Section 21.4: Data Contribution to Real-World Evidence Studies

21.4.1 The “Why”: The Efficacy vs. Effectiveness Gap

In your career, you have encountered this scenario a thousand times: a groundbreaking, “miracle” drug gets FDA approval based on a flawless Phase III clinical trial, but when it’s used in your “real” patients, the results are… underwhelming. The adherence is poor, the side effects are more pronounced, or it just doesn’t seem to work as well as the glossy journal reprint claimed. This is the efficacy versus effectiveness gap, and it is the single most important concept in post-marketing research.

• Efficacy: Does the drug work in a perfect, controlled, academic environment? This is what a Randomized Controlled Trial (RCT) measures. It uses “perfect” patients (no comorbidities, 100% adherent, no challenging concomitant meds) to prove a scientific concept.
• Effectiveness: Does the drug work in the “real world”? This is what Real-World Evidence (RWE) measures. It looks at “messy” patients—the elderly, the non-adherent, the patient with 12 other meds and 3 comorbidities—and asks, “What actually happens?”

For decades, healthcare decisions were made almost exclusively on efficacy data from RCTs. But payers, providers, and manufacturers have all realized this is not enough. A payer doesn’t care if a drug can work; they care if it does work in their patient population, and if it’s worth the $100,000 price tag.

This has created an insatiable demand for high-quality Real-World Data (RWD)—and specialty pharmacy, it turns out, is sitting on a goldmine. Because of your high-touch, longitudinal model, you are not just a dispenser. You are a data generator. Your daily dispensing records, your clinical notes from adherence calls, your documentation of side effects—this is the raw material. When aggregated and de-identified, this RWD is analyzed to create RWE, which is now one of the most powerful forces shaping formulary decisions, value-based contracts, and even FDA approvals. This section is your masterclass on how your daily work becomes that evidence.

21.4.2 Defining the RWE Landscape: RWD, RWE, and the SP Advantage

To be an advanced practitioner in this space, you must speak the language with precision. The terms “RWD” and “RWE” are often used interchangeably, but they are critically different.

• Real-World Data (RWD): This is the raw material. It is the data collected from patients as part of their normal healthcare, *outside* of a clinical trial. It is the pile of messy, unorganized “comment cards” from the analogy.
• Real-World Evidence (RWE): This is the finished product. It is the insight and knowledge generated from the analysis of RWD. It is the polished, final report to the restaurant owners.

Your pharmacy collects RWD. Your pharmacy’s data science team (or a partner) generates RWE.

Masterclass Table: Sources of Real-World Data

Specialty pharmacy data is not the only source of RWD, but it has a unique and powerful advantage over all other sources. Understanding this advantage is key to understanding your value.

RWD Source	What It Is	Key Strength	Key Weakness (The “Gap”)
Insurance Claims Data	Billing records from payers/PBMs (e.g., medical diagnoses, prescription *fills*).	Massive Scale. Covers millions of lives. Good for “big picture” epidemiology.	No Clinical Detail. You know a prescription was filled, not if it was taken. You have zero clinical data (no lab results, no side effects).
Electronic Medical/Health Records (EMR/EHR)	Digital charts from hospitals and doctor’s offices (e.g., lab results, vital signs, doctor’s notes).	Rich Clinical Detail. This is the “why.” It contains the diagnosis, the labs, the vitals.	Fragmented & Unstructured. Data is locked in thousands of different, non-connected EMRs. Most data is “unstructured” (free-text doctor’s notes) and hard to analyze.
Patient-Generated Data	Data from wearables (Apple Watch), apps, and social media.	High-Frequency & Patient-Centric. Can capture daily activity, sleep, and patient sentiment.
Specialty Pharmacy (SP) Data	Your dispensing records, clinical call notes, adherence data, and patient-reported outcomes.	The “Goldilocks” Dataset. It’s the only dataset that reliably links dispensing (from claims), adherence (from your calls), and clinical outcomes (from your notes/PROs) for a specific, high-cost drug over time (longitudinal).	Limited Scope. It’s a “deep” dataset on a “narrow” population (only *your* patients on *those* drugs). It’s missing the full EMR/hospital data unless you are an integrated SP.

21.4.3 The SP’s Data Goldmine: Anatomy of RWD

When a manufacturer or payer signs a “data sharing agreement” with your specialty pharmacy, what are they actually getting? They are getting access to a meticulously curated, de-identified dataset built from your daily work. Your patient profile is not just a clinical tool; it is a research-grade database.

Let’s break down the specific, high-value data points you generate every day and why they are so valuable for RWE.

Masterclass Table: The Specialty Pharmacy RWD Asset Map

Data Category	Specific Data Point	Source	Value for Real-World Evidence (RWE)
Patient & Payer Data	Patient Demographics	Patient Intake Form	Subgroup Analysis. Allows researchers to ask: “Does the drug work as well in patients > 65?” or “Is adherence different between men and women?”
	Geography (De-identified)	Patient Address (Zip3)	Health Disparities. Used to analyze care patterns, access issues, and outcomes in different regions (e.g., “urban vs. rural”).
	Payer & Plan Type	Insurance Card	Payer Strategy. Allows payers to benchmark their own population (e.g., “How does our Medicare plan’s adherence compare to our Commercial plan’s?”).
	Diagnosis Code (ICD-10)	Prescription / EMR	Indication-Specific Analysis. Essential for separating cohorts (e.g., “Humira for RA” vs. “Humira for Crohn’s”).
	Co-pay / Out-of-Pocket Cost	Adjudication Record	Financial Toxicity. A critical RWE data point. “What is the relationship between co-pay amount and adherence?”
Dispensing & Logistics Data	Dispensing Record	Pharmacy System	The core of the dataset. Tracks drug, dose, quantity, and days’ supply for every fill.
	Adherence (MPR / PDC)	Pharmacy System (Calculated)	The #1 Metric. This is the most sought-after RWE. Proves the “real-world” adherence rate, which is the best predictor of effectiveness.
	Persistency	Pharmacy System (Calculated)	The #2 Metric. “How long does a patient stay on the drug before giving up?” A drug with 90% adherence but only 3 months of persistency is a failure.
	Refill Gaps	Pharmacy System (Calculated)	Used to model and predict when patients are most likely to drop off therapy.
	Dose Titration / Change	Pharmacy System	Tracks the patient’s dosage journey. “Do most patients have to titrate down due to side effects?”
	Shipping / Delivery	Logistics System	Can be used to analyze the impact of shipping delays, cold chain breaks, or carrier performance on adherence.
Clinical & Operational Data	Reason for Discontinuation	Pharmacist Clinical Call (Structured Note)	THE GOLDEN DATA POINT. Why did the patient stop? “Side Effects,” “High Cost,” “Perceived Lack of Efficacy.” This is vastly superior to claims data, which just shows the fills stopped.
	Patient-Reported Outcomes (PROs)	Pharmacist Clinical Call (Survey)	Captures the patient’s voice. “On a scale of 1-10, what is your pain level?” This is essential for proving “value.”
	Adverse Events (AEs)	Pharmacist Clinical Call (PV Intake)	The core of post-marketing safety surveillance (see Sec 21.3). Feeds directly into signal detection.
	Pharmacist Call / EMR Snippet	“Patient reports their latest A1c was 6.8.” This links the drug (dispensing data) to an objective clinical outcome (the lab). Incredibly valuable.
	Concomitant Medications	Allows for RWE studies on drug-drug interactions that were never studied in an RCT.
	Clinical Interventions	Pharmacist Note	“Pharmacist counseled on side effect management.” “Pharmacist coordinated PA.” This proves the SP’s value, not just the drug’s.

Tutorial: The Key Adherence Metrics (MPR vs. PDC)

Your SP system calculates these automatically, but you must understand what they mean, as they are the most common RWE endpoints. They are both attempts to measure adherence over a period of time (e.g., 1 year).

1. Medication Possession Ratio (MPR)

This is the simplest (and most flawed) measure. It’s the “sum of days’ supply” divided by the “number of days in the period.”

$$ \text{MPR} = \frac{\text{Sum of Days’ Supply for all fills in period}}{\text{Number of Days in Period (e.g., 365)}} \times 100 $$

The “Gotcha”: MPR can be > 100%. If a patient refills early, they might acquire 390 “days’ supply” in a 365-day period. This (107% MPR) falsely suggests perfect adherence. This is why MPR is considered an outdated, “quick and dirty” metric.

2. Proportion of Days Covered (PDC)

This is the modern, preferred, and more conservative standard. It looks at the period and asks, “On how many unique days *within* this period did the patient have drug on hand?” It caps each day at “1,” so the max score is 100%.

$$ \text{PDC} = \frac{\text{Number of unique days patient was covered in period}}{\text{Number of Days in Period (e.g., 365)}} \times 100 $$

Example:

• A patient gets a 30-day supply on Jan 1.
• They refill *early* on Jan 25 (another 30-day supply).
• MPR: They have 60 days’ supply. For January, their MPR is $60/31 = 193\%$. (Nonsense)
• PDC: The system looks at each day. Jan 1 (covered), Jan 2 (covered)… Jan 31 (covered). The number of unique days covered is 31. The PDC is $31/31 = 100\%$. (A much more accurate reflection).

Why it matters: When a payer asks for an RWE study on adherence, you must use PDC. It is the accepted currency for proving value.

21.4.4 The Legal & Ethical Framework: De-identification & HIPAA

Your patient’s data is their most sensitive information. Your ability to perform any RWE research is 100% dependent on your absolute, unyielding compliance with the Health Insurance Portability and Accountability Act (HIPAA). As an SP, you are a “Covered Entity.” You cannot, under any circumstances, sell a list of “patients on Drug X” to a manufacturer. This would be a massive, company-ending breach.

So how is this data ever shared? It must be rendered anonymous. This can happen in two primary ways:

• Aggregated Data: This is high-level summary data. E.g., “For the 5,200 patients on Drug X in our system, the average PDC is 81%.” No individual patient data is shared. This is simple and very low-risk.
• De-Identified Data: This is the “patient-level” dataset that researchers want. It’s a massive spreadsheet where each row is a unique, anonymous patient. This data is no longer considered Protected Health Information (PHI) and can be shared… *if* it is de-identified according to one of two methods.

Masterclass: The 2 Methods of HIPAA De-Identification

Method 1: The “Safe Harbor” Method (The 18 Identifiers)

This is the most common, checklist-based method. The rule states that a dataset is de-identified if you remove all 18 of the following Protected Health Information (PHI) identifiers for the patient, their relatives, or their employers. Your IT team will “scrub” the dataset to remove or modify these fields.

Identifier	How It’s “Scrubbed” or Removed
1. Names	Completely removed.
2. Geographic subdivisions smaller than a state	All specific addresses/cities/counties are removed. You may only keep the State, or sometimes the first 3 digits of a Zip Code (a “Zip3”) if it’s not a rare/small area.
3. All elements of dates (except year)	All specific dates (birth date, admit date, dispense date) are removed. You can keep “Year” (e.g., “Birth Year: 1965”). Dates are often converted to “days from start” (e.g., “Day 0”, “Day 30”, “Day 60”).
4. Phone numbers	Completely removed.
5. Fax numbers	Completely removed.
6. Email addresses	Completely removed.
7. Social Security numbers	Completely removed.
8. Medical record numbers	Completely removed.
9. Health plan beneficiary numbers	Completely removed.
10. Account numbers	Completely removed.
11. Certificate/license numbers	Completely removed.
12. Vehicle identifiers and serial numbers	Completely removed.
13. Device identifiers and serial numbers	Completely removed.
14. Web URLs	Completely removed.
15. IP addresses	Completely removed.
16. Biometric identifiers (fingerprints, voice)	Completely removed.
17. Full-face photos	Completely removed.
18. Any other unique identifying number or code	This is the catch-all. This means the internal Patient ID must be “hashed” or replaced with a new, random, non-traceable Research ID.

Method 2: The “Expert Determination” Method (The Statistician)

This is a more flexible but more complex method. It allows you to keep *some* of the identifiers (like a specific date, or a 5-digit zip code) *if* a qualified statistician performs an analysis and formally determines that the “risk of re-identification” of any individual is “very small.” This is used when the specific dates or locations are essential for the research (e.g., a study on COVID-19 outbreaks).

In almost all cases, your SP will use the Safe Harbor method.

21.4.5 How RWE Studies are Built: A Pharmacist’s Guide to Study Design

Once you have a clean, de-identified dataset, you can’t just “look at it.” You must apply a formal scientific method to it to generate valid evidence. As an advanced pharmacist, you must be able to understand and critique these study designs.

Workhorse Design 1: The Observational Cohort Study

This is the most common and intuitive RWE design.

• What it is: You identify a group of people (a “cohort”) who are starting a drug. You then follow them forward in time to see what happens to them.
• Common Use: A “head-to-head” study.
  • Step 1 (Identify Cohorts): From your data, you find 5,000 patients starting New Drug A and 5,000 patients starting Old Drug B for the same diagnosis.
  • Step 2 (Follow Forward): You follow all 10,000 patients for 2 years.
  • Step 3 (Measure Outcomes): You compare the outcomes. “The Drug A cohort had a 1-year persistency of 65%, while the Drug B cohort only had a 40% persistency.”
  • Conclusion (RWE): Drug A appears to be more effective in the real world at keeping patients on therapy.

The Solution: Propensity Score Matching (PSM)

This is the “magic” of modern RWE, and you must understand it. Propensity Score Matching is a statistical “speed dating” technique used to create a fair, “apples-to-apples” comparison.

It works like this:

1. Step 1: Build the Propensity Model. You take all your patients (e.g., the 10,000 from before) and you ignore their drug for a second. You use a statistical model to calculate a “propensity score” for every single patient. This score (from 0.0 to 1.0) is the calculated *probability* that a patient would have received New Drug A, based on all their known characteristics (age, sex, comorbidities, prior meds, etc.).
2. Step 2: The “Statistical Twins.” You now have 5,000 patients in the Drug A group and 5,000 in the Drug B group, each with a propensity score. The algorithm goes through the Drug A group and, for each patient, tries to find a “statistical twin” in the Drug B group who has an almost identical propensity score.
3. Step 3: Discard the Un-Matchables. The algorithm may find that 1,000 of the “very sick” Drug A patients have no “twin” in the Drug B group. And 1,000 of the “very healthy” Drug B patients have no “twin” in the Drug A group. It throws them out of the study.
4. Step 4: The Matched Cohort. You are left with a new, smaller, but perfectly balanced study. You now have 4,000 Drug A patients and 4,000 Drug B patients who are, statistically speaking, identical in terms of their age, sickness, comorbidities, etc.
5. Step 5: The Fair Comparison. Now you can compare the outcomes. “In this propensity-score-matched cohort, Drug A still had a 60% persistency rate, while Drug B had 42%.” This RWE is now powerful, believable, and can be published in a major journal.

21.4.6 Masterclass: The 3 Main Consumers of Your RWE

Your data is collected, de-identified, and analyzed using robust methods. Who buys it, and what do they do with it? Your work directly informs the three most powerful players in healthcare.

1. Payers (Health Plans, PBMs, and P&T Committees)

Primary Question: “Which drug provides the best value (outcome per dollar) for my population?”

Payers are the biggest consumers of RWE. They use it to make high-stakes financial decisions. As an SP pharmacist, your data directly fuels their P&T Committee.

• Use Case: Formulary Decisions (Head-to-Head).
  Scenario: Two new oral oncology drugs (Drug A and Drug B) are approved for the same cancer. They cost $15,000/month each. The RCTs show they have *identical* efficacy (e.g., 6-month Progression-Free Survival). Which one should the P&T committee make the “preferred” agent?
  Your RWE Contribution: The payer commissions an RWE study from your SP data. The study (using PSM) finds that Drug A’s real-world discontinuation rate due to side effects (which you meticulously logged) is 40% at 3 months. Drug B’s discontinuation rate is only 10%.
  The Decision: The P&T committee makes Drug B the preferred agent. They know that a patient who stops a drug at 3 months gets no benefit, costing them $45,000 for nothing. Your clinical notes just saved the health plan millions.
• Use Case: Value-Based Contracts (VBCs).
  Scenario: A manufacturer wants their new $300,000 gene therapy on the formulary. The payer is skeptical.
  Your RWE Contribution: The payer and manufacturer create a VBC using your SP data as the referee. The contract states: “The payer will pay for the drug. But if the SP’s RWE (based on patient-reported outcomes and lab data) does not show a 50% reduction in hospitalizations for this patient cohort after 2 years, the manufacturer must rebate 40% of the drug’s cost.”
  The Decision: Your clinical data collection is now the legal mechanism for a multi-million dollar contract.

2. Manufacturers (Pharma: HEOR & Medical Affairs)

Primary Question: “How can I prove my drug’s total value (clinical, economic, humanistic) to payers?”

The manufacturer’s Health Economics and Outcomes Research (HEOR) department uses your RWE to build their “value story.”

• Use Case: The HEOR Value Dossier.
  Scenario: The manufacturer’s new drug (Drug X) costs $20,000/year more than the old standard (Drug Y). Payers are rejecting it.
  Your RWE Contribution: The manufacturer sponsors a study of your data. The RWE shows that patients on Drug X have significantly better PDC (adherence) because it’s dosed once-a-month instead of daily. This better adherence is linked (in your data) to fewer “flares,” which in turn is linked (in claims data) to a 30% reduction in ER visits and hospitalizations.
  The Decision: The HEOR team builds a “cost-effectiveness model” showing that while Drug X costs $20,000 more, it *saves* the plan $35,000 in medical costs. Your data just proved the drug’s value, and the manufacturer now uses this study in every P&T meeting.
• Use Case: Phase IV & Label Expansion.
  Scenario: The FDA approved Drug X but, as a condition, requires a Phase IV study to monitor its long-term safety.
  Your RWE Contribution: Instead of a costly new trial, the manufacturer sponsors an RWE study using your pharmacy’s PV and AE data (see Sec 21.3) collected over 5 years. This “passive” data collection fulfills their regulatory requirement.

3. Regulators (The FDA)

Primary Question: “Is this drug safe in the real world, and can RWE be used to make regulatory decisions?”

This is the newest and most exciting frontier. The FDA, through its SENTINEL Initiative and new guidance, is now actively using RWE to make decisions.

• Use Case: Pharmacovigilance & Safety.
  Scenario: As discussed in 21.3, your AE reports are RWD. They flow into the FAERS database.
  Your RWE Contribution: The FDA mines this data for signals. If they find a signal (e.g., Drug X and pancreatitis), they can issue a new safety warning or, in extreme cases, pull the drug from the market. Your SP data is a primary source for this.
• Use Case: Label Expansion (The New Frontier).
  Scenario: A drug (Ibrance) is approved for breast cancer. Doctors, in practice, are using it “off-label” for a rare type of male breast cancer.
  Your RWE Contribution: The manufacturer can’t run an RCT—the condition is too rare. Instead, they partner with major SPs and cancer EMR companies to create a large RWE study. This study analyzes the RWD from the “off-label” use and shows that it is safe and effective.
  The Decision: In 2019, the FDA did exactly this. They approved a new indication for Ibrance based *entirely on RWE*. Your daily dispensing and clinical data could, in the future, be the primary evidence for a new FDA approval.

21.4.7 Your Role: From Clinical Pharmacist to RWE Data Steward

This entire, multi-billion dollar RWE ecosystem hinges on one thing: data quality. A study built on garbage data will produce garbage evidence. This is where your role as an Advanced Specialty Pharmacist comes full circle. You are not a data-entry clerk; you are a data steward. The quality of your documentation in your daily calls *directly* impacts the quality of the evidence that shapes your entire profession.

21.4.8 Conclusion: The Pharmacist as Data Scientist

Your role as a specialty pharmacist has fundamentally evolved. You are no longer at the “end” of the drug development pipeline; you are at the *center* of its real-world evaluation. The data you generate every day—by processing a fill, calculating a PDC, and, most importantly, by *talking* to your patient and *documenting* that conversation in a structured way—is the most valuable asset in modern healthcare decision-making.

In this section, you have learned the critical difference between efficacy (RCTs) and effectiveness (RWE). You now understand that your SP is a source of “Goldilocks” data that uniquely links dispensing, adherence, and clinical outcomes. You are a legal data steward, bound by HIPAA and using Safe Harbor de-identification to protect your patients while enabling research. You are a budding epidemiologist, able to understand the difference between a simple cohort study and a powerful, propensity-score-matched analysis.

Ultimately, you are the direct partner to payers, manufacturers, and regulators. The RWE you generate will decide which drugs are preferred on formularies, which drugs are supported by Value-Based Contracts, and which drugs receive new safety warnings or even new indications from the FDA. This is no longer a side-project; it is a core, advanced competency of the modern specialty pharmacist.