A clinical decision support tool for Medicare Advantage plans to optimize Star Ratings through gap-to-threshold analysis and prioritized pharmacy interventions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install cms-star-ratings
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install cms-star-ratings using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is CMS Star Ratings Clinical Intelligence?
The CMS Star Ratings clinical intelligence skill is a specialized agent designed for managed care professionals and clinical pharmacists. It serves as a bridge between raw plan performance data and actionable, evidence-based interventions. By integrating current CMS cutpoints and clinical guidelines, the skill helps teams navigate the complexities of Medicare Part D performance.
This Openclaw Skills integration focuses heavily on triple-weighted adherence measures, such as diabetes medications, RAS antagonists, and statins. It ensures that interventions are not just performed, but prioritized based on PDC thresholds and clinical risk layering, maximizing the impact on the overall plan rating while maintaining high standards of patient care.
CMS Star Ratings Clinical Intelligence Use Cases
- Performing gap-to-next-star analysis to identify the smallest performance lifts required for a rating increase.
- Stratifying patient populations for adherence interventions based on proximity to the 80% PDC threshold.
- Identifying guideline drift where clinical updates diverge from CMS or MIPS measure specifications.
- Structuring Medication Therapy Management (MTM) and Comprehensive Medication Review (CMR) workflows for maximum clinical yield.
- Navigating MY2026 changes including SDS risk adjustment and IP/SNF PDC methodology updates.
How CMS Star Ratings Clinical Intelligence Works
- Data Normalization: The skill ingests plan or patient-level data, normalizing measure names to CMS IDs and standardizing PDC values.
- Reference Loading: It dynamically loads specific reference modules for measure cutpoints, adherence logic, and guideline drift registries.
- Gap Analysis: It calculates the distance to the next star threshold, weighting findings by measure impact (e.g., triple-weighted adherence).
- Clinical Risk Layering: For patient-level data, it overlays clinical factors like ASCVD risk or A1C levels onto adherence gaps to score intervention priority.
- Recommendation Engine: Generates prioritized intervention strategies matched to the root cause of non-adherence or clinical mismatch.
CMS Star Ratings Clinical Intelligence Setup
To deploy this skill within your environment, ensure you have the core Openclaw Skills framework installed and the reference directory populated.
# Clone the skill repository
git clone https://github.com/openclaw/cms-star-ratings.git
# Install dependencies
cd cms-star-ratings && npm install
# Initialize reference files
cp config/measures.sample.md references/measures.md
CMS Star Ratings Clinical Intelligence Data Schema & Taxonomy
| Component | Format | Description |
|---|---|---|
| Measure IDs | D10, D11, D12, etc. | Standardized CMS Part D measure identification |
| PDC Values | Decimal or Percentage | Normalized to percentage (e.g., 0.80 becomes 80%) |
| Risk Factors | Categorical/Numeric | ASCVD scores, LDL levels, BP goals, and CKD stages |
| Guideline Drift | Registry Template | Mapping of clinical guideline version vs. CMS spec version |
| Intervention Map | Logic Framework | Links root causes (cost, side effects, forgetfulness) to specific actions |
CMS Star Ratings Clinical Intelligence Advanced Features
- Multi-year strategy flagging: Automatically identifies and alerts users to temporary weighting changes specific to MY2026.
- SDS Risk Adjustment: Integrated logic to account for sociodemographic status (LIS, disability, dual eligibility) in performance analysis.
- Automated Drift Detection: Proactive monitoring for mismatches between evolving clinical evidence and static regulatory measures.
- IP/SNF Exclusion Logic: Advanced PDC calculations that automatically adjust for inpatient or skilled nursing facility stays.
SKILL.md
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