How Real-World Data Can Help Maximize Patient Access

By Carolyn Zele, Advisor, Solution Consulting, MMIT, and Lance Wolkenbrod, Senior Director, Commercial Solutions, Panalgo

When faced with launching its new product, one midsized pharma manufacturer’s marketing team needed an innovative way to quickly identify and geographically target the right cohort of patients for their highly specialized therapy. By leveraging the power of real-world data and advanced analytics, they achieved a match rate between key targets, payers, providers and claims that were 40% higher than they had ever achieved and eventually noted a 20% increase in uptake versus previous launches that might never have been possible if they had used their traditional methods.

As this example shows, understanding market dynamics and the patient journey is essential for the successful launch and management of brand performance. Analyzing patient-level real-world data—specifically claims, provider and payer data—is not only valuable for patient uptake of new drugs but also to investigate the patient journey, market share and other treatment patterns including treatment sequences, persistence, adherence and switches.

As pharma companies shift from developing drugs that serve large populations to specialty drugs, targeting the right patients becomes a key driver of success. Fifty percent of pharma organizations that launched products missed first-year forecasts due to issues with market access. Sales rep access to physicians has decreased by 57%, and two out of three patients with complex conditions are unable to navigate payer controls such as prior authorization, step edits, pharmacy network restrictions and National Drug Code (NDC) restrictions.

Clearly, a more robust data and analytics strategy is critical to address today’s most pressing commercial challenges. To succeed at launch, companies need to harness claims and patient-level data to more accurately locate the correct cohort of patients. However, while most pharma organizations recognize the importance of leveraging these types of data, they are at a loss for what to do once the data sets have been purchased. Often, the right data exists but is siloed among different departments. Many organizations simply lack the right people, capabilities and processes to consolidate and cleanse their data to put it to good use. Even though large pharma companies are spending as much as $400 million on people, platforms, data and analytics, many do not see a meaningful return on their investment.

Why are companies purchasing data but failing to make the most of it? Here are five key reasons:

  1. Lack of data integration: Companies have been using analytics for many years but bringing together multiple data sources to inform decision-making can be a laborious process. In many pharma companies, analytics groups spend nearly 80% of their time just bridging the data together, validating and coding to develop the appropriate model, sometimes leaving only 20% of their time for the actual analytics. Developing a way to minimize this front-end burden can help you better leverage your data.
  2. Inadequate analysis: Most analytics models are set up to answer specific questions. The problem is that every question leads to another question, and the questions you ask at the beginning aren’t the same as the ones you need to ask as you go through the analysis. Companies need to be able to answer questions as they arise without having to go back to square one, coding and modeling for the new analysis.
  3. Lack of experienced analysts: One of the most significant concerns reported by respondents to a recent survey of healthcare analytics experts remains the scarcity of experienced analysts who not only have experience with large data analytics and statistical modeling but also have a working knowledge of market access and the clinical drug development process. Half of all respondents stated that a lack of experienced analysts is a significant barrier to investment. Finding ways to simplify analysis of complex data sets for the less experienced analysts is key to getting more from your data.
  4.  Incomplete reporting: Most pharma companies provide the results of their analyses to senior management to enable them to make key decisions. However, standard analyses often only touch on a portion of the critical information they need. Advanced analytics using the most relevant databases can increase the thoroughness of your reporting by quickly providing insight to additional questions that come up during the analysis. These insights can then be used to inform brand management, payer negotiations, field team optimization, promotional marketing and prescriber education. Imagine the decisions that you could make if you were able to include 40% more real-world data than you can access today in the analysis. Accessing a larger patient population across more of the payer landscape to gain an understanding of your unique market—all so you can target your resources appropriately—feels like a dream come true for most commercial teams.
  5. Extended lag time to insights: After purchasing claims and coverage data, it might take six months to normalize and map the data and another six months to make sense of it, assuming the analyst has experience in patient, clinical and market access research. This year-long (or more) lag time diminishes the value of the insights because it simply takes too long. Organizations need to be fast and nimble to ensure a successful launch and ultimately reach the most patients that can benefit from new therapies.

Healthcare today is more specialized, competitive and cost-sensitive than ever before. Effectively leveraging data and analytics with speed and accuracy to increase patient access is critical to an organization’s successful launch and long-term growth of your product portfolios.

For more on how MMIT and Panalgo can help you leverage real-world data for commercial strategies, click here to learn about the new Patient Access Analytics solution.

The editorial staff had no role in this post's creation.