Hierarchical condition categories (HCCs) reveal the severity, complexity and interaction of member conditions. They are used by the Centers for Medicare and Medicaid Services (CMS) to adjust risk scores that determine payment for Medicare Advantage (MA) beneficiaries.

With CMS leading the way, additional government and commercial payers leverage HCCs to drive quality ratings, shared savings, risk scores and payment adjustments for a broad range of value-based contracts and activities.

As HCCs rise in importance, it is important that health plans maximize coding efficiencies. Meaning, health plans must have dependable HCCs to fully understand, determine and predict risk from every perspective and enable value-based partners to accurately determine associated quality and reimbursement adjustments properly.

Medicare Advantage and Beyond: The Importance of HCCs

HCCs align with billed diagnosis codes and the expected cost for future treatment. Condition severity, interactions and complexity are weighted to accurately adjust payments to Medicare Advantage plans, thereby ensuring appropriate funding for the correct level of services. More heavily weighted risk scores translate into higher per member per month (PMPM) payments.

Ten groups, including American Medical Association and American College of Surgeons, petitioned CMS to add MA as an alternative payment model (APM) under MACRA starting in 2019, thus further reinforcing the role it will play.
Beyond Medicare Advantage:

  • HCCs help risk adjust non-grandfathered individual and small group insurance markets on and off the exchanges, which determines resource allocation between commercial plans accepting ACA (Affordable Care Act)-exchange beneficiaries.
  • HCCs determine risk-adjusted reimbursement for organizations participating in value-based payment models such as the Medicare Shared Savings Program and the Next Generation ACO (Accountable Care Organization) model.
  • HCCs determine PMPM capitation payments to managed Medicare plans.
  • HCCs reconcile observed rates of quality of care versus expected rates within the context of value-based purchasing programs.
  • HCCs help adjust payments for fee-for-service Medicare physicians eligible for participation in the Quality Payment Program of the Medicare Access and CHIP Reauthorization Act (MACRA).
  • HCCs help health plans better manage population health, providing a clearer picture of risk categories and serving as an internal barometer for disease and case management program effectiveness

Escaping Revenue: The Financial & Regulatory Impact of Coding Errors

A risk-based reimbursement structure in which sicker and more complex members are reimbursed at higher rates, combined with a rapidly aging population, makes the MA market highly attractive to insurers. MA plans happen to be highly attractive to prospective members too. About one-third of all Medicare beneficiaries selected MA plans for 2018. CMS predicts the percentage to rise above 40 percent over the next decade. The Kaiser Family Foundation reports MA enrollment already exceeds 40 percent in six states.

MA Plan Scrutiny:

  • The Office of the Inspector General (OIG) revealed that 55 percent of routine provider evaluation and management (E/M) services were “improperly coded and/or lacked sufficient documentation.” Errors occurred in overreporting and underreporting.
  • CMS estimates it overpaid $14.1 billion in 2013 to MA organizations, primarily from unsupported diagnoses.
  • CMS paid an estimated $160 billion in 2014 for about 16 million beneficiaries. It estimates 9.5 percent of these payments were improper.
  • The Department of Justice (DOJ) is pursuing multiple lawsuits against multiple insurers for allegedly misrepresenting MA member health status to receive higher reimbursement rates.
  • Senator Charles Grassley recently sent a letter to CMS questioning, in part, what steps CMS is taking to “implement safeguards to reduce score fraud, waste, and abuse.”
  • The letter states “risk-score gaming is not going to go away” and resulted in overpayments to MA plans in excess of $70 billion between the years of 2008 and 2013.

In addition to regulatory risk, pervasive under coding limits plan growth, provides cracks for members to slip through, and depresses margins on otherwise-profitable lines of business. Thousands of dollars in lost incremental reimbursement quickly adds up to millions for even relatively small MA (and other value-based) populations each year.

Plans with improperly coded members with complex or chronic conditions may also fail to add them to patient registries or enroll them in disease or case management programs, ensuring they receive necessary medical care. Lacking services, under-coded members will likely suffer as their health deteriorates, potentially leading to expensive and avoidable complications. Without connections to primary care providers or care coordination, these members tend to rely heavily on expensive and disjointed emergency services for their growing care needs.

Path to Continuous, Complete & Accurate Risk Scores

In an environment of tightening federal policies, widespread scrutiny and a need for better documentation surrounding the complexities of member health, health plans must evaluate their coding practices and look for an alternative to time-consuming, outdated and disjointed processes to acquire, review and submit HCCs.

Innovative health plans are successfully leveraging proven analytic technologies to determine the appropriate level of member condition complexity and severity, accurately predict future costs of care, reduce regulatory risk exposure and maximize MA payments. The best of these technologies:

  • Combine clinical, electronic health records (EHR), claims, lab, pharmacy and social determinants of health (SDoH) data to bring together necessary, but previously isolated, information.
  • Leverage machine learning algorithms to continuously improve coding accuracy and optimization across unique and differentiated network contracts.
  • Utilize natural language processing to turn invaluable, but buried, clinical notes into usable data.

Clinical Integration

Health plans must prepare for, and move toward, clinically-integrated technology that delivers decision support and enables providers to easily improve coding practices at the point of care, driving acceptance, provider engagement and in-office improvement.

Accurately capturing diagnostic codes is time-consuming and frustrating for providers, with thousands of diagnostic codes rolling up into relatively few HCCs and doesn’t naturally fit into the clinical workflow.

A single, shared analytics platform can automatically comb clinical records to substantiate data and reduce the number of manual in-office chart audits. It delivers clear visibility into potential coding gaps, allowing care teams to easily address and close them at the point of care within their EHR workflow.

A shared analytics platform enables health plans and their provider networks to see the same information across coding and quality measures, thereby eliminating error-prone handoffs and helping create an environment of transparency, trust and collaboration.

Machine-learning Algorithms & Unstructured Data Sources

Best-in-class analytics platforms deliver immediate risk-score adjustment factor (RAF) improvement and ongoing refinement through machine-learning algorithms and analytics that unify isolated data sources that will:

  • Detect missing HCCs
  • Populate missing HCCs automatically, when appropriate
  • Prioritize members based on RAF improvement potential
  • Predict future cost, utilization, performance and risk
  • Identify and prioritize suspect conditions and co-morbidities for clinical intervention
  • Reveal potential coding gaps within the provider workflow

These technologies discover codes that existed in the previous reporting year but not the current one. When valid supporting data exists, those codes are automatically and appropriately populated for the current year. When supporting data does not exist, the gaps are prioritized for internal chart auditing

Once clinically integrated, an analytics platform automatically pushes notifications into the clinical workflow where office staff can easily map them to upcoming appointments and providers can view and address potential gaps throughout the office visit and from within their EHR.

The more data sources an analytics platform can access, the better the results and the more the system can do to passively and organically improve HCC accuracy and risk scores optimization without active intervention.

Conclusion

Health plans in the best position to optimize risk scores approach the coding process from a holistic standpoint that keeps the welfare of the member in mind, engages providers in a meaningful and gradual way, and provides robust, clinically-integrated technology to ease the burden and dramatically improve accuracy. With the help of automated HCC support and clinically-integrated workflows, analytic solutions platforms can boost plan revenue and quality scores through proper, dependable and maximized risk assessment.

Sponsor: Geneia

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