Risk Adjustment

Revealing Insights to Optimize Care and Understand Risk

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AI-Enabled Prospective and Retrospective Modular Solution Suite.

Health plans and providers alike can harness the power of the most mature, clinically-raised NLP on the market that can distill medical data and claims information into an unprecedented clinical source of truth. Through that unification, our partners have a better understanding of risk across lines of business and patient populations to optimize care plans while obtaining accurate government-sponsored program payments.

Whether it’s an EHR-integrated provider workflow, a retrospective review for payers, or effective value-based care analytics, our integrated approach improves efficiency while eliminating organizational silos.

Infographic - Risk Adjustment


Clinical Workflow Suite

Risk adjustment in a clinical setting is prospective, aligning itself with clinical workflows and goals by surfacing conditions - suspects - not previously diagnosed but anticipated based on clinical evidence (claims, pharmacy, diagnostic tests, etc.). These suspects can be routed to the care team directly at the point –of care, or can be curated by a review team during pre-encounter for an increased likelihood of acceptance.

  • Attain net-new RAF capture by surfacing undiagnosed conditions that are mined from unstructured clinical data via EHR workflows
  • Enhance care plan effectiveness and a clinician’s time in-treatment with more complete risk capture and patient documentation inclusive of all comorbidities
  • One single solution across all contracts, payers, populations, and EHR systems

A modular, EHR-integrated solution to identify, manage, and document population risk.

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Coding Workflow Suite

With enrollment continually growing, the risk adjustment burden is rising – as well as the associated costs. Traditional, manual approaches to risk adjustment are siloed, resource-intensive, error-prone, and do not scale with an organization’s growth. To ensure appropriate reimbursement for the risk factors present in government-sponsored populations, organizations need to accurately identify, document, and report risk scores.

  • Increase risk capture by 20-30% by accurately identifying HCCs that are commonly missed or coded improperly during manual reviews
  • Optimize coder efficiency through a prioritized case list that presents only those records requiring a review, reducing coding volume and decreasing the time spent per record with all supporting evidence linked to the member
  • Minimize compliance and audit risk with two-way coding reviews

A modular, end-to-end solution that identifies, manages, and documents member risk across all product lines, ensuring complete and accurate payments.

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NLP Suspecting

Dig deeper into unstructured data to surface conditions not previously diagnosed but anticipated based on our AI model’s clinical evidence review. Findings come from a combination of disparate data sources to identify unrecognized conditions, or undocumented instances of complexity, or comorbidity. In each case, a possible suspect is determined through the blending of criteria that includes available data points and assessment through rules-based and complex logic.

  • Generate a more complete and accurate list of suspected conditions with both administrative and clinical data for provider validation
  • Throttle suspect volume with confidence scoring, suppression, and filtering at both the global and local level, including care specialization condition targeting
  • Confirm up to 20-25% more valid conditions to gain up to a +10% RAF (risk adjustment factor) value

Our AI-enabled suspecting analyzes clinical and claims data to uncover HCCs and potential diagnosis gaps that otherwise get overlooked in a claims-only prospective processes.

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Encounter Submissions

A comprehensive submission platform across all managed care plans and programs.

  • End-to-end encounter lifecycle dashboard visibility across lines of business
  • State-specific Medicaid submission modules driving continuous compliance
  • Flexible platform to improve scalability, operational efficiency, and revenue integrity for all managed care programs

Operational optimization that drives accurate, complete, and compliant encounter data submissions across all product lines

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