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Perficient describes Stars Intelligence as an AI-native performance-management offering for Medicare Advantage and Prescription Drug organizations. It is designed to connect plan data and CMS-aligned measure calculations with contract-risk assessment, member-level gap prioritization, outreach tracking, scenario modeling, and natural-language analysis. These are capabilities Perficient says the product offers; the available product information does not establish independent results, customer deployments, launch timing, or implementation requirements.
What is Perficient’s Stars Intelligence?
Stars Intelligence is a business-to-business tool for health plans, not a consumer-facing service for comparing Medicare plans. Perficient presents it as a workflow for managing the data and interventions that inform Medicare Advantage and Part D quality performance.
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Perficient says the system uses a FHIR-native data foundation and audited, versioned measure logic aligned with CMS requirements. Its described workflow covers:
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- Standardizing plan data and calculating measures.
- Monitoring ratings, weighted measures, and potential financial impact.
- Identifying contracts at risk and members with open care gaps.
- Prioritizing interventions and following activity through gap closure.
- Tracking outreach impact and modeling scenarios, including potential return on investment and rule changes.
- Using a natural-language AI navigator to analyze information.
This description explains the product’s intended workflow, not proof that its predictions are accurate or that its interventions improve ratings or financial outcomes. Perficient’s product page does not provide independent performance findings, pricing, implementation duration, or evidence of customer deployments.
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Why do Medicare Advantage Star Ratings matter financially?
CMS publishes Star Ratings annually to help beneficiaries compare the quality of health and drug plans. CMS states that ratings determine Quality Bonus Payments and affect Medicare Advantage rebates. That makes quality measurement consequential for both plan oversight and financial planning, though a particular rating does not by itself establish a specific organization’s revenue or rebate outcome.
For plans, the operational challenge is to understand measure performance early enough to decide where to focus resources, then determine whether outreach and other actions are closing the relevant gaps. A management tool can be evaluated against that need, but vendor descriptions alone do not show that any tool achieves better ratings or savings.
What changed in the 2027 Medicare Advantage Star Ratings?
CMS published the 2027 Star Ratings on October 8, 2026. They are available on Medicare Plan Finder for the 2027 Open Enrollment period and affect 2028 Medicare Advantage quality bonus payments. CMS says contracts may be rated on up to 43 measures for MA-PD, up to 32 for MA-only, and up to 13 for standalone Part D plans; these are maximum counts, not the number every contract receives.
Separately, CMS’s Contract Year 2027 final rule changes the measure set and reward framework. The timeline matters: the new Part C Depression Screening and Follow-Up measure begins with measurement year 2027 and appears in the 2029 Star Ratings. Measurement year, rating year, and payment year refer to different stages and should not be treated as interchangeable.
| CMS change | What it means |
|---|---|
| 11 measures removed | The final rule removes measures focused on administrative processes or areas with little meaningful variation between plans. |
| Historical reward factor retained | CMS retains a historical reward factor for consistently high performance. |
| Part C Depression Screening and Follow-Up added | The measure starts with measurement year 2027 and is included beginning with the 2029 Star Ratings. |
These changes create a reason for plans to check whether measure logic and forecasting reflect the applicable CMS rules and timeline. Perficient says its measure logic is versioned and aligned with CMS requirements; the product page does not independently document how quickly updates are incorporated or how each rule change is implemented.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should a health plan evaluate an AI-supported ratings tool?
Rather than relying on a general claim of AI capability, buyers can assess whether a system fits their measurement, intervention, and governance needs. Useful questions include:
- Measure logic: Can the vendor show how calculations map to CMS specifications, how versions are controlled, and how a result can be audited?
- Risk timing and quality: How early does the tool surface contract or member risk, and what evidence supports the accuracy of those signals?
- Action linkage: Does risk identification connect to specific intervention workflows, and can the plan follow actions through gap closure and assess outreach impact?
- Integration: What data sources and technical components are required, and how does the implementation work with the plan’s existing environment?
- Governance: What privacy, security, access-control, and human-review practices apply to AI-supported analysis and prioritization?
- Evidence and cost: Can the vendor provide relevant references, deployment evidence, implementation scope, and total cost for the plan’s use case?
These are evaluation criteria, not established strengths or weaknesses of Stars Intelligence. The available product information does not answer them in enough technical detail to replace a buyer’s own diligence.
Which platforms does Perficient’s Stars Intelligence support?
Perficient says the solution is designed to operate within a health plan’s technology environment and names Databricks, Google Cloud, Snowflake, and AWS as examples. The product information does not establish that every capability works with every named platform, or provide platform-by-platform compatibility, architecture, or deployment requirements. Buyers should request specifics for their intended configuration.
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