Help optimize risk adjustment scores to promote more accurate payment and reimbursement through flagging previously documented HCC diagnoses that need to be readdressed, as well as suspected HCC-supported conditions.
Since 2004, CMS has been using HCCs to adjust healthcare reimbursement payments to private health care plans for the expenditure risk of their enrollees. HCCs are used by a variety of CMS and commercial programs to determine reimbursement and baseline costs.
The CMS risk-adjustment model measures the disease burden of more than 70 HCCs that correlate to ICD-10 diagnosis codes.
Cerner HCC intelligence can help identify gaps in documentation by flagging previously documented HCCs that need readdressed and identifying suspected conditions based on labs results, vital signs, medications and treatments.
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 Cerner data and insights platform, HealtheIntent®.
Once suspected conditions are flagged, reporting enables users to react to supporting and competing facts, and facilitate 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.
Beating the Status Quo: How Hospitals Can Streamline Operations and Optimize Patient Care
Genesis ACO uses data, care management tools to help improve quality measures
“Providers captured the acuity levels more accurately in their Medicare Shared Savings Program (MSSP) population and saw Genesis’ risk adjustment factor (RAF) score go from 1.080 to 1.164.” -Steven Aguilar, MD, Medical Director for Primary Care, Genesis ACO
"The HCC advisor is invaluable; having the problem list flag the HCC codes in a visual way was beneficial for our team. We would update problem lists, and then the system would flag HCCs needing coded. That’s worked well especially for diabetes." -Steven Aguilar, MD, Medical Director for Primary Care, Genesis ACO
Tap into Cerner’s analytic expertise — from strategy to implementation to execution — we can help you.
Enable the ability to query de-identified data, extract and transform data sets in research-ready formats, build complex data models and algorithms, and validate findings all from a single environment.
Host disparate data within a single location and provide users the ability to onboard and organize data and create custom reports and dashboards to satisfy your organization’s unique needs.
Maximize the impact of your EHR investment through analyzing system performance and solution adoption.
Enable control over your clinician experience by accessing individual user- or group-level efficiency and adoption data.
Support patients’ entire throughput journey by coordinating the right caregiver, with the right patient, at the right place and time.
Help reduce time and resources dedicated to identifying appropriate analytic content and creating analytic visualizations with ready-to-use dashboards and reports designed around specific, analytic use cases.
Monitor quality data, concurrently or retrospectively, to enable the simplification of the reporting process for hospitals and eligible professionals.
To proactively identify gaps in care, recommend targeted interventions and provider performance, Cerner offers a registries and scorecards solution that enables organizations to identify, attribute, measure and monitor people and providers at an individual or population level.