A data analytics firm is transforming how pharmaceutical manufacturers understand the patient experience, aiming to remove obstacles that prevent people with complex conditions from receiving sophisticated therapies. By integrating and analyzing disparate healthcare data, Claritas Rx provides actionable insights that help companies ensure prescribed treatments successfully reach the patients who need them most. The company’s work addresses a critical challenge in modern medicine: as drugs for specialty and rare diseases become more advanced, the journey from prescription to administration becomes increasingly fraught with potential delays and disruptions.
Since its founding in 2011, the company has shifted from a consultancy focused on channel data to a technology leader in patient-level data analytics. It now serves a wide range of clients, from emerging biotech firms launching their first specialty product to established global manufacturers. At the core of its mission is the goal of supporting patients with chronic and life-threatening diseases by ensuring they receive the full benefit of their prescribed treatments. This is achieved by creating a detailed, longitudinal view of the patient journey, which reveals points of friction that can be addressed by pharmacy and field support teams. A key partnership with the global pharmaceutical company Servier, which began in 2020 with two oncology drugs, has now expanded to encompass Servier’s entire cancer treatment portfolio, demonstrating the value of this data-driven approach.
Aggregating Complex Healthcare Data
The primary capability of Claritas Rx lies in its ability to bring together diverse and often siloed data sources to create a coherent, unified view of patient activity. In the specialty pharmaceutical space, data may come from specialty pharmacies, hubs, third-party logistics providers, and various other partners involved in the distribution and administration of a drug. Each source provides a different piece of the puzzle, and the company’s platform is designed to assemble these pieces into a complete picture. This process of data aggregation is foundational to generating meaningful analytics.
Athena Uzzo, Senior Vice President of Customer Experience at Claritas Rx, describes the company’s strength as connecting these data streams to provide intelligence that directly impacts a patient’s ability to access therapy. The platform is built for flexibility, serving clients with a single product on the market as well as those with large, multi-brand portfolios that require both brand-specific and disease-specific insights. By handling the complex process of data integration, the company allows pharmaceutical teams to focus on interpreting the results and taking action rather than managing raw data feeds.
Mapping the Longitudinal Patient Journey
A central element of the company’s analytical method is the focus on longitudinal data. This involves tracking individual, anonymized patients over extended periods to understand their entire therapeutic journey. This approach contrasts with static, cross-sectional analyses, which only offer a snapshot in time. By following a patient’s path from the initial prescription through refills and ongoing treatment, the platform can identify recurring patterns, bottlenecks, and critical drop-off points. This enriched, long-term perspective is what allows for a deeper understanding of real-world patient experiences.
This longitudinal view helps answer critical business questions for manufacturers. For instance, it can reveal how long it takes for a patient to get started on a therapy after it is prescribed, identify the specific barriers that cause delays, and pinpoint where patients are most likely to abandon their treatment. According to Uzzo, the company’s specialty is delivering this enriched patient-level data, which provides a granular, evidence-based foundation for improving support systems and intervention strategies. “Our company started with the mission to ensure that patients with chronic and life-threatening diseases receive the support that enables the greatest benefit from their therapy,” Uzzo states.
A Collaborative Partnership in Oncology
The collaboration with Servier provides a concrete example of how these data insights are applied. What began in 2020 as a focused project on two specific oncology drugs has since matured into a comprehensive partnership covering the company’s entire cancer portfolio. Claritas Rx manages the data aggregation from all of Servier’s partners across these brands and delivers the resulting analytics. Uzzo characterizes the relationship as a “collaborative partnership” centered on answering Servier’s specific business questions, rather than providing a one-size-fits-all data product.
This expansion reflects a growing recognition within the pharmaceutical industry of the need for sophisticated data analytics to navigate the complexities of specialty drug commercialization, particularly in a field like oncology. The insights generated help Servier’s teams understand product performance, patient access patterns, and the effectiveness of their support programs, enabling them to refine their strategies and better serve cancer patients.
Shifting Toward Predictive and Prescriptive Analytics
The company is now advancing its capabilities beyond descriptive and diagnostic analytics toward predictive and even prescriptive modeling. The collaboration with Servier is entering a new phase focused on these more sophisticated approaches. The goal is to move from explaining what has already happened to anticipating future challenges before they occur. “We’re expanding our analytics and leveraging predictive modelling to give teams insights into where patients will struggle with therapy access before it happens,” Uzzo says.
Anticipating Patient Needs
Predictive modeling uses historical data to forecast the likelihood of future events, such as a patient missing a refill or facing a coverage denial. By identifying at-risk patients early, support teams can intervene proactively. This represents a significant shift from a reactive to a proactive model of patient support, aligning with broader trends in healthcare toward personalized medicine. As therapies become more tailored to individuals, the systems for ensuring access to those therapies must also become more personalized.
Tailoring Specific Interventions
Beyond prediction, the company aims to develop prescriptive modeling. This next step would not only identify a patient at risk but also recommend the specific type of intervention most likely to be effective for that individual’s circumstances. “The access to therapy is also personalised,” Uzzo notes. The objective is to provide manufacturers and pharmacies with precise, patient-specific insights, moving beyond standardized support protocols to deliver truly individualized assistance. This evolution promises a future where data analytics can help create a more seamless and supportive healthcare experience for every patient.