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MODULE 26: DATA ANALYTICS & POPULATION HEALTH IN UTILIZATION MANAGEMENT
Section 2: Identifying Trends and Outliers
Step into the role of a data detective. Learn how to use data to spot providers with unusual prescribing patterns, identify geographic hotspots for high-cost drugs, and uncover opportunities for intervention.
SECTION 26.2
Identifying Trends and Outliers: The Pharmacist as a Data Detective
From sifting through data to spotting critical patterns that impact cost and care.
26.2.1 The “Why”: Moving from Reactive to Proactive Intervention
In Section 1, we learned the language of PBM data. Now, we learn to use that language to find the crucial stories hidden within the numbers. A single prior authorization review is a reactive process: a request arrives, you apply criteria, and you make a decision. It’s essential work, but it’s fundamentally a one-at-a-time activity. The real power of a PBM, and the next evolution of your role as a managed care pharmacist, lies in shifting from this reactive stance to a proactive one. This is achieved by systematically analyzing aggregated data to identify trends, patterns, and—most importantly—outliers.
An outlier is a data point that deviates significantly from the norm. It could be a physician who prescribes a high-cost specialty drug at ten times the rate of their peers. It could be a pharmacy with an unusually low generic dispensing rate. It could be a small town with a sudden, inexplicable spike in the use of a new migraine medication. These outliers are signals in the noise. They are questions that demand an answer. Answering these questions is the core function of a data detective. Is the outlier provider simply an expert in a rare disease, or are they misinformed about clinical guidelines? Is the pharmacy’s low generic rate a result of their patient population, or is there a contractual issue? Is the geographic “hotspot” the result of a new drug rep’s effective marketing, or is there an underlying environmental or demographic reason for the trend?
Identifying these patterns is the first step toward strategic, population-level intervention. Instead of correcting one prescription, you can design a program that educates a hundred providers. Instead of processing one PA denial for a non-preferred drug, you can provide the data that justifies a formulary change affecting thousands of members. This is how PBMs manage cost and quality at scale. Your ability to look at a spreadsheet or a dashboard, apply your clinical knowledge, and say, “That doesn’t look right,” is an incredibly valuable skill. This section will teach you the fundamental techniques for spotting these trends and outliers, transforming you from a case reviewer into a true clinical data analyst.
Retail Pharmacist Analogy: The Monday Morning Opioid Detective
Imagine it’s a Monday morning at your busy community pharmacy. While reviewing the controlled substance fills from the weekend, you notice something odd. Dr. Allen, a local family physician, usually writes for maybe five to ten opioid prescriptions a week, mostly for post-surgical pain. This weekend, however, your pharmacy filled thirty prescriptions from him for oxycodone 30mg, all for new patients, all for a #120 count, and all paid for with cash.
Your internal “data alarm” goes off. This is a massive deviation from his established pattern. You are no longer just a pharmacist; you are an investigator. You begin to analyze the data points:
- Pattern Recognition: You see a clear, repeating pattern: same doctor, same drug, same strength, same quantity, same payment method.
- Outlier Identification: You compare this weekend’s activity (30 high-strength opioid scripts) to his baseline (5-10 low-strength scripts). He is a significant outlier compared to his own historical data. You also mentally compare him to other family physicians in the area; none of them write for this volume or strength of oxycodone. He is an outlier compared to his peers.
- Hypothesis Generation: You formulate several hypotheses. Is it possible his prescription pad was stolen? Is he willingly participating in a “pill mill” operation? Is his DEA number being used fraudulently without his knowledge?
- Actionable Intervention: You don’t just keep filling. You take proactive steps. You check the state’s Prescription Drug Monitoring Program (PDMP) and see these same patients are also filling similar prescriptions from other pharmacies. You decide to call Dr. Allen’s office to verify the prescriptions. You may even decide it’s necessary to file a report with the DEA or the state board of pharmacy.
This entire process—detecting a deviation from the norm, comparing it to peers, analyzing the associated data points, and taking action—is the essence of trend and outlier analysis in a PBM. You already possess the core skills of clinical suspicion and pattern recognition. A PBM simply provides you with more powerful tools (like software instead of manual review) and a much larger data set (millions of claims instead of one pharmacy’s fills) to apply those skills on a population-wide scale.
26.2.2 The Data Detective’s Toolkit: Core Analytical Methodologies
To systematically identify trends and outliers, PBM analysts employ several core methodologies. These are not complex statistical concepts but rather intuitive ways of slicing and visualizing data to make patterns emerge. As a pharmacist, your role is to understand the output of these analyses and apply your clinical judgment to interpret the findings.
1. Provider Profiling and Peer Benchmarking
This is the most common and powerful form of outlier analysis. The goal is to understand a single provider’s prescribing habits by comparing them to their peers in the same specialty and geographic area. By establishing a “normal” baseline, extreme deviations become immediately obvious.
Masterclass Table: Key Metrics for Provider Profiling
| Metric | How It’s Calculated | What an Outlier Might Mean | PBM Intervention |
|---|---|---|---|
| Total Cost of Care PMPM (Per Member Per Month) | (Total drug & medical cost for a provider’s patient panel) / (Total member months) | A high-cost outlier may be over-utilizing expensive services or treating a sicker-than-average patient population. | Academic detailing, targeted education, or review of their overall care patterns. |
| Specialty Drug Utilization Rate | (Number of patients on a specialty drug) / (Total number of patients in panel) | A high-utilization outlier might be an early adopter of new therapies or may be using them in broader populations than guidelines recommend. | Review their PA submissions to ensure appropriate use; provide education on step therapy or preferred alternatives. |
| Brand Dispense Rate for a Class | (Number of brand claims) / (Total brand + generic claims for a class) | A provider with a high brand dispense rate for statins may be unaware of generic equivalency or may be influenced by pharmaceutical marketing. | Targeted faxes or letters showing their brand rate vs. peers and highlighting generic alternatives. |
| PA Denial Rate | (Number of denied PA requests) / (Total number of PA requests submitted) | A high denial rate indicates a provider who consistently submits requests that do not meet clinical criteria. They may not understand the requirements. | Proactive outreach from a pharmacist to walk through the criteria for their most commonly denied drugs. |
Visualizing Outliers: The Bell Curve
The most effective way to communicate provider outlier status is with a simple distribution graph, often called a bell curve. By plotting a specific metric (e.g., Cost PMPM) for all cardiologists in a region, it’s easy to see who falls within the normal range and who are the statistical outliers.
Cardiologist Prescribing Cost PMPM – Philadelphia Region
Dr. Highcost
Low Cost
Average Cost
High Cost
In this visualization, most providers cluster around the average. Dr. Highcost, however, is a clear outlier. This graph doesn’t prove wrongdoing, but it provides a compelling reason to start an investigation into his prescribing patterns. The goal of outreach would be to understand the “why” behind his outlier status and, if necessary, educate him on more cost-effective, clinically appropriate alternatives to move him closer to the peer average.
2. Geographic and Regional Analysis (“Hotspotting”)
Sometimes, trends are not tied to a specific provider but to a specific location. Geographic analysis, or “hotspotting,” involves mapping utilization data to identify areas with unusually high rates of prescribing for a particular drug or class. This can provide clues about environmental factors, demographic concentrations, or targeted marketing efforts.
Masterclass Table: Interpreting Geographic Hotspots
| Hotspot Scenario | Potential Underlying Causes | PBM Action / Intervention |
|---|---|---|
| High utilization of a new, expensive brand-name antidepressant in a single, wealthy suburb. |
|
Launch a member and provider education campaign in that zip code, highlighting the efficacy and cost-effectiveness of established generic SSRIs and SNRIs. |
| A cluster of towns showing a spike in prescriptions for an expensive medication for alpha-1 antitrypsin deficiency, a rare genetic disorder. |
|
This is likely appropriate utilization. The PBM’s role is not to stop it, but to ensure patients are managed by a specialty pharmacy network and receive appropriate support services to maximize adherence and outcomes. |
| Extremely high opioid prescribing rates concentrated in a few rural counties. |
|
Deploy a multi-faceted strategy: implement quantity limits, require PA for long-acting opioids, analyze PDMP data for doctor/pharmacy shopping, and provide targeted education to the highest prescribers in the region. |
3. Longitudinal and Therapeutic Class Trend Analysis
This methodology involves looking at data over time to see how utilization and costs are changing. Are new drugs displacing old ones? Are costs for a specific class of drugs, like GLP-1 agonists for diabetes, growing at an unsustainable rate? This type of analysis is crucial for forecasting future drug spend, making formulary decisions, and understanding the impact of market events.
Distinguishing Trends from Noise: The Importance of Normalization
When looking at trend data, it’s crucial to use normalized metrics. Simply looking at raw “Total Spend” can be misleading. For example, if a health plan’s membership grows by 10%, you would expect drug spend to also grow by about 10%. This isn’t a utilization trend; it’s a population change.
Key Normalized Metrics:
- PMPM (Per Member Per Month) Cost: This is the gold standard. It tells you the average cost for each member, regardless of how many members there are. An increasing PMPM is a true trend.
- Utilization Per 1000 Members: This tells you how many people out of a standard population of 1000 are using a drug. It measures the rate of use, not the raw number of users.
The Pharmacist’s Role: When you are presented with trend data, your first question should always be: “Is this data normalized?” Raw numbers can be easily misinterpreted. PMPM is the language of managed care finance.
Case Study: The Rise of the GLP-1 Agonists
An analyst presents the following chart to a PBM’s clinical strategy committee. The data has been normalized to show the PMPM cost for various diabetes drug classes over the past five years.
Diabetes Drug Class PMPM Cost Trend (2020-2025)
Metformin
Sulfonylureas
DPP-4i
SGLT2i
GLP-1 RA
Interpretation and Actionable Insights:
- The Problem: The chart clearly shows that while costs for older drug classes are flat or declining, the PMPM spend on SGLT2 inhibitors and especially GLP-1 Receptor Agonists is skyrocketing and has become the primary driver of cost in the entire diabetes category.
- The Clinical Question: A data analyst can identify this trend, but they need a pharmacist to answer the “why.” Is this trend appropriate? Your clinical knowledge is crucial here. You would explain that recent guidelines (ADA, etc.) have elevated these classes due to proven cardiovascular and renal benefits, so an increase in utilization is clinically expected and appropriate for many patients.
- The Management Question: The question then shifts from “How do we stop this?” to “How do we manage this?” The data has identified the problem. The clinical context has qualified it. Now, strategy begins.
- Could we implement step therapy, requiring a trial of a lower-cost SGLT2i before a GLP-1 for patients without ASCVD?
- Could we negotiate a better rebate on a preferred GLP-1 to make it exclusive on the formulary?
- Are members using these drugs appropriately for weight loss instead of diabetes, and should our PA criteria be tightened to ensure use is for the FDA-approved indication on the member’s plan?
