Over the past few decades, pharma companies have perfected the art of using market access data to support product launches, negotiate payer discounts, and secure preferred status and positioning. Manufacturers typically track formulary and medical policy guidelines and restrictions—including prior authorization and step therapy requirements—for a product and its competitors, using this data to inform their market access strategy.
For the most part, this approach works. But in competitive therapeutic areas, like immunology and oncology, optimizing market share depends on creating a more structured, holistic market access story. By augmenting traditional datasets with real-world data, manufacturers can follow patients’ progression along the care continuum—and uncover new opportunities to improve access.
Understanding how access functions in reality
In recent years, the traditional concept of ‘market access data’ has grown to encompass real-world sources, most notably claims and lab data. The closer data gets to the individual patient, the more readily manufacturers can answer the fundamental question: can patients easily fill their prescription or receive their infusion? If not, what factors stand in their way?
There are often notable differences between what a payer, PBM, or provider intends to happen and what actually happens. Although a payer may agree to grant a drug preferred status, patients might still be prescribed competitor drugs without stepping through the preferred drug. From provider-requested exceptions to conflicting manufacturer contracts, there are many reasons why access works unexpectedly. For example, an IDN-affiliated provider might choose to abide by their IDN’s recommendation rather than a payer’s preferences.
Without claims data, manufacturers cannot evaluate the complete market access picture. In recent MMIT research for a competitive oncology indication, we found that a top PBM frequently provided reimbursement for a competitor drug rather than the drug listed as preferred on their formulary. Claims data revealed the disparity between intention and reality, which had a significant impact on the preferred drug’s market share.
Uncovering hidden influences
By analyzing claims and coverage data in parallel, manufacturers can arrive at a deeper understanding of the patient journey—and determine how to intervene. For example, when a patient is moving from a first-line to a second-line therapy, what’s influencing this shift? Is the change patient-driven, a result of side effects? Or is it driven by physicians or payers? In an MMIT analysis of oncology categories, we found that a considerable percentage of therapy changes are influenced by payers.
The divergence between claims data and clinical pathways can also be illuminating. An oncology product’s inclusion on clinical pathways has a much larger influence on market share than payer coverage, yet not all manufacturers track pathway positioning or pull-through. By examining the intersection between coverage, pathways, and claims data, manufacturers can better understand how access is functioning across geographical regions. In some cases, manufacturers could gain as much as 5% market share by encouraging tighter pathway alignment.
Claims data can also identify variance between formulary-mandated copays and the patient’s actual copay. For indications like diabetes, for example, claims data might reveal that the average patient copay is lower or higher than the copay mandated by a product’s formulary tier, indicating an incorrect placement.
Tracking disease progression over time
Integrating lab data into the market access data mix introduces another dimension of the patient journey. Many manufacturers are beginning to use normalized lab data early in the development lifecycle to optimize site selection for clinical trials and improve recruitment and enrollment.
From a patient access perspective, lab data can help manufacturers find relevant patients and monitor their disease progression. Even before a diagnosis, manufacturers can identify patients that have tested positive for a particular lab test or combination of tests, including genetic, genomic and biomarker tests. Given the cost of specialty therapies, locating even a handful of potential patients via lab data can provide a healthy ROI.
Linking lab data to claims data can also give manufacturers greater insight into patients’ care journey, from their testing history to prescribed therapies. Longitudinal lab data can reveal if a patient’s condition is deteriorating over time, which can help manufacturers identify prime candidates for their therapy.
Integrating real-world lab and claims data with coverage and clinical pathways data reveals a new world of opportunity for manufacturers. With full visibility into how patients receive care, manufacturers can maximize the ratio of diagnosed patients to treated patients—and optimize access to their therapies.