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

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

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

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