

Over the years, CMS has explored different ways to determine the error rate of MAO overpayment. In the most recent 2023 Final Rule, CMS has released their go-forward plan to extrapolate beginning in payment year 2018, however, no specific methodology for error rate determination has been defined, nor has a commencement date been announced. Plans can expect to be notified prior to extrapolation so they can forecast.

Health plans need to implement a strong risk mitigation program to ensure reimbursement accuracy.

Tune in to this episode to discover:

• The impact to smaller plans
• Industry-wide changes in response to the Final Rule
• How plans will deal with the potential reimbursement loss
• Ways to improve reimbursement accuracy

About Our Guest
Amanda Proctor has over 13 years in risk adjustment coding and specializes in risk mitigation, coding quality and education. She holds multiple certifications in coding and is an AAPC approved instructor.

There is a distinct advantage to enrollment technologies that are built specifically for CMS's enrollment and dis-enrollment regulations for Medicare Advantage and Part D. One such specification includes the Application Programming Interface (API) integration of the CMS MARX database for the validation of eligibility for Medicare Part A, B and D. This is a unique function that allows for real-time eligibility validation within the enrollment technology and avoids the less timely alternative of using batch processes for file submission to CMS. Additionally, special election periods are factored in to the technology logic and consists of low income subsidies from CMS, moving service areas, Chronic Condition Special Needs Plans (CSNPs), and members losing employer-sponsored group coverage.

Another CMS rule set defines whether an enrollment is complete or incomplete. Certain elements must be present such as: member's signature, responses to questions about other sources of coverage, ensuring the member's permanent address is within the service area. Incomplete enrollments are funneled through automated workflows to obtain missing information, for example, a request for information letter may be triggered. Once the membership is complete, changes in membership status or updates to information are initiated through the enrollment technology and flows to impacted systems.

Because enrollment is the member's first touchpoint with the health plan, enrollment technologies should also enable other downstream activities like claims processing, vendor integration, and member correspondence so each aspect of the member experience feels seamless and promotes a cohesive brand identity for the health plan.

About Our Guest:
Dave Laity is the Product Director for Advantasure's Enrollment and Billing products. Dave has nearly 20 years of healthcare experience that include the development of enrollment solutions that focus on Medicare Advantage and Part D.

In this bonus episode, we welcome Stars expert, Megan Piotrowski, for an in-depth review of the recent Star Ratings release. On October 13, 2023, the 2024 Medicare Advantage and Part D Star Ratings were published on Medicare Plan Finder, largely representing Measurement Year 2022 data submitted to CMS.

We discuss the variables that influenced performance and why it's the second year in a row, the average Star Ratings have declined and some plans might be seeing less than ideal performance. More than a third of plans saw a Star Ratings decline this cycle.

Tune in to learn about the changes that will impact 2024 performance including the Tukey Outlier Deletion Method, the transition of several HEDIS measures to Electronic Clinical Data Systems (ECDS), measure weight changes, and more.

About The Expert
Megan Piotrowski is a Star Ratings and quality strategy manager evaluating market trends and competitive landscape to develop initiative offerings and long-term strategies for the continued maintenance of 4-Star Ratings. Megan has held a variety of regulatory, quality leadership and consultant roles across the continuum of healthcare. She’s led the quality improvement initiatives on behalf of health plans across all lines of business, as well as for health systems, providers, and community and government-based organizations. She holds a Master of Science in Health Informatics from Northwestern University.

In this episode, we discuss the significant changes in the weight of Star measures, with the reversal of the 2022 Final Rule—many measures are returning to their previous weights. In response to the 2022 Final Rule, plans made significant investments to enhance the member experience in areas like customer service and developing digital platforms. The 2023 Final Rule has recently announced the removal of the Reward Factor and is replacing it with the Health Equity Index, which aims to incentivize plans to focus on serving members with higher social risk factors.

“Members are getting a better experience because plans reacted to the measure weight increases. Even though the measure weights may be redacted, the investments are already there and members are going to continue to feel the benefits of these enhancements.” - Michelle Simon

Although the removal of the Reward Factor may temporarily affect Star Ratings, plans that perform well for their underserved members will receive a bonus tied to their performance.

The point system and measure weights play a crucial role in plan performance for Star Ratings. The difference between a 4-Star and a 5-Star plan is often a very tight threshold. It’s critical for plans to adopt a dynamic approach that constantly evaluates the data and how it relates to the weight of the measures. Stars is a math game that requires constant analysis and iteration to strategically direct efforts and resources towards the highest impact.

While the upcoming changes are generating a mix of excitement and apprehension among plans, the focus remains on advocating for the members and achieving better outcomes.

Tune into this episode to hear valuable insights into the challenges and opportunities in the Stars program, and learn strategies in navigating the changes and improving performance.

About Our Guest
Michelle Simon has over 15 years of experience in quality programs. She began her career on the commercial side with the quality rating system and transitioned into the Stars space where she has spent the majority of her career. Simon has a Masters degree in Organizational Leadership and a Post-Graduate Certificate in Healthcare Informatics and Data Analytics.

As stewards of healthcare, health plans are responsible for managing the care of its members. This includes working with providers to capture member conditions accurately and comprehensively via medical charts and coding. This improves member outcomes and optimizes the plan's risk adjustment revenue which ultimately reduces member costs.

The scope of a prospective risk adjustment program is to account for historical member conditions, and identify and close gaps on suspected member conditions. Many plans attempt to close as many prospective gaps in a year as they can and whatever they cannot close in that year is sent to retrospective programs. This is an unsophisticated, costly approach that tends to over-suspect and send providers weak evidence which diminishes provider trust and engagement.

Based on CMS guidelines, the prospective format has very specific language requirements for how providers document member conditions. Plans cannot go back in time and change how its providers code and document a condition, thereby making retrospective programs administratively heavy.

AI machine learning models offer a higher level of sophistication by scanning the clinical evidence and assigning a probability score to each piece of evidence in support of a suspected member condition. This saves administrative time and offers providers a high level of trust that the data sent via CDI alerts is compelling and indicative of a condition. When providers have confidence in the data, it increases their participation in prospective programs and leads to more gaps closed.

Tune in to this episode to learn more about AI suspecting program logic and prospective programs.

About Our Guest:
Elizabeth Burreson is an expert in risk adjustment analytics technology and has 20 years of IT data management experience, managing product portfolios and backlogs.