Thought Leadership

Pharma companies are increasingly turning to real-world data to answer their commercial business questions, but not all realize that unstructured EMR data is the unsung hero of most queries. Whether a manufacturer is struggling to find a niche patient population, conduct unbiased outcomes research, or generate persuasive proof points, unstructured data can fill in the gaps left by other real-world data sources.

In a recent post on specialty carve-out mechanisms, we examined how payers’ use of specialty benefit managers and alternative funding programs can impact manufacturers’ patient assistance programs (PAPs). Today’s post takes a look at another managed care trend: the rise of copay accumulators and maximizers, also known as copay adjustment programs.

In 2025, copay accumulator and maximizer programs have become one of payers’ primary strategies for mitigating the use of PAPs. While these controversial programs help payers fully leverage the funds that manufacturers deploy to increase patient access, they can often result in exorbitant surprise costs for patients, which can lead to treatment abandonment.

Obstructive sleep apnea (OSA) is a prevalent yet underdiagnosed condition affecting millions worldwide. People with OSA are subject to sudden drops in blood oxygenation levels that can lead to severe cardiovascular, metabolic, and cognitive consequences if the condition remains untreated. Traditionally, OSA has been managed through personal appliances that apply continuous positive airway pressure  (CPAP). Although CPAP machines are considered the gold standard of care, patient adherence remains a major challenge due to their relative discomfort and inconvenience.

As therapies have become more complex, pharma companies are now challenged to achieve precision targeting within a much tighter timeframe. While claims data is readily available, one of its key limitations is the lack of timeliness.

Many manufacturers now rely on specialized lab data—from imaging results to genetic testing and genomics—to identify eligible patients and their providers. As lab data is often the key driver in diagnostic decisions, this is an excellent source for commercial targeting initiatives. But what about understanding the intent behind the testing?

Approximately 6.9 million Americans aged 65 and older have been diagnosed with Alzheimer’s disease (AD). By 2060, prevalence could reach 13.8 million if no therapies are approved to prevent or cure AD.

For decades, Alzheimer’s treatment focused on symptom management, with stagnant progress in experimental breakthroughs. That changed recently with the development of beta-amyloid targeting therapies. Although these disease-modifying drugs are not a cure for AD, they do remove beta-amyloid buildup in the brain, a promising yet still debated approach to slowing cognitive decline.

Months and months of market access planning occur long before a new therapy is approved. Once that therapy is finally on the market, pharma companies must then turn their attention to the art of effective brand promotion.

While the first step is identifying the right prescribing physicians, how manufacturers engage with these HCPs is equally important. In today’s market, pharma companies need to adopt a coordinated, omni-channel approach to ensure prescribers fully understand their brand’s value.

With the right tools and data sources in place, your brand team can design and execute a promotional strategy that matures alongside your brand.

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.

Payers tend to manage high-cost, high-complexity disease states quite differently than other indications. In recent years, payers’ growing reliance on managed care carveouts, in the form of specialty benefit managers (SBMs) and alternative funding programs (AFPs), has directly impacted pharma companies that offer patient assistance. This year, the downstream effects of the Inflation Reduction Act (IRA) and changes to Medicare Part D benefit will also play a role in patient access.

For pharma companies that offer a patient assistance program (PAP), keeping an eye on managed care trends is essential, as utilization shifts can directly impact the availability of funds. PAPs are typically focused on providing assistance for both uninsured and under-insured patients, or patients whose health plans do not cover a specific drug.

As of 2024, more than 54 million Medicare beneficiaries had prescription drug coverage through Medicare Part D. Of that total, 31 million had Part D coverage alongside a Medicare Advantage plan, and about 23 million had a Part D prescription drug plan.

As a result of the Inflation Reduction Act (IRA), significant changes to the Part D benefit will take effect in 2025. Changes include a reduced out-of-pocket maximum, elimination of the coverage gap, continued drug price negotiations for selected therapies, and a new Medicare Prescription Payment Plan. While these changes will offer millions of Medicare beneficiaries increased access and coverage protection, they may also cause payers to increase utilization management restrictions within their commercial plans due to losses incurred by the IRA.

The progressive integration of AI within medicine is transforming the way physicians evaluate and monitor disease, enabling earlier detection and improving outcomes and quality of care. Medical practices are increasingly leveraging datasets, algorithms, and other machine learning techniques to identify subtle patterns that might not be discernable to physicians without this technology.

These AI tools are especially useful in detecting conditions like early-stage cancer, cardiovascular diseases, and neurological disorders. For example, AI can analyze imaging data, such as CT and MRI scans and mammograms, to flag abnormalities with greater accuracy and efficiency than current methods, reducing the risk of incorrect diagnoses.