Mapping the Patient Journey: Six Commercial Use Cases for RWD
For years, pharma commercialization strategies focused only on traditional market access data: payer policies and restrictions. While payer coverage is still essential information, this single-source approach cannot help manufacturers identify the many barriers to access. In the path to treatment, there are so many hurdles that can stand in the way of a patient receiving the right therapy, from referral delays to misdiagnosis, incomplete testing to payer behavior—the list is exhaustive.
To uncover and resolve these barriers, manufacturers need real-world data to illuminate the full patient journey. How many treatments must a patient try before beginning an effective regimen? How do diagnostic and testing delays impact the start of therapy? What role does out-of-pocket cost play in a patient’s treatment?
To help our clients answer these questions, MMIT queries our integrated claims, coverage, lab and EMR data, powered by NorstellaLinQ, to generate actionable intelligence about specific patient populations. Here are the top six patient-focused areas of inquiry for our clients:
1. What is the typical patient-level care journey?
Mapping the time that elapses between initial diagnosis, biomarker testing and treatment helps pharma companies understand the typical sequence of events and identify existing roadblocks. How long does it take for patients to be diagnosed? What percentage of patients are tested after diagnosis, and how much time passes between testing and beginning therapy?
By viewing the distribution of genetic testing, manufacturers can learn which genetic markers are most commonly tested for, and how many potential patients are receiving the correct tests. For therapies that require a specific biomarker result, your team can also use this data to project how many target patients will soon be eligible for your brand. Manufacturers of second-line therapies can track the duration of patients’ treatment on a first-line therapy, and the testing that occurs before a second-line therapy is prescribed.
2. What’s the typical treatment sequence for patients?
Charting the most common treatment paths for a patient population is helpful for a number of use cases, from pre-market research to understanding why a brand’s volume has decreased. What percentage of patients are taking a particular brand, and what treatments do patients typically take before and after that brand?
How common is drug switching in this space? Historical data can reveal how these dynamics have changed over time, as more therapy options were released. Pharma companies worried about loss of volume can also use this data to determine which drugs their patients are switching to, and at what point in their treatment regimen. Claims and unstructured EMR data can uncover factors contributing to prescription switching—are patients put off by cost, repeated rejections, or limited outcomes?
3. What are the most common regimens at each line of therapy?
Understanding current (and historical) treatment utilization by line of therapy is essential for manufacturers’ competitive analysis. Which product is the most common first-line therapy, and how did its volume change after the introduction of another brand? Of the patients who progress to a second-line therapy, which product is most commonly used? What is the impact of a competitor’s launch? This data can also help clarify new market opportunities for your brand. What percentage of patients are taking your brand as a third-line therapy instead of a second-line therapy, and what are they taking instead?
4. What is the average co-pay by coverage status and treatment?
Understanding the patient’s cost burden is essential, yet many manufacturers do not know what their patients actually pay for treatment. Integrated claims and coverage data reveals how a brand’s coverage status—from preferred to nonpreferred, with or without restrictions, etc.—impacts the median co-pay.
Is a competitor’s therapy more expensive than yours? Perhaps additional patient support is needed, in the form of patient support hubs or revised eligibility criteria for your patient assistance program. Are patients with commercial plans paying far less than Medicare beneficiaries for your brand? Viewing a breakdown of payer and patient costs by payer and channel can uncover beneficial contracting opportunities.
5. How does the patients’ out-of-pocket cost impact market share?
How does patient cost-sharing vary by factors like product, therapeutic area, and patient type? What are the current and historical patient out-of-pocket (OOP) costs for each product in an indication? How have market events—like the introduction of a new product, or a price increase for a competitor product—impacted both patient cost and market share? With claims data, pharma companies can study the connections between OOP cost, claim rejections, and prescription reversals. For example, a manufacturer could discover that a higher-than-average co-pay led patients to fill only one prescription before switching to a competitor.
6. How do patient co-pays impact volume and behavior?
By studying the range of co-pay amounts across patient types—such as new-to-therapy patients, brand-switching patients, or patients who abandoned therapy—manufacturers can better understand the pricing sensitivities that impact a therapeutic area. How does the OOP cost affect patient volume and number of refills by drug? What was the average copay and OOP cost of abandoned claims, per brand? For example, a manufacturer might find that the average patient costs for abandoned Brand A claims are ten times the average co-pay for its paid claims, leading Brand A to have a much higher abandonment rate than brand B.
Defining, segmenting, and tracking your patient population through the typical twists and turns of the patient experience is essential for developing an effective commercial strategy. At MMIT, our RWD Insights team can query our integrated data assets—including open and closed claims, structured and unstructured EMR data, lab results and commercial intelligence—to help you determine how to eliminate barriers to your therapy.
To learn more about how our integrated real-world data can help your team smooth patient access, check out this infographic or visit our RWD Insights site.
