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With an updated understanding of what type of patients use a brand under what clinical conditions, marketers can find ways to invigorate even a mature brand
You have a plan for your brand. You've even updated it every year since launch. But here's the question: Have you updated your view of your patients since launch? In too many cases, brand managers never take that crucial step. They may revise their strategy and tactics, but they're doing it based on a view of the patient that was established late in clinical trials.
When that happens, crucial questions remain unanswered: Do you know how various patient segments are responding to your product's current positioning? This is an increasingly vital insight given the rise of consumerism and the role that patients play in treatment decisions.
What about how the treatment tracks with patients' disease progression? If you don't know, you may be overlooking unmet needs that can be addressed with additional clinical investment.
And, can you tell if your product is winning new business only to lose it through product switching or poor patient persistence?
Attention to detail like that can make the difference between holding one's own in the market and breaking away from the pack. With an updated understanding of what type of patients use a brand under what clinical conditions, you can discover opportunities that can invigorate even a mature brand.
In the past, the only way to segment patient populations according to their clinical characteristics was through primary research. Today, we have information sources containing anonymized patient-level data (APLD) to help define the needs of various patient segments and identify patterns in how products are used. Anonymized patient-level metrics are not new, but they've largely been overlooked or underutilized. With APLD, companies can rely on actual behavioral data to profile the types and groups of patients who receive specific treatments.
APLD comes in several forms. Health plans' claims data can be used to define the unmet treatment needs of patient segments and provide an evidence-based view of the treatment context for millions of de-identified patients, tracked over time. Data include details on patients' pharmaceutical therapy, diagnoses, testing, medical procedures, and hospitalizations, leading to a complete picture of patients' healthcare experiences.
Another source of APLD, longitudinal prescription data, tracks dispensed prescriptions for millions of de-identified patients over time and ties that information to prescriber-level details—providing deep insight into how drugs are used in real-world settings.
Insight into how therapies are used—and not used—in the real world can help focus strategic thinking and enable more deliberate marketing decisions over the life of a pharmaceutical molecule.
Quite often, the process unearths opportunities that may not otherwise have surfaced. For instance, a company might learn that a meaningful subsegment of the patient population with a particular comorbidity profile is much more apt to be prescribed a competing product. The company could then assess the value of conducting further clinical work to prove the product's degree of efficacy within this subpopulation.
Let's look at an example: A segmentation analysis of patients diagnosed with type II diabetes showed four patient types having distinctly different clinical profiles. One segment was made up of older patients whose condition was well managed. These patients were frequently treated with combination therapies, and also tended to be on cardioprotective therapies.
This information suggested that to increase utilization within this valuable cohort, the brand team should:
Patient flow models (also often called patient funnels or treatment cascades) are a useful tool for revealing where patients enter the healthcare system and studying their treatment progress. In quantifying the number of patients in each part of the pathway, and in displaying patterns in real-world clinical practices, patient flow models clarify where marketers should be redirecting their efforts.
For example, if an unusually large number of patients are diagnosed but remain untreated for a given condition, a company might want to step up direct-to-consumer education, stressing the importance of therapy in managing the disease. Or, if it appears that diagnostic hurdles are hampering the administration of treatment, a company might support diagnostic innovations.
Patient flow models can also highlight how often physicians deviate from treatment guidelines, how often they switch patients from a given product, what they are switching them to, at what point in the treatment progression patient persistence drops off, and so forth. In each instance, such insight would suggest to marketers ways that they might improve treatment rates.
The conceptual patient-flow model combines APLD from health plans' claims databases with data drawn from longitudinal prescription databases. If this model were populated with actual patient counts for a chronic condition, it might reveal that a brand was generally being used as second line therapy—an unwelcome finding if the product were approved for first line therapy. Knowing this, the brand team could investigate the cause and then build corrective actions into the marketing plan to create a better match between the product's indication and its actual use. Furthermore, the model would provide significant insight into the type, timing, and locale of dynamic treatment decisions that were being made, giving the brand team the ability to create tactical plans to address areas of concern.
When brand managers invest the time to better understand the clinical profiles of patients and where they are in the treatment pathway—leveraging insights now available with APLD—they are often rewarded with a fresh set of opportunities for their brands. Innovative marketing tactics developed to capitalize on these discoveries help fuel brand growth, "maximizing the molecule" over its lifecycle.
Jim Carroll is director, Launch and Brand Management, and can be reached at firstname.lastname@example.org. Matt Guagenty is senior principal, IMS Consulting, and can be reached at email@example.com
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