Section 11.5: Adherence Analytics (MPR, PDC, Gap Analysis)

11.5.1 The “Why”: “If You Can’t Measure It, You Can’t Manage It”

In the preceding sections of this module, we have established the profound importance of medication adherence and persistency in specialty pharmacy. We have explored the multifaceted barriers patients face (11.1), mastered the communication techniques to overcome them (11.2), designed longitudinal follow-up protocols (11.3), and examined the digital tools that amplify our efforts (11.4). Now, we arrive at the critical question: How do we know if any of this is actually working? How do we quantify adherence, identify patients who are struggling, measure the impact of our interventions, and demonstrate the value of our services to payers, manufacturers, and accrediting bodies?

The answer lies in adherence analytics. This final section is a practical, hands-on masterclass in the calculation, interpretation, and application of the key metrics used throughout the healthcare industry to measure medication adherence. While persistency (Section 11.3) measures the duration of therapy, adherence metrics measure the intensity and consistency of medication use during that period. Your community pharmacy experience likely exposed you to these concepts, perhaps in the context of Medicare Star Ratings for diabetes, hypertension, or statin adherence. As a CASP, you must achieve a deeper, more nuanced mastery.

We will focus on the three most common and important claims-based adherence metrics:

1. Medication Possession Ratio (MPR): The traditional, simpler metric.
2. Proportion of Days Covered (PDC): The current industry standard, preferred by quality organizations like PQA and NCQA.
3. Gap Analysis: A related concept focusing on the time between refills.

For each, we will provide a detailed tutorial covering:
– The precise calculation methodology with worked examples.
– The advantages and (significant) disadvantages of each metric.
– How these metrics are used operationally in specialty pharmacy for risk stratification, intervention targeting, and reporting.
– The critical limitations of relying solely on dispensing data and the importance of clinical context.

Mastering these analytics is not just an academic exercise. It is the language you will use to identify patients needing your coaching skills, to justify your clinical programs, and to prove the tangible value your specialty pharmacy delivers in improving patient outcomes and managing healthcare costs. This is how we translate our high-touch care into hard data.

11.5.2 Deep Dive: Medication Possession Ratio (MPR)

The Medication Possession Ratio (MPR) is the oldest and simplest method for estimating adherence using pharmacy claims data. It essentially calculates the percentage of a time period during which the patient “possessed” enough medication, based on the total days’ supply dispensed.

The MPR Calculation

MPR is calculated for a specific patient, for a specific medication (or class), over a defined time interval (e.g., 365 days).

Where:
– $\sum (\text{Days’ Supply for all fills in interval})$: Add up the days’ supply for every fill of the target medication dispensed during the specified time period (e.g., 1 year).
– $\text{Number of days in interval}$: The total length of the measurement period (e.g., 365 days).

MPR Tutorial: Worked Example

Scenario: Patient starts Drug X (30-day supply) on January 1st. We want to calculate their MPR over a 180-day period (Jan 1 – June 29).

Fill History:

• Fill 1: Jan 1 (Day 1) – 30 Days Supply
• Fill 2: Jan 28 (Day 28) – 30 Days Supply
• Fill 3: Feb 25 (Day 56) – 30 Days Supply
• Fill 4: Mar 28 (Day 87) – 30 Days Supply
• Fill 5: Apr 25 (Day 115) – 30 Days Supply
• Fill 6: May 23 (Day 143) – 30 Days Supply
• Fill 7: June 28 (Day 179) – 30 Days Supply (This fill occurs within the 180-day interval)

Calculation Steps:

1. Sum of Days’ Supply: $7 \text{ fills} \times 30 \text{ days/fill} = 210 \text{ days}$
2. Number of days in interval: $180 \text{ days}$
3. Calculate MPR: $ \left( \frac{210 \text{ days}}{180 \text{ days}} \right) \times 100\% = 1.1667 \times 100\% = 116.7\% $

Interpretation: The patient’s MPR is 116.7%. What does this mean? It means they possessed enough medication to cover 116.7% of the days in the interval. This immediately highlights the major flaw of MPR.

MPR: Advantages and (Major) Disadvantages

Due to these significant limitations, particularly the overestimation issue, MPR has largely been replaced by PDC as the preferred metric for quality measurement and research.

11.5.3 Deep Dive: Proportion of Days Covered (PDC)

The Proportion of Days Covered (PDC) is the current industry standard for measuring adherence using claims data. It addresses the major flaw of MPR by measuring the percentage of days within the time interval that the patient actually had medication available, accounting for overlapping days’ supply. PDC is capped at 100% (or 1.0).

PDC is the metric used by the Pharmacy Quality Alliance (PQA) and the National Committee for Quality Assurance (NCQA) for most adherence-based quality measures, including Medicare Star Ratings.

The PDC Calculation

PDC looks at each day in the interval and determines if the patient was “covered” by a dispensed medication on that day, based on the fill date and days’ supply.

Where:
– $\text{Number of unique days covered}$: Count each unique day within the interval that falls within the days’ supply of any fill dispensed for the medication. Overlapping days are counted only once.
– $\text{Number of days in interval}$: The total length of the measurement period (e.g., 365 days).

PDC Tutorial: Worked Example (Using the Same Scenario)

Scenario: Patient starts Drug X (30-day supply) on January 1st. Interval = 180 days (Jan 1 – June 29).

Fill History:

• Fill 1: Jan 1 (Day 1) – 30 Days Supply (Covers Days 1-30)
• Fill 2: Jan 28 (Day 28) – 30 Days Supply (Covers Days 28-57) ← Overlap!
• Fill 3: Feb 25 (Day 56) – 30 Days Supply (Covers Days 56-85) ← Overlap!
• Fill 4: Mar 28 (Day 87) – 30 Days Supply (Covers Days 87-116) ← Slight Gap Here!
• Fill 5: Apr 25 (Day 115) – 30 Days Supply (Covers Days 115-144) ← Overlap!
• Fill 6: May 23 (Day 143) – 30 Days Supply (Covers Days 143-172) ← Overlap!
• Fill 7: June 28 (Day 179) – 30 Days Supply (Covers Days 179-208)

Calculation Steps (Day-by-Day Method):

1. Go day by day through the 180-day interval.
2. Determine if the patient had medication available on that specific day based on the fill dates and days’ supply.
3. Crucially: If multiple fills cover the same day, count that day only ONCE.
4. Count the total number of unique days covered within the interval.

Visualizing the Coverage:

Counting the Covered Days:

• Days 1-85 are continuously covered by Fills 1, 2, 3 (despite overlaps). Total = 85 days.
• Day 86 is NOT covered (Gap!).
• Days 87-172 are continuously covered by Fills 4, 5, 6 (despite overlaps). Total = (172 – 87 + 1) = 86 days.
• Days 173-178 are NOT covered (Gap!).
• Days 179-180 are covered by Fill 7 (within the 180-day interval). Total = 2 days.
• Total Days Covered within the 180-day interval = 85 + 86 + 2 = 173 days.

Calculate PDC: $ \left( \frac{173 \text{ days}}{180 \text{ days}} \right) \times 100\% = 0.9611 \times 100\% = \bf 96.1\% $

Interpretation: The patient’s PDC is 96.1%. This means they had medication available for 96.1% of the days in the 180-day period. This is a much more realistic and clinically meaningful number than the 116.7% MPR.

PDC: Advantages and Disadvantages

PDC Thresholds and Interpretation

A key aspect of PDC is the use of a threshold to categorize patients as “adherent” or “non-adherent” for quality measurement purposes. The most common threshold, used for Medicare Star Ratings for diabetes, hypertension, and statin medications, is:

PDC ≥ 80% = Adherent

While 80% is a common benchmark, the optimal PDC threshold can vary depending on the drug class and disease state. For some critical therapies (e.g., HIV antiretrovirals, transplant immunosuppressants), adherence rates closer to 95% are often necessary for optimal outcomes. However, the 80% threshold is widely used as a standard operational benchmark.

Your Goal: To use PDC calculations proactively to identify patients falling below the 80% threshold (or a higher threshold for critical drugs) and intervene before the measurement period ends.

11.5.4 Deep Dive: Gap Analysis

While PDC gives you an overall adherence score over a long period, Gap Analysis provides a more immediate, real-time indicator of potential adherence problems. It focuses on the time elapsed between refills.

The Concept of Gap Days

A “gap day” occurs when the number of days between two consecutive fills is greater than the days’ supply of the earlier fill. It represents a day (or days) when the patient likely did not have medication on hand, assuming they started the new fill immediately upon receiving it.

Gap Analysis Tutorial: Worked Example (Using Same Scenario)

Let’s calculate the gaps between fills for our patient:

• Gap between Fill 1 & 2: $(\text{Day } 28 – \text{Day } 1) – 30 \text{ days} = 27 – 30 = -3 \text{ days} \rightarrow \bf 0 \text{ Gap Days}$ (Overlap)
• Gap between Fill 2 & 3: $(\text{Day } 56 – \text{Day } 28) – 30 \text{ days} = 28 – 30 = -2 \text{ days} \rightarrow \bf 0 \text{ Gap Days}$ (Overlap)
• Gap between Fill 3 & 4: $(\text{Day } 87 – \text{Day } 56) – 30 \text{ days} = 31 – 30 = \bf 1 \text{ Gap Day}$
• Gap between Fill 4 & 5: $(\text{Day } 115 – \text{Day } 87) – 30 \text{ days} = 28 – 30 = -2 \text{ days} \rightarrow \bf 0 \text{ Gap Days}$ (Overlap)
• Gap between Fill 5 & 6: $(\text{Day } 143 – \text{Day } 115) – 30 \text{ days} = 28 – 30 = -2 \text{ days} \rightarrow \bf 0 \text{ Gap Days}$ (Overlap)
• Gap between Fill 6 & 7: $(\text{Day } 179 – \text{Day } 143) – 30 \text{ days} = 36 – 30 = \bf 6 \text{ Gap Days}$

Interpretation: The Gap Analysis immediately highlights two periods where the patient likely ran out of medication: a 1-day gap after Fill 3 and a more significant 6-day gap after Fill 6. The PDC score (96.1%) already reflected these gaps, but the Gap Analysis pinpoints exactly when they occurred.

Using Gap Analysis Operationally

Gap analysis is primarily used for real-time risk identification and intervention triggering.

• Refill Reminders: Pharmacy systems use expected refill dates (last fill date + days supply – buffer) to trigger reminder calls or texts, preventing gaps before they happen.
• Late Refill Alerts: If a patient goes beyond their expected refill date by a certain threshold (e.g., 7 days past due), an alert is generated for the pharmacist or technician to proactively call the patient and investigate the reason for the delay (potential barrier identification).
• Identifying Potential Discontinuation: A large gap (e.g., >30 days beyond expected refill) is a strong indicator that the patient may have discontinued therapy. This triggers a more intensive outreach effort to confirm status and address reasons for stopping.
• Targeting Adherence Interventions: Patients with frequent or large gaps are prioritized for adherence coaching calls using MI and problem-solving.

Gap analysis provides the granular, timely data needed to operationalize the proactive follow-up protocols discussed in Section 11.3.

11.5.5 Operationalizing Adherence Analytics: From Data to Action

Calculating MPR, PDC, and gaps is only half the battle. The true value lies in how this data is systematically integrated into the pharmacy workflow to drive clinical action and improve patient care.

Use Case 1: Risk Stratification for Targeted Follow-Up

You cannot call every patient every month. Adherence analytics allow you to efficiently allocate your clinical resources to the patients most likely to benefit from intervention. A common approach involves creating risk tiers based on PDC and gap data:

This allows pharmacists to focus their intensive coaching (MI, SDM) on the High-Risk tier, while using technicians and technology to monitor the Medium and Low-Risk groups.

Use Case 2: Quality Reporting & Accreditation

Adherence metrics, particularly PDC, are central to external quality reporting and accreditation requirements.

• Medicare Star Ratings: Pharmacies play a role in health plan Star Ratings through measures like PDC for diabetes, RASA, and statins. High performance impacts plan reimbursement and reputation.
• URAC & ACHC Accreditation: Specialty pharmacy accreditation bodies require pharmacies to have robust programs for monitoring adherence, intervening with non-adherent patients, and reporting on their performance using standard metrics like PDC.
• Payer Scorecards: Many payers provide specialty pharmacies with scorecards comparing their adherence rates (e.g., PDC > 80%) for specific drug classes against benchmarks or competing pharmacies. Performance can impact network status and reimbursement.

Your pharmacy’s ability to accurately calculate, track, and improve these metrics is essential for business success and demonstrating quality.

Use Case 3: Manufacturer Contracts & Performance

Manufacturers often tie access to limited distribution drugs (LDDs) or performance-based rebates to the specialty pharmacy’s ability to achieve specific adherence and persistency targets (often PDC > 80% or 85% at 12 months).

Pharmacies must collect and report this data back to the manufacturer, demonstrating the effectiveness of their patient support programs. High performance strengthens the pharmacy’s relationship with the manufacturer and ensures continued access to critical therapies.

Critical Limitations: What Claims Data CANNOT Tell You

While essential, relying solely on MPR/PDC calculated from dispensing data has major blind spots. As an advanced pharmacist, you must always interpret these numbers with clinical context and skepticism.

Beyond Claims: Other Adherence Measurement Methods (Briefly)

While claims data (MPR/PDC) is the standard for large-scale analytics due to its availability, other methods exist, each with pros and cons:

• Patient Self-Report: Asking the patient (e.g., “How many doses did you miss last week?”).
  Pros: Simple, direct insight into ingestion.
  Cons: Highly subject to recall bias and social desirability bias (patients often overestimate adherence).
• Pill Counts: Counting remaining pills at follow-up.
  Pros: More objective than self-report.
  Cons: Impractical for specialty pharmacy (mailed meds), assumes patient didn’t discard pills (“pill dumping”).
• Electronic Monitoring Devices: Smart pill bottles/caps, connected injectors tracking usage.
  Pros: Objective data on access/administration timing.
  Cons: Expensive, intrusive, doesn’t confirm ingestion (patient could open bottle and not take pill).
• Biomarkers/Drug Levels: Measuring drug concentration in blood/urine.
  Pros: Confirms actual ingestion and exposure.
  Cons: Invasive, expensive, only reflects recent adherence (short half-life drugs), levels affected by PK variability.

In practice, specialty pharmacies rely primarily on claims data (PDC, Gaps) combined with insights gathered during pharmacist follow-up calls (structured self-report and clinical assessment) to get the most holistic view of adherence.

11.5.6 The Pharmacist’s Role: Clinician, Analyst, & Coach

Adherence analytics are powerful tools, but they are only as effective as the clinician interpreting and acting upon them. Your role as a CASP involves wearing multiple hats:

• The Analyst: Understanding how PDC and gaps are calculated, recognizing their limitations, and interpreting the data within the patient’s clinical context. Questioning outliers or scores that don’t match the clinical picture.
• The Risk Assessor: Using PDC thresholds and gap alerts to systematically identify patients who require proactive outreach and intervention.
• The Investigator: Using a low PDC score or a large gap as a starting point for a conversation, not an accusation. Employing MI skills (Open-Ended Questions, Reflections) to explore the reasons behind the number. (“I noticed there was a bit of a delay in getting your last refill. Can you tell me what was happening around that time?”)
• The Coach: Using adherence data (when appropriate and shared collaboratively) during MI or SDM conversations to help patients see their own patterns and motivate change. (“Looking at your refill history together, it seems like things were going really well until [Life Event], and that’s when the missed doses started. How can we build a plan to get back on track?”)
• The Advocate: Using adherence data to advocate for changes in therapy (e.g., switching to a less frequent dosing schedule if adherence is poor), or to justify the need for additional support resources (e.g., home nursing visits, financial aid).
• The Reporter: Ensuring accurate documentation of adherence assessments, barriers, interventions, and outcomes, contributing to the pharmacy’s quality reporting and value demonstration.

Ultimately, adherence metrics are tools to enhance, not replace, your clinical judgment and patient relationship. They help you ask better questions and focus your coaching where it’s needed most.

11.5.7 Conclusion & Module Summary: The Data-Driven Adherence Expert

This section concludes our deep dive into Module 11: Patient Adherence & Persistency. We began by dissecting the complex web of barriers that prevent specialty patients from succeeding on therapy (11.1). We then mastered the essential communication skills—Health Literacy, Motivational Interviewing, and Shared Decision-Making—needed to engage patients as partners in their care (11.2). We learned to architect proactive, longitudinal follow-up protocols and understand the critical concept of persistency (11.3). We explored how digital tools can amplify our reach and efficiency (11.4). And finally, in this section, we gained practical mastery over the adherence analytics—MPR, PDC, and Gap Analysis—that allow us to measure adherence, target interventions, and demonstrate our value.

You are now equipped not just to dispense specialty medications, but to actively manage patient adherence and persistency as a core clinical function. You understand that adherence is a complex behavior, influenced by a multitude of factors, and that supporting it requires a blend of clinical knowledge, investigative skill, communication expertise, structured follow-up, technological savvy, and data analysis.

By integrating these principles into your daily practice—using the 5 Pillars to diagnose barriers, MI and SDM to coach and collaborate, structured protocols and digital tools to manage proactively, and adherence analytics to measure and refine—you embody the role of the Certified Advanced Specialty Pharmacist. You are the engine driving patient success, ensuring these life-changing therapies deliver on their promise, one patient, one conversation, and one data point at a time.