Skill: erikaalexander1/cms-star-ratings (version 1.1.0)

--- name: cms-star-ratings description: "CMS Star Ratings clinical intelligence for Medicare Advantage pharmacy optimization. Use when analyzing plan performance against Star cutpoints, identifying gap-to-threshold opportunities on triple-weighted adherence measures (diabetes meds, RAS antagonists, statins), prioritizing adherence interventions by PDC and clinical risk layering, detecting guideline drift between clinical guidelines and CMS/MIPS measure specifications, optimizing MTM/CMR completion and clinical impact, working with SUPD or polypharmacy measures (COB, Poly-ACH), or any Medicare Part D Stars strategy question. Designed for clinical pharmacists in managed care, Stars optimization, or medication management platform roles." --- # CMS Star Ratings Clinical Intelligence Clinical decision support for Medicare Advantage Star Ratings optimization. Translates plan performance data into prioritized, evidence-based intervention recommendations. ## Core Workflow ### 1. Assess Plan Performance When given plan-level data, perform gap-to-threshold analysis: 1. Map each measure to current CMS cutpoints and star thresholds 2. Calculate gap-to-next-star for each measure (absolute and relative) 3. Weight by measure impact: triple-weighted measures first, then display measures by improvement feasibility 4. Identify the smallest lifts that move the overall star rating Load **references/measures.md** for current cutpoints, measure weights, and threshold logic. ### 2. Prioritize Adherence Interventions When given patient-level PDC data and medication lists: 1. Flag patients below 80% PDC threshold 2. Stratify by proximity to threshold (78-79% PDC = highest conversion potential) 3. Layer clinical risk factors on top of PDC: - **Statins**: ASCVD risk score, LDL level, recent CV event - **RAS antagonists**: BP at goal, CKD stage, heart failure status - **Diabetes meds**: A1C level, hypoglycemia risk, recent hospitalization 4. Score composite priority: PDC gap × clinical risk × measure weight 5. Recommend specific interventions matched to root cause of non-adherence Load **references/adherence-interventions.md** for PDC logic, risk layering framework, and intervention mapping. ### 3. Detect Guideline Drift When reviewing clinical guidelines or measure specifications: 1. Check the guideline drift registry for known mismatches 2. Use the drift detection framework to evaluate new guideline updates against current CMS measure specs and MIPS quality measures 3. Flag clinical implications: where following the guideline diverges from what the measure rewards 4. Recommend platform logic updates or measure feedback to CMS Load **references/guideline-drift.md** for the drift registry, detection framework, and mismatch template. ### 4. Optimize MTM/CMR When building CMR strategy: 1. Identify highest-yield CMR candidates (clinical complexity × likelihood of completion) 2. Structure interventions for clinical impact, not just completion rate 3. Align CMR content with open adherence gaps and other Star measures 4. Track CMR completion rate against the Star measure threshold (resumes scoring 2027) Load **references/mtm-cmr.md** for CMR candidate prioritization, intervention structuring, and measure alignment. ## MY2026 Key Changes (Cross-Cutting) These changes affect multiple workflows. Surface when relevant: - **Temporary weight change**: Triple-weighting of Part D adherence measures is temporary for MY2026 only (reverts to standard weighting). Flag when discussing multi-year strategy or measure prioritization. See references/measures.md. - **SDS risk adjustment**: New sociodemographic status (SDS) risk adjustment on adherence measures — accounts for age, gender, LIS status, disability, and dual eligibility. Flag when analyzing plan performance or adherence prioritization for plans serving high-need populations. See references/measures.md and references/adherence-interventions.md. - **IP/SNF PDC methodology**: CMS now excludes inpatient and SNF stay days from PDC denominators. Flag when discussing PDC calculations, adherence outliers, or members with recent hospitalizations/SNF stays. See references/adherence-interventions.md. ## Input Formats The skill accepts plan and patient data in any structured format. When data is provided: - Normalize measure names to CMS measure IDs (D10, D11, D12, etc.) - Treat PDC values as decimals (0.80) or percentages (80%) — normalize to percentages internally - Flag missing data explicitly rather than assuming defaults ## Clinical Guardrails - Never recommend starting or stopping a medication — frame as considerations for prescriber discussion - Cite guideline sources (ACC/AHA, ADA, KDIGO, AGS Beers, etc.) when relevant - When evidence is clear, be direct; when mixed or limited, say so explicitly - Flag drug interaction severity (major/moderate/minor) with clinical significance - Distinguish between "measure-optimal" and "clinically optimal" when they diverge

path: references/adherence-interventions.md
content: # Adherence Intervention Logic

## Table of Contents
1. [Context: Why Adherence Measures Matter More in 2027+](#context-why-adherence-measures-matter-more-in-2027)
2. [PDC Fundamentals](#pdc-fundamentals)
3. [Patient Prioritization Framework](#patient-prioritization-framework)
4. [Clinical Risk Layering](#clinical-risk-layering)
5. [Composite Priority Scoring](#composite-priority-scoring)
6. [Root Cause Analysis and Intervention Mapping](#root-cause-analysis-and-intervention-mapping)
7. [Intervention Timing](#intervention-timing)
8. [Important Considerations](#important-considerations)

---

## Context: Why Adherence Measures Matter More in 2027+

CMS's CY2027 proposed rule removes 12 measures from Star Ratings — mostly administrative and process measures where plans showed little performance variation. The Part D adherence measures (D10, D11, D12) are NOT being removed. With fewer measures in the overall calculation, the triple-weighted adherence measures carry an even larger share of the total star rating. Every percentage point of PDC improvement is worth more going forward than it was before. Plans that relied on easy administrative measures to pad their star rating will lose that safety net. Clinical measures — especially adherence — are now the game.

Additionally, starting MY2026, CMS is applying sociodemographic status (SDS) risk adjustment to adherence measures — accounting for age, gender, LIS status, disability, and dual eligibility. Plans serving high-need populations may see adjusted scores. The priority scoring in this skill focuses on clinical risk layering at the individual member level, which complements but is distinct from the plan-level SDS adjustment.

Note: For MY2026 only, adherence measures (D10, D11, D12) are temporarily reduced from triple-weighted (3×) to single-weighted (1×). They return to 3× in MY2027. This does not change the clinical importance of adherence — it temporarily reduces the measure's mathematical impact on the star rating. Do not reduce adherence investment during this window.

---

## PDC Fundamentals

### Calculating PDC
- PDC = (days covered by medication in measurement period) / (days in measurement period) × 100
- Threshold: ≥80% = adherent
- Each fill's days supply covers specific dates; overlapping fills shift forward
- Only the measurement year counts — carryover from prior year fills applies to day 1

### Key PDC Nuances
- **Switching within class**: If a patient switches from lisinopril to losartan (ACEI to ARB), both count toward D11 PDC — the class is RAS antagonists, not individual drugs
- **Combination products**: Amlodipine/valsartan counts for D11 (RAS component)
- **90-day fills**: Dramatically improve PDC — a single 90-day fill covers a quarter of the year
- **Late fills**: A fill on December 1 for 30-day supply only covers 31 days of the year (Dec 1-31), but it converts the member to 80%+ if they were at ~72% through November

### MY2026 PDC Methodology Change — IP/SNF Stays
Starting MY2026, days when members are hospitalized or in skilled nursing facilities are NO LONGER excluded from PDC calculations. Previously, inpatient days were removed from the denominator, effectively "pausing" the PDC clock during a hospitalization. Now those days count toward the denominator. This means:
- Members with hospitalizations may show lower PDC scores even if adherent before and after the stay
- Post-discharge medication reconciliation becomes even more critical — ensure the member restarts fills immediately after discharge
- Plans should model the PDC impact of this change on their current population to recalibrate gap-to-threshold projections
- Pair this with CMR outreach in the 7-14 day post-discharge window (see mtm-cmr.md)

### PDC Calculation Variability
PDC is not calculated identically across all platforms and PBMs. Differences in how overlapping fills, mid-year switches, hospital days, and early refills are handled can produce different PDC values for the same patient. When working with a platform like Arine, understand how their specific PDC engine handles these edge cases — a 1-2% difference in calculation methodology can move a patient across the 80% threshold in either direction. Always verify the platform's calculation logic against the CMS Technical Notes specification for the applicable measurement year.

---

## Patient Prioritization Framework

### Tier 1: High-Conversion Patients (PDC 75-79%)
- Within striking distance of 80% threshold
- Often need only 1-2 additional fills or a fill timing adjustment
- Highest ROI per intervention dollar
- **Action**: Immediate outreach — refill reminder, 90-day conversion, mail order

### Tier 2: Moderate-Gap Patients (PDC 60-74%)
- Need sustained engagement, not a single touchpoint
- Often have identifiable barriers (cost, side effects, complexity)
- **Action**: Barrier assessment, medication synchronization, prescriber collaboration

### Tier 3: Significant-Gap Patients (PDC below 60%)
- May have discontinued intentionally or been lost to follow-up
- Requires clinical assessment — is non-adherence clinically appropriate?
- **Action**: Clinical review first — confirm therapy is still indicated before pushing adherence

### Tier 4: New-to-Therapy Patients
- Started medication mid-year, limited PDC runway
- High risk of early discontinuation (especially statins, RAS antagonists)
- **Action**: Early touchpoint at 30-60 days, address side effects proactively

---

## Clinical Risk Layering

PDC alone is insufficient. Layer clinical risk to distinguish claims-level adherence gaps from
clinically urgent interventions.

### Statin Adherence (D12) — Risk Factors

| Factor | Data Source | Risk Amplification |
|--------|------------|-------------------|
| ASCVD risk score at or above 20% | Claims + lab data | High — guideline-concordant therapy, non-adherence = preventable events |
| Prior MI/stroke/revascularization | Claims (ICD-10, CPT) | Very high — secondary prevention population |
| LDL above 190 mg/dL | Lab data | High — familial hypercholesterolemia likely |
| Diabetes comorbidity | Claims/Rx data | Moderate — overlaps D10, dual measure impact |
| LDL at goal on current therapy | Lab data | Low for intervention *prioritization*. **However: if LDL is at goal BECAUSE of the statin, discontinuation will cause LDL to rebound. Evidence shows that discontinuing statins even when LDL is controlled leads to increased cardiovascular events and mortality. "At goal" does not mean "doesn't need the med." Low priority for outreach sequencing, but discontinuation is still clinically inappropriate. The exception: patients who achieved LDL goal through lifestyle changes alone — this is a clinical judgment call requiring lab trends and history review.** |

### RAS Antagonist Adherence (D11) — Risk Factors

| Factor | Data Source | Risk Amplification |
|--------|------------|-------------------|
| BP above 140/90 on most recent reading | Lab/vitals data | High — uncontrolled hypertension |
| CKD Stage 3+ | Claims (ICD-10) | High — RAS inhibition is renoprotective |
| Heart failure with reduced EF | Claims (ICD-10) | Very high — mortality benefit |
| Proteinuria/albuminuria | Lab data | High — RAS inhibition slows progression |
| BP at goal | Vitals data | Low — controlled, though discontinuation still not recommended |

### Diabetes Medication Adherence (D10) — Risk Factors

| Factor | Data Source | Risk Amplification |
|--------|------------|-------------------|
| A1C at or above 9% | Lab data | Very high — uncontrolled, complications risk |
| A1C 7-9% | Lab data | Moderate — above most targets |
| Recent hospitalization for hyperglycemia | Claims | Very high — acute decompensation |
| Hypoglycemia history | Claims (ICD-10) | Context-dependent — non-adherence may be fear-driven. **If a patient stopped a sulfonylurea after a hypoglycemic episode, this may be a rational safety decision. Flag as a potential deprescribing opportunity, NOT an adherence gap. Do not push adherence on someone who stopped a medication for a legitimate safety reason.** |
| ESRD / dialysis | Claims | Exclusion — check if patient should be excluded from measure |
| On insulin only (no oral agents) | Rx data | Exclusion — not in D10 denominator |
| On insulin + oral agents (combo) | Rx data | **NOT excluded** — patients on insulin AND oral agents (e.g., insulin + metformin) ARE in the D10 denominator. Only insulin-only regimens are excluded. This is a common targeting mistake — do not exclude combo patients. |

---

## Composite Priority Scoring

### Formula
```
Priority Score = PDC_Proximity x Clinical_Risk x Measure_Weight x Timing_Factor
```

### Component Definitions

**PDC_Proximity** — How close is this member to converting at the 80% threshold?
Higher score = closer to threshold = less effort to convert.

| PDC Range | Score | Rationale |
|-----------|-------|-----------|
| 78-79% | 10 | 1-2 fills may convert. Highest ROI per intervention. |
| 75-77% | 8 | Likely needs 3-5 fills or 90-day conversion. Achievable. |
| 70-74% | 5 | Needs sustained engagement over multiple months. |
| 60-69% | 3 | Significant gap. Barrier assessment required. |
| Below 60% | 1 | May have discontinued. Assess clinical appropriateness first. |

**Clinical_Risk** — How clinically urgent is adherence for THIS patient?
Based on the risk layering tables above (statin/RAS/diabetes sections).

| Risk Level | Score | Example |
|------------|-------|---------|
| Very High | 4 | Post-MI patient non-adherent to statin. A1C 9.2% non-adherent to metformin. HFrEF non-adherent to ARB. |
| High | 3 | ASCVD risk over 20%, CKD Stage 3+ on RAS, A1C 7-9% on oral diabetes meds. |
| Moderate | 2 | Diabetes + statin (dual D10/D12 measure impact), proteinuria on RAS. |
| Low | 1 | Controlled on current therapy (BP at goal, A1C at target, LDL at goal). Non-adherence is still suboptimal but less clinically urgent. |

**Measure_Weight** — How much does this measure impact the overall star rating?

| Measure | Weight Score | Rationale |
|---------|-------------|-----------|
| D10, D11, D12 | 3 (1 for MY2026 only) | Triple-weighted in overall star calculation — temporarily reduced to single-weight for MY2026 only, returns to 3× in MY2027. With CY2027 measure removals, these carry an even larger share of the total rating when at full weight. Even at 1× for MY2026, adherence remains a strategic priority because backsliding during a reduced-weight year makes recovery harder when the weight increases. |
| D14, D13, COB, Poly-ACH | 1 | Single-weighted. |

**Timing_Factor** — How much measurement year is left to intervene?

| Months Remaining | Score | Rationale |
|-----------------|-------|-----------|
| 6 or more (Jan-Jun) | 1.0 | Full runway. Build the pipeline. |
| 3-5 (Jul-Sep) | 1.5 | Urgency increasing. Escalate Tier 2 patients. |
| 1-2 (Oct-Nov) | 2.0 | Critical window. Focus exclusively on Tier 1. |
| Less than 1 (Dec) | 2.5 | Last chance. 90-day fill or single fill to close the gap. |

### Worked Examples

**Example 1 — High priority:**
Member on atorvastatin, PDC 78%, history of MI (secondary prevention), October.
```
PDC_Proximity: 10 (78% PDC)
Clinical_Risk: 4 (Very High — post-MI, secondary prevention)
Measure_Weight: 3 (D12, triple-weighted)
Timing_Factor: 2.0 (October, 1-2 months remaining)
Score = 10 x 4 x 3 x 2.0 = 240
```
Interpretation: Immediate intervention. Senior pharmacist outreach. One 90-day fill converts this member and addresses a direct mortality-reduction gap.

**Example 2 — Moderate priority:**
Member on lisinopril, PDC 72%, BP at goal (128/78), July.
```
PDC_Proximity: 5 (72% PDC)
Clinical_Risk: 1 (Low — BP at goal on current therapy)
Measure_Weight: 3 (D11, triple-weighted)
Timing_Factor: 1.5 (July, 3-5 months remaining)
Score = 5 x 1 x 3 x 1.5 = 22.5
```
Interpretation: Monitoring priority. Intervention if resources allow — 90-day conversion and auto-refill could close this gap without intensive outreach.

**Example 3 — Low priority (investigate first):**
Member on metformin, PDC 45%, no recent labs available, March.
```
PDC_Proximity: 1 (below 60%)
Clinical_Risk: 2 (Moderate — diabetes, but no data to escalate)
Measure_Weight: 3 (D10, triple-weighted)
Timing_Factor: 1.0 (March, 6+ months remaining)
Score = 1 x 2 x 3 x 1.0 = 6
```
Interpretation: Low priority for adherence push. Investigate first — did the prescriber discontinue? Did the patient switch to insulin (which would exclude from D10)? Is there an A1C showing control without the med? Clinical appropriateness review before intervention.

### Score Interpretation Ranges

| Score | Priority Level | Action |
|-------|---------------|--------|
| 60 or above | Immediate — highest priority | Assign to senior pharmacist. Intervene within 48 hours. |
| 30-59 | Active — standard priority | Schedule outreach within 1-2 weeks. |
| 10-29 | Monitoring | Intervene if resources allow. Auto-refill, 90-day conversion. |
| Below 10 | Low / Investigate | Review clinical appropriateness before pushing adherence. |

---

## Root Cause Analysis and Intervention Mapping

### Common Root Causes and Matched Interventions

| Root Cause | Indicators | Intervention |
|-----------|-----------|-------------|
| Cost/copay barrier | Claims show fills at quarter boundaries, gaps after plan stage changes | Therapeutic substitution to lower-cost alternative, patient assistance programs, LIS eligibility check |
| Side effects | Fill pattern shows 1-2 fills then stop, restart, stop | Prescriber discussion re: alternative agent, dose adjustment, timing change |
| Complexity/polypharmacy | 10+ concurrent meds, multiple daily dosing | **Medication synchronization (med sync)** — align all fill dates to a single date per month. One pharmacy visit, all meds filled, all PDCs covered simultaneously. This matters for Stars because a single med sync enrollment can reduce gaps across D10, D11, and D12 at once while simplifying the patient's regimen. Coordinate with pharmacy to short-fill or extend fills to align dates, then maintain the sync date going forward. Also supports adherence packaging and simplification review. |
| Forgetfulness/logistics | Irregular fill patterns, no clinical reason | Auto-refill enrollment, 90-day supply conversion, mail order pharmacy |
| Intentional non-adherence | Patient-reported, provider notes | Motivational interviewing, shared decision-making, clinical pharmacist consult |
| Therapy perceived unnecessary | Statin in primary prevention, asymptomatic HTN | Education focus — but respect patient autonomy, document discussion |
| Access/transportation | Gaps correlate with rural zip codes, no mail order | Mail order conversion, community pharmacy outreach, delivery programs |
| Prescriber discontinuation | No refill authorized after office visit | Verify — if intentional, flag for measure exclusion consideration |
| Safety-driven discontinuation | Hypoglycemic episode then stopped sulfonylurea, fall then stopped sedative | **Do not treat as an adherence gap.** This may be a rational safety decision. Flag as potential deprescribing opportunity. Verify with prescriber. If clinically appropriate to remain off therapy, document rationale and investigate measure exclusion. |

### SUPD/D12 Cross-Measure Trap

> **⚠️ CRITICAL**: When intervening on SUPD (D14) by initiating a new statin, the member enters
> the D12 (Statin Adherence) denominator immediately. If the member fills once and stops, SUPD
> improves but D12 gets WORSE. **Every SUPD statin initiation must include an adherence support
> plan from day one.** Pair with: 90-day supply, auto-refill enrollment, follow-up at 30 days.
> This is the single most common cross-measure mistake in Stars optimization.

### Intervention Escalation Ladder
1. **Automated**: Refill reminder (IVR, text, app notification)
2. **Technician outreach**: Fill transfer, 90-day conversion, mail order setup, **med sync alignment**
3. **Pharmacist outreach**: Barrier assessment, clinical intervention, prescriber fax
4. **Prescriber collaboration**: Therapy modification, alternative agent, documented rationale
5. **In-person/telehealth CMR**: Comprehensive review for complex patients

Note on intervention intensity: Evidence consistently shows that more intensive interventions (pharmacist-led motivational interviewing, comprehensive medication reviews) produce larger adherence improvements than automated reminders alone. However, intensive interventions cost more per member. Match intervention intensity to priority score — reserve senior pharmacist time for scores above 60, use automated and technician-level interventions for scores 10-29. This maximizes clinical impact per resource dollar spent.

---

## Intervention Timing

### Quarterly Checkpoints
- **Q1 (Jan-Mar)**: Baseline identification, new-to-therapy monitoring, carryover fill assessment
- **Q2 (Apr-Jun)**: Mid-year review, identify members falling below trajectory, 90-day conversions, **med sync enrollment**
- **Q3 (Jul-Sep)**: Escalation window — members below 70% PDC need intensive engagement
- **Q4 (Oct-Dec)**: Critical window — every fill counts, focus on Tier 1 patients, last-chance 90-day fills

### December Crunch
- A 90-day fill in early October covers the member through December 31
- A 30-day fill on December 1 covers December (31 days) — can be enough to push over 80%
- Plans that wait until Q4 to intervene are playing catch-up — build the pipeline in Q1-Q2
- **Important**: While Q4 fills count and the December playbook is operationally necessary, sustainable adherence interventions started in Q1-Q2 produce better long-term outcomes than last-minute gap-closing. Plans should not design their entire Stars strategy around the December crunch — it should be a safety net, not the primary approach.

---

## Important Considerations

### The Healthy Adherer Effect
Adherent patients tend to have better outcomes — but adherence is also a marker for overall healthy behavior. Patients who reliably fill medications are more likely to keep appointments, follow dietary recommendations, and engage with their care. This doesn't negate the importance of adherence interventions, but it means that pushing PDC from 45% to 80% through refill reminders alone may not produce the same clinical benefit as a patient who organically maintains 85% PDC because they're broadly engaged in their health. The implication for Stars programs: pair adherence interventions with broader health engagement strategies when possible, especially for Tier 2 and Tier 3 patients. A refill reminder fixes PDC. A pharmacist conversation that addresses barriers, builds trust, and connects the patient to resources fixes the patient.

### Adherence ≠ Clinical Success
An 80% PDC does not guarantee therapeutic success. A patient can be perfectly adherent to a subtherapeutic dose, a wrong drug for their condition, or a medication they no longer need. Adherence is a process measure, not an outcome measure. The clinical risk layering in this skill exists specifically to prevent the trap of optimizing adherence on a medication that shouldn't be continued. Always ask: is this the right drug, at the right dose, for the right patient — before asking whether they're taking it consistently.


path: references/guideline-drift.md
content: # Clinical Guideline Drift Detection

## Table of Contents
1. [What Is Guideline Drift](#what-is-guideline-drift)
2. [Context: Why Drift Detection Matters More in 2027+](#context-why-drift-detection-matters-more-in-2027)
3. [Drift Detection Framework](#drift-detection-framework)
4. [Drift Registry](#drift-registry)
5. [Detection Workflow](#detection-workflow)
6. [Mismatch Logging Template](#mismatch-logging-template)
7. [Sources to Monitor](#sources-to-monitor)

---

## What Is Guideline Drift

Guideline drift occurs when clinical practice guidelines update faster than the CMS measure
specifications or MIPS quality measures that reference them. This creates a gap where:

- **Clinicians** follow the updated guideline (evidence-based, current standard of care)
- **Measures** still reward or penalize based on the old guideline logic
- **Platforms** must decide whether to optimize for clinical accuracy or measure performance

This is not theoretical — it happens regularly. CMS measure specifications update on annual
cycles. Clinical guidelines update whenever the evidence warrants it. The lag creates
actionable mismatches that clinical pharmacists in Stars optimization roles must identify and
manage.

### Ethical Dimension

When guidelines and measures diverge, clinical teams face a genuine tension: do you follow the current evidence (which may hurt the measure score) or optimize for the measure (which may mean applying outdated clinical logic)? There is no universal answer. The role of this skill is to make the divergence visible so the decision is conscious and documented, not accidental.

---

## Context: Why Drift Detection Matters More in 2027+

CMS's CY2027 proposed rule removes 12 measures from Star Ratings — seven focused on operational and administrative performance, three on process of care, and two on patient experience. The clinical measures that remain carry a larger share of the overall rating. When a guideline drifts from a clinical measure specification, the impact on the star rating is now amplified. A mismatch between guideline-concordant care and measure-optimal behavior on a triple-weighted adherence measure is three times as costly as it was when administrative measures diluted the weight.

### Health Equity Considerations

CMS proposed in the CY2027 rule NOT to implement the "Excellent Health Outcomes for All" reward (previously called the Health Equity Index reward) and will continue the historical reward factor instead. CMS deferred this adjustment, but it may return in future rulemaking cycles. Monitor CMS Advance Notices and proposed rules for potential health equity reward implementation, as this could affect how measures are scored for plans serving vulnerable populations and may create new drift scenarios where guideline-concordant care intersects with equity-adjusted scoring.

---

## Drift Detection Framework

### Three-Source Comparison Model

Every clinical measure sits at the intersection of three sources. Drift occurs when any two
diverge:

```
┌─────────────────────┐
│  Clinical Guideline  │  (ACC/AHA, ADA, KDIGO, IDSA, AGS, etc.)
│  Current evidence    │
└──────────┬──────────┘
           │
    ┌──────┴──────┐
    │  MATCH?     │
    ├─────────────┤
    │             │
┌───▼───┐   ┌───▼────┐
│  CMS   │   │  MIPS   │
│ Star   │   │ Quality │
│ Measure│   │ Measure │
└───┬────┘   └───┬────┘
    │  MATCH?    │
    └─────┬──────┘
          ▼
```

### Drift Categories

**Category A — Guideline vs. CMS Star Measure**
Guideline updates a recommendation; Star measure still references old criteria.
*Impact*: Platform logic optimizing for the measure may give clinically outdated recommendations.

**Category B — Guideline vs. MIPS Quality Measure**
Guideline updates; MIPS measure still references old criteria.
*Impact*: Provider quality scores may penalize guideline-concordant care.

**Category C — CMS Star Measure vs. MIPS Measure**
Same clinical domain, different specifications between Stars and MIPS.
*Impact*: Dual-eligible patients or aligned plans may face contradictory optimization targets.

**Category D — Drug Class Definition Drift**
New drug classes or agents approved after measure specification was written.
*Impact*: New medications may not count toward measure numerators (e.g., novel diabetes agents).

### Severity Rating

- **Critical**: Drift directly affects measure scoring AND clinical recommendations diverge — following the guideline hurts the measure score or vice versa
- **Significant**: Drift creates confusion or suboptimal recommendations but doesn't directly invert measure performance
- **Monitoring**: Drift is emerging — guideline update is published but measure spec hasn't been tested against it yet

---

## Drift Registry

### Active Drifts

#### DRIFT-001: ASCVD Risk Assessment — PREVENT vs. PCE

| Field | Detail |
|-------|--------|
| **Category** | A + B (Guideline vs. CMS Star + MIPS) |
| **Severity** | Critical |
| **Guideline** | 2026 ACC/AHA Dyslipidemia Guideline |
| **Change** | Officially adopted AHA PREVENT equations as primary ASCVD risk calculator, replacing Pooled Cohort Equations (PCE). This is now the current standard of care — not a proposal, a published guideline change. |
| **CMS Star Impact** | D12 (Statin Adherence) and D14 (SUPD) measure specifications reference statin eligibility populations. Statin-benefit groups defined by risk thresholds were historically calibrated to PCE output. |
| **MIPS Impact** | MIPS Measure #438 (Statin Therapy for Prevention/Treatment of CVD) still references PCE-based risk thresholds for the eligible population denominator |
| **Clinical Significance** | PREVENT and PCE do NOT produce equivalent risk estimates for the same patient. PREVENT generally estimates lower 10-year ASCVD risk than PCE, particularly in younger patients and certain demographic groups. A patient with PCE-calculated risk above 7.5% may have PREVENT-calculated risk below 7.5%. |
| **Platform Implication** | If a platform uses PREVENT (guideline-concordant) to identify statin-eligible patients, it may identify FEWER patients than the PCE-based measure denominator expects. This creates a mismatch between clinical recommendations and measure optimization. |
| **Recommended Action** | (1) Flag the discrepancy in clinical logic. (2) Run both calculators and document when results diverge — dual-calculator approach allows guideline-concordant clinical recommendations while tracking measure-specification impact. (3) Monitor CMS Technical Notes for D12/D14 specification updates adopting PREVENT. (4) For platform logic: maintain PCE-based identification for measure compliance while flagging PREVENT-discordant cases for clinical review. (5) If CMS updates to PREVENT-based thresholds, be prepared for significant denominator changes — patient populations eligible for statin therapy may shift substantially. |
| **Date Identified** | March 2026 |
| **Status** | Active — 2026 guideline officially adopted PREVENT; no CMS measure spec update announced |

---

#### DRIFT-002: ADA Glycemic Targets — A1C vs. Time-in-Range (TIR)

| Field | Detail |
|-------|--------|
| **Category** | A (Guideline vs. CMS Star Measure) — Note: This is better classified as measure interpretation complexity rather than pure guideline drift. The guideline didn't change what medications to use — it changed how glycemic control is assessed. D10 measures medication adherence, not glycemic control. The tension is indirect but operationally real. |
| **Severity** | Significant |
| **Guideline** | 2023 ADA Standards of Care (and subsequent annual updates) |
| **Change** | Endorsed time-in-range (TIR) from continuous glucose monitoring (CGM) as a complementary — and in some cases preferred — glycemic target alongside A1C. TIR above 70% is now a recognized goal. |
| **CMS Star Impact** | D10 (Diabetes Medication Adherence) measures PDC for oral diabetes medications using pharmacy claims data. It does not measure glycemic outcomes. A patient managed primarily on CGM-guided insulin titration with minimal oral agent use could be clinically well-managed but appear non-adherent on D10 claims. |
| **MIPS Impact** | MIPS diabetes measures still reference A1C-based targets. No MIPS measure currently incorporates TIR. |
| **Clinical Significance** | A patient with excellent TIR on CGM-guided insulin therapy who takes minimal oral agents is not a D10 failure — they may be optimally managed. Pushing oral agent adherence in this context could be clinically inappropriate. |
| **Platform Implication** | Platforms flagging low oral diabetes PDC should cross-reference insulin claims and CGM device claims before targeting for adherence intervention. Patients on insulin-only regimens are excluded from D10; patients on insulin + oral agents are NOT excluded (see adherence-interventions.md for detail on this common targeting mistake). |
| **Recommended Action** | (1) Before flagging a diabetes patient for D10 non-adherence, check for concurrent insulin and CGM claims. (2) If patient is on insulin + oral agent combo with evidence of CGM use, prioritize clinical review over automated adherence outreach. (3) Document clinical rationale when oral agent non-adherence is assessed as clinically appropriate in CGM-managed patients. |
| **Date Identified** | March 2026 |
| **Status** | Active — ADA continues to emphasize TIR; CMS D10 specification unchanged |

---

#### DRIFT-003: GLP-1 Receptor Agonist Indication Ambiguity

| Field | Detail |
|-------|--------|
| **Category** | D (Drug Class Definition Drift) |
| **Severity** | Monitoring |
| **Guideline** | Multiple — ADA Standards of Care, ACC/AHA Obesity Guidelines, FDA labeling |
| **Change** | Semaglutide and other GLP-1 RAs are now widely prescribed for both type 2 diabetes AND obesity/weight management. FDA-approved indications span both conditions. Prescribing volume for obesity indications has grown dramatically. |
| **CMS Star Impact** | D10 (Diabetes Medication Adherence) denominator inclusion is drug-class-based, not indication-based. GLP-1 RAs are currently NOT in the D10 denominator (D10 covers oral diabetes medications only). However, GLP-1 RA fills may appear in pharmacy claims alongside oral diabetes agents, creating confusion about which patients are being treated for diabetes vs. obesity. |
| **MIPS Impact** | MIPS diabetes measures reference diabetes diagnosis codes. A patient filling semaglutide for obesity without a diabetes diagnosis should not appear in diabetes quality measure denominators — but claims data may not clearly distinguish indication. |
| **Clinical Significance** | A patient filling semaglutide for weight management who does not have diabetes should not be targeted for diabetes-related adherence interventions or Star measure optimization. |
| **Platform Implication** | Platforms should flag any patient with GLP-1 RA fills and no diabetes diagnosis (ICD-10 E11.x) for clinical review before D10 targeting. Monitor for CMS clarification on whether obesity-indication GLP-1 fills should be excluded from diabetes-related measure denominators. |
| **Recommended Action** | (1) Cross-reference GLP-1 RA fills with diabetes diagnosis codes before including in any diabetes measure targeting. (2) Flag patients on GLP-1 RA without E11.x diagnosis for clinical review. (3) Monitor CMS Technical Notes and PQA measure specifications for guidance on GLP-1 RA indication handling. (4) If CMS adds GLP-1 RAs to the D10 denominator in future specifications, prepare for significant denominator expansion. |
| **Date Identified** | March 2026 |
| **Status** | Monitoring — no CMS specification change; indication ambiguity growing as obesity prescribing increases |

---

#### DRIFT-004: Depression Screening and Follow-Up — New Measure

| Field | Detail |
|-------|--------|
| **Category** | New measure (not a drift — logged here because it affects resource allocation and clinical program planning) |
| **Severity** | Significant |
| **Guideline** | CY2027 Proposed Rule |
| **Change** | CMS proposes adding a Part C Depression Screening and Follow-Up measure starting with 2027 measurement year, scoring in 2029 Star Ratings. The measure tracks two rates: (a) percentage of eligible members screened for clinical depression, and (b) if screened positive, percentage who received follow-up care within 30 days. CMS will average the two rates for the Star Rating. This measure aligns with the USPSTF Grade B recommendation for depression screening in adults. PHQ-2 and PHQ-9 are the standard screening tools likely to be used for implementation. |
| **CMS Star Impact** | New measure added to Part C. Will contribute to overall star rating beginning 2029. Displayed on the display page for 2026 Star Ratings (2024 measurement year data) for baseline reporting. |
| **MIPS Impact** | Aligns with existing MIPS behavioral health screening measures. |
| **Clinical Significance** | Behavioral health integration into Star Ratings signals CMS prioritization of mental health. For platforms like Arine that serve populations with high comorbid depression (diabetes, cardiovascular disease, chronic pain), this measure creates opportunity to demonstrate value in cross-condition clinical programming. Depression is independently associated with medication non-adherence — members with unscreened/untreated depression are more likely to have open adherence gaps on D10, D11, and D12. Addressing depression can improve adherence outcomes. |
| **Platform Implication** | Platforms with behavioral health clinical pharmacist (BCPP) expertise can build integrated workflows that address both depression screening and medication adherence gaps in a single encounter. A member flagged for statin non-adherence who also screens positive for depression may need a fundamentally different intervention than a refill reminder — the depression may be driving the non-adherence. |
| **Recommended Action** | (1) Begin tracking depression screening rates in current member populations. (2) Identify members with comorbid depression and open medication adherence gaps — these patients may benefit from integrated interventions addressing both the behavioral health barrier and the adherence gap simultaneously. (3) Build follow-up workflows that ensure positive screens result in documented care within 30 days. (4) Monitor the 2026 display page data to establish baseline performance before the measure scores. (5) This measure aligns with the USPSTF Grade B recommendation for depression screening in adults. PHQ-2 and PHQ-9 are the standard screening tools — ensure platform workflows support structured screening documentation using these validated instruments. |
| **Date Identified** | March 2026 |
| **Status** | Active — proposed in CY2027 rule, display-only in 2026, scoring expected 2029 |

---

*Add new drifts below using the Mismatch Logging Template.*

---

## Detection Workflow

### When to Run Drift Detection

1. **Guideline publication**: Any major clinical guideline update (ACC/AHA, ADA, KDIGO, IDSA, AGS Beers, USPSTF)
2. **CMS annual update**: When new Star Ratings Technical Notes or Call Letter is published
3. **CMS mid-year updates**: CMS sometimes issues mid-year clarifications or technical corrections — do not assume specifications are static between annual cycles
4. **MIPS update cycle**: When MIPS measure specifications are updated
5. **FDA approval**: New drug class or indication that may affect measure denominators/numerators
6. **Measure retirement announcements**: When CMS announces measure retirements, assess impact on platform logic and resource allocation — a retired measure may free resources for remaining measures
7. **Health equity methodology changes**: CMS considered implementing a Health Equity Index (HEI / "Excellent Health Outcomes for All" reward) for 2027 Star Ratings but deferred it in the CY2027 proposed rule, continuing the historical reward factor instead. Monitor for future rulemaking — if HEI is implemented in a later cycle, it will affect how measures are scored for plans serving vulnerable populations and may create new drift scenarios where guideline-concordant care intersects with equity-adjusted scoring.
8. **Quarterly review**: Scheduled review of existing drifts for resolution status

### Detection Steps

1. **Identify the clinical domain** affected by the update
2. **Map to Star measures**: Which CMS Star measures reference this clinical domain?
3. **Map to MIPS measures**: Which MIPS quality measures reference this domain?
4. **Compare specifications**: Does the updated guideline change any of the following?
   - Risk thresholds used to define eligible populations
   - Drug class definitions (what counts, what doesn't)
   - Treatment targets (A1C goals, BP targets, LDL thresholds)
   - Screening/monitoring intervals
   - Recommended first-line agents
5. **Assess impact**: Does the change create a scenario where following the guideline diverges from measure-optimal behavior?
6. **Cross-measure impact assessment**: When a guideline changes, assess impact across ALL potentially affected measures, not just the obvious one. Example: a statin guideline change affects D12 (statin adherence), D14 (SUPD), and potentially D10 (if the patient also has diabetes). Assess all three.
7. **Account for contract-level aggregation**: Star Ratings are calculated at the contract level, not the plan level. A guideline change may affect different geographic regions or plan benefit packages within a contract differently, but the contract-level rating will not reflect this variation. When assessing drift impact, consider whether the drift disproportionately affects certain sub-populations within the contract.
8. **Log the drift** using the template below
9. **Recommend action**: Dual-logic, monitor, or flag for CMS feedback

---

## Mismatch Logging Template

Copy this template for each new drift entry:

```markdown
#### DRIFT-XXX: [Short Title]

| Field | Detail |
|-------|--------|
| **Category** | [A / B / C / D / New Measure] |
| **Severity** | [Critical / Significant / Monitoring] |
| **Guideline** | [Guideline name and year] |
| **Change** | [What changed in the guideline] |
| **CMS Star Impact** | [Which measures affected and how] |
| **MIPS Impact** | [Which MIPS measures affected and how] |
| **Clinical Significance** | [What this means for patient care] |
| **Platform Implication** | [What this means for platform logic / recommendations] |
| **Recommended Action** | [Specific steps to manage the drift] |
| **Date Identified** | [Month Year] |
| **Status** | [Active / Resolved / Monitoring] |
```

### Registry Maintenance

- Review all Active drifts quarterly
- Move to Resolved when CMS/MIPS measure specs align with guideline
- Archive Resolved drifts with resolution date and spec version
- Drifts in Monitoring status should be escalated if no spec update within 2 annual cycles

---

## Sources to Monitor

| Source | URL | Update Frequency | What to Watch For |
|--------|-----|-----------------|-------------------|
| CMS Star Ratings Technical Notes | cms.gov/medicare/health-drug-plans/part-c-d-performance-data | Annual (typically October) + mid-year corrections | Cutpoint changes, measure specification updates, methodology changes |
| CMS Advance Notice / Rate Announcement (Call Letter) | cms.gov/medicare/health-drug-plans/part-c-d-performance-data | Annual (February draft, April final) | New measures, measure retirements, weight changes, MTM eligibility threshold updates |
| CMS Proposed and Final Rules | cms.gov/newsroom/fact-sheets | Annual (proposed ~November, final ~April) | Major policy changes, measure additions/removals, health equity methodology, Star Ratings restructuring |
| MIPS Quality Measure Specifications | qpp.cms.gov | Annual | Measure denominator/numerator changes, new measures, retired measures |
| ACC/AHA Clinical Practice Guidelines | acc.org, professional.heart.org | As published (variable) | Risk calculator changes (PCE/PREVENT), treatment thresholds, drug class recommendations |
| ADA Standards of Care | diabetesjournals.org | Annual (January) + living updates | Glycemic targets, medication algorithms, TIR/CGM guidance, A1C goal modifications |
| AGS Beers Criteria | americangeriatrics.org | Every 4 years (next update ~2027) | High-risk medication list changes affecting Poly-ACH measure |
| KDIGO Guidelines | kdigo.org | As published (variable) | CKD management, RAS antagonist recommendations, BP targets in renal disease |
| USPSTF Recommendations | uspreventiveservicestaskforce.org | As published (variable) | Screening recommendations that may affect Part C measures, including depression screening |
| FDA New Drug Approvals | fda.gov/drugs | Ongoing | New drug classes, new indications for existing drugs, safety communications affecting prescribing |
| PQA (Pharmacy Quality Alliance) | pqaalliance.org | Annual | Measure specification updates — PQA develops many Part D Star measures including adherence and polypharmacy measures |


path: references/measures.md
content: # CMS Star Ratings Measures Reference

## Table of Contents
1. [Context: Why These Measures Matter More in 2027+](#context-why-these-measures-matter-more-in-2027)
2. [Triple-Weighted Part D Adherence Measures](#triple-weighted-part-d-adherence-measures)
3. [Other Key Part D Measures](#other-key-part-d-measures)
4. [Cutpoint Thresholds](#cutpoint-thresholds)
5. [Gap-to-Threshold Analysis Framework](#gap-to-threshold-analysis-framework)
6. [Measure Weight Impact on Overall Rating](#measure-weight-impact-on-overall-rating)

---

## Context: Why These Measures Matter More in 2027+

CMS's CY2027 proposed rule removes 12 measures from Star Ratings — seven focused on operational and administrative performance, three on process of care, and two on patient experience. The Part D adherence measures (D10, D11, D12) are NOT being removed. With fewer measures in the overall calculation, the triple-weighted adherence measures carry an even larger share of the total star rating. Every percentage point of improvement on these measures is worth more going forward than it was before.

### MY2026 Temporary Weight Change

For Measurement Year 2026 ONLY, CMS is temporarily reducing the triple-weighted adherence measures (D10, D11, D12) to single-weighted (1×). They are expected to return to triple-weighted (3×) in MY2027. This temporary change means the adherence measures carry less relative weight in the 2028 Star Ratings (which use MY2026 data) — but plans should NOT reduce adherence investment. The measures return to 3× weight the following year, and backsliding during a single-weight year makes recovery harder when the weight increases again.

### MY2026 Sociodemographic Status (SDS) Risk Adjustment

Starting MY2026, CMS will apply sociodemographic status (SDS) risk adjustment to adherence scores. Adherence measures will now account for age, gender, low-income subsidy (LIS) status, disability status, and dual eligibility. This means plans serving high-need populations may see adjusted scores that better reflect the difficulty of achieving adherence in those populations. Monitor how SDS adjustment affects your plan's specific scores relative to cutpoints.

### MY2026 PDC Calculation Change — IP/SNF Stays

Starting MY2026, days when members are hospitalized or in skilled nursing facilities (SNFs) will NO LONGER be excluded from PDC calculations. Previously, inpatient days were removed from the denominator. Now they count — which means members with hospitalizations may show lower PDC scores even if they were adherent before and after the stay. This is a methodology change that could lower PDC rates across the board and must be factored into gap-to-threshold projections.

---

## Triple-Weighted Part D Adherence Measures

These carry 3× weight in the overall star calculation. Small improvements here have outsized
impact on the overall star rating. With CY2027 measure removals, the relative weight of these measures increases further.

### D10 — Medication Adherence for Diabetes Medications
- **Numerator**: Members with PDC ≥ 80% for oral diabetes medications
- **Denominator**: Members with ≥2 fills of oral diabetes medications in the measurement year
- **Drug classes**: Biguanides (metformin), sulfonylureas (glipizide, glimepiride, glyburide), TZDs (pioglitazone, rosiglitazone), DPP-4 inhibitors (sitagliptin, saxagliptin, linagliptin, alogliptin), SGLT2 inhibitors (canagliflozin, dapagliflozin, empagliflozin, ertugliflozin), meglitinides (repaglinide, nateglinide), alpha-glucosidase inhibitors (acarbose, miglitol)
- **SGLT2 inhibitor note**: SGLT2 inhibitors are oral diabetes medications and ARE included in D10. Given the rapid expansion of SGLT2 inhibitor prescribing for diabetes, heart failure, and CKD, confirm that your platform correctly includes these agents in the D10 denominator. Patients prescribed an SGLT2 inhibitor primarily for heart failure or CKD (with or without diabetes) may appear in the D10 denominator if they also have diabetes medication claims — cross-reference diagnosis codes.
- **Exclusions**: Members with ESRD, insulin-only regimens. **Note: patients on insulin + oral agents (combo) ARE included — only insulin-only is excluded.**
- **Weight**: 3× (temporarily reduced to 1× for MY2026 only; returns to 3× in MY2027)
- **Note**: GLP-1 RAs are NOT currently included in the D10 adherence measure denominator (D10 covers oral agents only; GLP-1 RAs are injectable). However, CMS has discussed potential inclusion as the diabetes treatment landscape evolves and GLP-1 RA use increases dramatically. Verify GLP-1 RA status against the current year's CMS Technical Notes before each measurement year.

### D11 — Medication Adherence for RAS Antagonists
- **Numerator**: Members with PDC ≥ 80% for RAS antagonists
- **Denominator**: Members with ≥2 fills of ACEIs, ARBs, or direct renin inhibitors
- **Drug classes**: ACEIs, ARBs, aliskiren, ACEI/CCB combinations, ARB/CCB combinations
- **Inclusions**: Sacubitril/valsartan (Entresto) IS included — the ARB component (valsartan) counts toward D11 RAS adherence. Do not mistakenly exclude ARNI patients from the denominator. **Verification note**: Confirm ARNI inclusion against the current year's CMS Technical Notes. While clinically logical (the product contains valsartan, an ARB), explicit CMS confirmation in the measure specification is prudent, especially as ARNI prescribing volume grows in heart failure populations.
- **Exclusions**: None specific beyond standard measure exclusions (hospice, ESRD where applicable)
- **Weight**: 3× (temporarily reduced to 1× for MY2026 only; returns to 3× in MY2027)

### D12 — Medication Adherence for Cholesterol (Statins)
- **Numerator**: Members with PDC ≥ 80% for statin medications
- **Denominator**: Members with ≥2 fills of statin medications
- **Drug classes**: All statins including combination products (statin + ezetimibe, statin + CCB)
- **Exclusions**: Ezetimibe monotherapy, PCSK9 inhibitors (evolocumab, alirocumab), bempedoic acid, and inclisiran (PCSK9-pathway siRNA agent) are NOT included. Inclisiran lowers LDL but is not a statin — do not assume it counts toward D12.
- **Weight**: 3× (temporarily reduced to 1× for MY2026 only; returns to 3× in MY2027)

---

## Other Key Part D Measures

### D14 — Statin Use in Persons with Diabetes (SUPD)
- **Numerator**: Members with diabetes aged 40-75 who received a statin fill
- **Denominator**: Members aged 40-75 with diabetes (≥2 fills of diabetes meds OR diabetes diagnosis)
- **Exclusions**: Members with ESRD, hospice, rhabdomyolysis history
- **Weight**: 1×
- **Note**: Binary measure — any statin fill counts, not adherence-based

> **⚠️ CRITICAL: SUPD ≠ Statin Adherence.** SUPD (D14) requires only ONE statin fill during
> the measurement year. D12 (Statin Adherence) requires PDC ≥80%. These are different measures
> with different denominators. **A member who fills a statin once for SUPD but does not continue
> enters the D12 denominator as a non-adherent member — improving D14 while HURTING D12.**
> Every SUPD statin initiation MUST include an adherence support plan from day one. Failing to
> coordinate SUPD and D12 interventions is the single most common cross-measure mistake in Stars
> optimization. Do not initiate a statin for SUPD without a plan to keep the member adherent.

### D13 — MTM Completion Rate for CMR
- **Numerator**: Members who received a CMR
- **Denominator**: Members enrolled in MTM program
- **Weight**: 1×
- **Scoring status**: CMS decided NOT to retire the CMR completion rate measure. The measure remains on the display page. Verify the exact scoring timeline (display-only vs. scored) against the current year's CMS Technical Notes and the most recent Rate Announcement. **Verification note**: The 2026 Rate Announcement confirmed the measure will remain; verify whether scoring resumes in measurement year 2027 or remains display-only longer.
- **Note**: Regardless of exact scoring timeline, build CMR infrastructure now. Plans that wait until the measure scores again will be caught off-guard. See mtm-cmr.md for optimization logic.

### Polypharmacy Measures

#### COB — Concurrent Use of Opioids and Benzodiazepines
- **Numerator**: Members with ≥2 fills of an opioid AND ≥2 fills of a benzodiazepine with ≥30 concurrent days
- **⚠️ INVERSE MEASURE — lower rate = better performance.** A high rate is BAD. Improvement means REDUCING the numerator. When reporting plan rates, always label directionality.
- **Weight**: 1×

#### Poly-ACH — Use of High-Risk Medications in the Elderly (Anticholinergic Burden)
- **Numerator**: Members ≥65 with ≥2 fills of high-risk medications per AGS Beers Criteria
- **⚠️ INVERSE MEASURE — lower rate = better performance.** A high rate is BAD. Improvement means REDUCING the numerator (deprescribing, switching to safer alternatives). When reporting plan rates, always label directionality.
- **Weight**: 1×
- **Key drug classes**: First-generation antihistamines (diphenhydramine, hydroxyzine), tertiary TCAs (amitriptyline, doxepin), antispasmodics, skeletal muscle relaxants (cyclobenzaprine, methocarbamol), non-benzo hypnotics (zolpidem), certain antiarrhythmics (amiodarone for rhythm control in elderly), long-acting sulfonylureas (glyburide) in older adults
- **Note**: This is not exhaustive — reference the full AGS Beers Criteria list for comprehensive screening. Beers Criteria is updated approximately every 4 years (most recent 2023, next update expected ~2027).

---

## Cutpoint Thresholds

CMS publishes cutpoints annually. These shift based on national performance distribution.

### 2025 Star Rating Year Cutpoints (Reference)

Note: 2026 Star Ratings cutpoints have been published by CMS in the 2026 Star Ratings Data Tables, available at cms.gov/medicare/health-drug-plans/part-c-d-performance-data. Verify against the 2026 cutpoints for current analysis. The table below uses 2025 values as a reference baseline.

| Measure | 2-Star | 3-Star | 4-Star | 5-Star |
|---------|--------|--------|--------|--------|
| D10 Diabetes Adherence | <72% | 72-79% | 80-86% | ≥87% |
| D11 RAS Adherence | <74% | 74-81% | 82-87% | ≥88% |
| D12 Statin Adherence | <73% | 73-80% | 81-87% | ≥88% |
| D14 SUPD | <78% | 78-82% | 83-86% | ≥87% |
| D13 MTM/CMR | <60% | 60-74% | 75-89% | ≥90% |

> **Important**: Cutpoints shift annually based on clustering methodology. Always verify against
> the current year's **CMS Medicare Advantage and Part D Star Ratings Technical Notes**,
> published annually and available at **cms.gov/medicare/health-drug-plans/part-c-d-performance-data**.
> The above are representative thresholds — use actual published cutpoints for live plan analysis.

### Cutpoint Methodology
- CMS uses a clustering algorithm (not fixed percentiles) to set thresholds
- Cutpoints can tighten when national performance improves
- Plans must monitor mid-year performance against BOTH current and projected cutpoints

---

## Gap-to-Threshold Analysis Framework

### Input Required
- Plan-level performance rate for each measure (percentage)
- Current CMS cutpoints for the measurement year
- Plan's current overall star rating

### Analysis Steps

1. **Calculate absolute gap**: `threshold - plan_rate` for each measure at each star level
2. **Identify nearest achievable threshold**: The next star level up where the gap is smallest
3. **Estimate member impact**: `gap × eligible_denominator = members needed to convert`
4. **Rank by effort-to-impact ratio**: 
   - `impact_score = measure_weight × star_level_change / members_to_convert`
   - Triple-weighted measures get 3× multiplier
5. **Flag cliff measures in BOTH directions**:
   - **Upside cliff**: Where the plan is within 2% of reaching the NEXT star level
   - **Downside cliff**: Where the plan is within 5% of DROPPING a star level
   - Run both checks on every measure, every time. A plan focused only on climbing can backslide and lose a star it already had — especially on triple-weighted measures where that loss is 3× as damaging.

### Output Format

For each measure, report:
- Current rate and star level
- **Upside gap**: Distance to next star threshold (absolute % and member count)
- **Downside gap**: Distance to dropping a star level (absolute % and member count)
- **Cliff alert** (if within 2% upside or 5% downside) — label clearly as OFFENSIVE or DEFENSIVE
- Weighted impact score
- Recommended intervention focus

### Critical Thresholds

The 3-star to 4-star boundary is the most valuable:
- Plans at 3.5+ stars qualify for quality bonus payments (QBP)
- QBP can represent millions in additional revenue
- A single triple-weighted measure moving from 3-star to 4-star can shift the overall rating

---

## Measure Weight Impact on Overall Rating

### How Weights Work
- Part D measures are weighted and combined with Part C measures for overall star rating
- Triple-weighted measures (D10, D11, D12) have 3× impact on the Part D summary
- Part D summary contributes to overall star rating alongside Part C, CAHPS, and HOS
- With CY2027 removing 12 measures (mostly administrative), the remaining clinical measures — including all Part D adherence measures — carry a proportionally larger share of the overall rating

### Strategic Prioritization
1. **First priority**: Triple-weighted adherence measures below 4-star cutpoint
2. **Second priority**: Any measure at risk of dropping a star level (defensive)
3. **Third priority**: SUPD (1× weight but often high-volume, easy intervention — coordinate with D12)
4. **Fourth priority**: Polypharmacy measures (inverse — identify members driving the numerator)
5. **Fifth priority**: MTM/CMR (build infrastructure now regardless of scoring timeline)


path: references/mtm-cmr.md
content: # MTM/CMR Optimization

## Table of Contents
1. [Context: Why CMR Infrastructure Matters Now](#context-why-cmr-infrastructure-matters-now)
2. [CMR as a Star Measure](#cmr-as-a-star-measure)
3. [CMR Candidate Prioritization](#cmr-candidate-prioritization)
4. [Structuring CMRs for Clinical Impact](#structuring-cmrs-for-clinical-impact)
5. [Cross-Measure Alignment](#cross-measure-alignment)
6. [Completion Rate Optimization](#completion-rate-optimization)

---

## Context: Why CMR Infrastructure Matters Now

CMS's CY2027 proposed rule removes 12 measures from Star Ratings — mostly administrative and process measures. The clinical measures that remain carry a larger share of the overall rating. CMR completion rate (D13) is not being removed, and CMS decided not to retire it despite considering doing so. With expanded MTM eligibility criteria effective 2025, the eligible population has grown — which means both the opportunity and the operational challenge have increased. Plans that build CMR infrastructure now will be positioned to perform when the measure matters most.

---

## CMR as a Star Measure

### D13 — MTM Completion Rate for CMR

- **What it measures**: Percentage of MTM-enrolled members who received a CMR
- **Scoring status**: Display-only through MY2026 (not scored toward Star Rating). CMS decided NOT to retire the measure — the 2026 Rate Announcement (April 2025) confirmed it will remain due to expanded MTM eligibility requirements. Scoring is expected to resume in MY2027 (impacting 2029 Star Ratings), per industry analysis including Arine's own published strategy guidance. **Verify scoring timeline annually against the most recent CMS Technical Notes and Rate Announcement.**
- **Why it matters now**: Even while display-only, CMS is collecting and publishing the data. Plans that ignore this measure will be caught off-guard when scoring resumes. Build infrastructure now.

### CMS MTM Program Eligibility (Current Criteria — Effective January 1, 2025)

The CY2024 Final Rule (April 2024) established expanded MTM targeting criteria effective January 1, 2025:

- **Chronic conditions**: ≥3 chronic conditions from the **10 core chronic diseases** (plans must target all 10):
  1. Alzheimer's disease
  2. Bone disease — arthritis (osteoporosis, osteoarthritis, rheumatoid arthritis)
  3. Chronic heart failure
  4. Chronic kidney disease
  5. Diabetes
  6. Dyslipidemia
  7. End-stage liver disease
  8. HIV/AIDS
  9. Hypertension
  10. Mental health (depression, schizophrenia, bipolar disorder, chronic/disabling mental health conditions)
- **Medications**: ≥2-8 Part D covered medications (plans set threshold within this range; CMS proposed lowering the maximum from 8 to 5 but did NOT finalize this change)
- **Cost threshold**: Likely to exceed **$1,276 in 2026** in annual Part D drug costs (indexed annually in the CMS Call Letter — always check the current year's threshold)
- Plans may use lower thresholds for any criteria (and many do to maximize intervention reach)

> **⚠️ DENOMINATOR TRAP**: Lowering MTM eligibility thresholds expands the D13 denominator. If
> outreach capacity doesn't scale proportionally, completion rate drops. A plan that voluntarily
> lowers thresholds or that sees expanded eligibility from the 2025 criteria changes without
> adding pharmacist capacity may inadvertently hurt D13 performance. CMS estimated a 2-3x
> increase in eligible population from the original proposed criteria changes, though the actual
> increase is smaller because the drug count threshold was not lowered from 8 to 5.

### The Completion Rate Problem
Many plans chase CMR completion rate as a checkbox: call the member, get them on the phone for 15 minutes, document the CMR, move on. This hits the numerator but wastes a clinical touchpoint. The skill should optimize for both completion AND clinical impact.

---

## CMR Candidate Prioritization

### Prioritization Matrix

Score each MTM-eligible member on two axes:
1. **Clinical complexity** — how much benefit could a CMR deliver?
2. **Completion likelihood** — how likely is the member to engage?

#### Clinical Complexity Scoring

| Factor | Points | Rationale |
|--------|--------|-----------|
| Open adherence gap on triple-weighted measure (D10/D11/D12) | +5 | CMR directly addresses Star measure |
| ≥12 concurrent medications | +4 | High polypharmacy burden, more DRPs likely |
| Recent hospitalization (≤90 days) | +4 | Transition of care = medication discrepancies |
| High-risk medication per AGS Beers (≥65 years) | +3 | Deprescribing opportunity |
| A1C ≥9% or BP >150/90 | +3 | Uncontrolled chronic condition |
| COB flag (concurrent opioid + benzo) | +3 | Safety concern + Star measure impact |
| ≥3 prescribers | +2 | Fragmented care, reconciliation needed |
| Renal/hepatic impairment | +2 | Dose adjustment opportunities |
| Recent formulary change or generic conversion | +1 | Confusion, potential non-adherence |

#### Completion Likelihood Scoring

| Factor | Points | Rationale |
|--------|--------|-----------|
| Completed CMR in prior year | +4 | Strong predictor of repeat engagement |
| Engaged with pharmacy (≥1 call answered in past 6 months) | +3 | Reachable member |
| Preferred language = English (or bilingual pharmacist available) | +2 | Reduces barrier |
| Has caregiver on file | +2 | Alternative contact for engagement |
| Multiple failed contact attempts | -3 | Resource drain, consider alternative channels |
| Cognitive impairment documented | -1 | May need caregiver involvement (not a disqualifier). **CMS allows caregiver-assisted CMRs — per 42 CFR § 423.153(d)(1)(vii)(B)(2), if the beneficiary is unable to accept the offer to participate, the MTM provider may perform the CMR with the beneficiary's prescriber, caregiver, or other authorized representative. Without noting this, teams skip cognitively impaired patients entirely and lose completions they could have captured.** |
| Declined CMR previously | -2 | Respect autonomy, try different approach |

#### Combined Priority Score
```
CMR Priority = Clinical_Complexity × Completion_Likelihood_Modifier
```

- **Tier 1** (Score ≥30): Schedule first, assign to senior clinical pharmacist
- **Tier 2** (Score 15-29): Schedule in standard rotation
- **Tier 3** (Score <15): Batch outreach, consider abbreviated review if resources are limited

---

## Structuring CMRs for Clinical Impact

### Beyond the Checkbox: Clinical Framework

A CMR that just verifies the medication list is a waste of a pharmacist's time. Structure every CMR around three pillars:

#### Pillar 1 — Medication Reconciliation
- Verify every active medication against fill data, provider records, and patient report
- Identify discrepancies: medications on the list not being taken, medications being taken not on the list
- Flag duplications, therapeutic overlaps, and missing therapies
- **Output**: Accurate, reconciled medication list

#### Pillar 2 — Drug Therapy Problem Identification
- Screen for DRPs using structured categories:
  - Unnecessary drug therapy
  - Needs additional therapy (gaps in evidence-based treatment)
  - Ineffective drug (wrong drug for condition)
  - Dose too low / too high
  - Adverse drug reaction
  - Drug-drug interactions (often the highest-severity finding in polypharmacy CMRs — screen explicitly using interaction databases, flag severity as major/moderate/minor with clinical significance)
  - Non-adherence (with root cause assessment)
- Prioritize DRPs by clinical severity and actionability
- **Output**: Prioritized DRP list with recommendations for prescriber

#### Pillar 3 — Star Measure Integration
- For every CMR, explicitly check:
  - Is this member below 80% PDC on any triple-weighted measure?
  - Is this member on the COB or Poly-ACH numerator?
  - Does this member qualify for SUPD but lack a statin fill?
  - Are there 90-day supply or mail order conversion opportunities?
- **Output**: Measure-specific interventions documented and tracked

### CMR Documentation Standards
- Use CMS-required format: Personal Medication List (PML) + Medication Action Plan (MAP)
- Include specific, actionable recommendations (not generic "discuss with doctor")
- Document prescriber outreach and outcomes
- Track DRP resolution rates — this is your quality metric
- CMS encourages beneficiaries to bring their PML and MAP to their annual wellness visit or any medical encounter — reinforce this during every CMR

---

## Cross-Measure Alignment

### Using CMRs to Move Multiple Measures Simultaneously

Every CMR is an opportunity to impact multiple Star measures in a single encounter:

| During CMR, Check For | Star Measure Impacted |
|----------------------|----------------------|
| PDC gap on diabetes meds | D10 (3× weight) |
| PDC gap on RAS antagonists | D11 (3× weight) |
| PDC gap on statins | D12 (3× weight) |
| No statin fill in diabetic patient 40-75 | D14 (SUPD) |
| Concurrent opioid + benzo use | COB (inverse) |
| High-risk med in elderly per Beers | Poly-ACH (inverse) |
| CMR completed | D13 |

A single well-executed CMR for a complex patient can impact 3-5 measures. This is the efficiency argument for investing in clinical pharmacist-led CMRs rather than checkbox completions.

### Cross-Measure Conflict Resolution
Sometimes measure optimization conflicts. Never sacrifice clinical judgment for a measure.

**Example 1 — Psychiatric Beers medication:**
- Patient on a Beers-listed medication for a valid psychiatric indication (BCPP territory)
- Stopping the medication helps Poly-ACH but may destabilize the patient
- **Resolution**: Document clinical rationale. Flag the conflict. Psychiatric stability takes precedence.

**Example 2 — Concurrent opioid + benzodiazepine (COB + Poly-ACH):**
- Patient on long-term low-dose benzodiazepine for GAD plus chronic opioid for pain
- Hits both COB and Poly-ACH measures simultaneously
- Abrupt benzodiazepine discontinuation is clinically dangerous — seizure risk, rebound anxiety, withdrawal
- **Resolution**: Document rationale. Attempt gradual taper if clinically appropriate (e.g., 10-25% reduction every 2-4 weeks). Never deprescribe unsafely to hit a measure. If taper is not appropriate, document why and accept the measure impact. This is a common scenario that Stars programs handle badly when driven by claims analysts instead of clinical pharmacists.

---

## Completion Rate Optimization

### Outreach Strategy

| Channel | Best For | Completion Rate |
|---------|----------|----------------|
| Telephonic (pharmacist-led) | Complex patients, relationship-based | Highest |
| Video/telehealth | Tech-comfortable patients, visual aid helpful | High |
| In-person (community pharmacy) | Patients who prefer face-to-face | High but limited scale |
| Asynchronous (portal-based review) | Simple med lists, engaged patients | Moderate |

### Scheduling Optimization
- **Avoid Q4 surge**: Spread CMRs across the year; Q4-only strategy leads to rushed, low-quality encounters
- **Pair with refill calls**: When a pharmacy calls about a refill, offer CMR scheduling
- **Post-discharge window**: 7-14 days after hospitalization — highest clinical relevance, member is engaged
- **Annual wellness visit alignment**: Coordinate with PCP AWV for comprehensive encounter

### Tracking Metrics
- **Completion rate**: Numerator/denominator (the Star measure)
- **Time-to-CMR post-MTM enrollment**: Days from MTM enrollment to CMR completion. Plans that enroll members in MTM but delay the CMR for months lose completions as members disenroll, become unreachable, or the measurement year ends. **Best practice target: CMR within 60 days of MTM enrollment.**
- **DRP identification rate**: Average DRPs per CMR (target: ≥2 actionable DRPs per CMR)
- **DRP resolution rate**: Percentage of identified DRPs with prescriber follow-through
- **Measure conversion rate**: Members who cross 80% PDC threshold after CMR intervention
- **Cost avoidance**: Hospitalization/ED visit reduction in CMR-completed population vs. matched controls


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