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Hierarchical condition categories

Optimize Medicare Advantage (MA) reimbursement payments

Hierarchical condition categories (HCC) are used by a variety of Centers for Medicare and Medicaid Services (CMS) and commercial programs to determine reimbursement and baseline costs for private health care plans. Identify gaps in documentation that contribute to how your population is risk adjusted to enable more accurate payment or reimbursement.

Ranked as top three vendor for both analytic product vision and capabilities in
Chilmark’s 2017 Healthcare Analytics Market Trends Report.

Optimize your risk adjustment scores to promote more accurate payment and reimbursement through flagging previously documented HCC diagnoses that need to be readdressed, as well as identifying suspected HCC-supported conditions.

In 2004, Medicare put into effect an HCC model to adjust capitation payments to private health care plans for the health expenditure risk of their enrollees.

The CMS risk adjustment model measure the disease burden of more than 70 HCCs that correlate to diagnosis codes.

HCCs are used by a variety of CMS and commercial programs to determine reimbursement and baseline costs.

Cerner's HCC intelligence can help you identify gaps in documentation that contribute to how your population is risk adjusted and is designed to enable more accurate payment or reimbursement. This is accomplished by both flagging previously documented HCCs that need to be readdressed, as well as identifying suspected conditions based on labs results, vital signs, medications and treatments.


Identify previously documented diagnoses

To identify members of programs requiring HCC documentation — including Medicare Advantage beneficiaries — intelligence scans for previously documented diagnoses that have not been documented for the current year.

To further explore documented diagnoses, users can pull a report that provides: the HCC code and description, diagnosed condition, ICD-10 condition code number and last service date.

To identify conditions that have not been discreetly documented, clinically-based models leverage aggregated, normalized data, such as lab results, medications and procedures through the big data platform, HealtheIntentSM.

Once suspected conditions are flagged, reporting enables users to react to supporting and competing facts, facilitating decision-making around the condition that maps to an HCC code.

Once identified, intelligence stratifies members with undocumented HCCs with no scheduled appointments and assists in setting up a visit.

Users can validate all claims have been paid through analytic and reporting capabilities. If a claim was not paid or denied, users can run a report to substantiate the claim to send to the payer.

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