What Pharma Needs From RWD: Top 5 Trends
The explosion of real-world data (RWD) now available to pharma companies can be overwhelming. While many larger manufacturers with their own teams of data analysts have been using RWD for years, smaller manufacturers are just beginning to incorporate RWD into their clinical development and market access strategies. Which data sets do they need, and how can they use them most effectively?
As the majority of pharma companies don’t have the resources to invest millions in a data lake, they’re looking for affordable ways to harness the power of RWD across the board, from devising study protocols to negotiating with payers to understanding the patient journey and improving prescribing patterns.
In our conversations with manufacturers, we’re hearing several reoccurring themes. These top five trends are guiding pharma’s decisions about RWD today:
Trend 1: Strategic partnerships, not multiple vendors
Pharma companies are inundated with vendors offering unique RWD data sets. The first challenge is determining the advantages of one data set versus another. If one vendor’s medical claims data covers 93% of the market, and another vendor’s claims data covers only 82%—but that data is also linked to lab data and prescription data—which is the most useful?
Manufacturers are answering this question differently than they did a few years ago, as they’ve learned from experience how costly data fragmentation can be. When manufacturers purchase an array of discrete data sets from multiple vendors, they’re left with discordant data, which requires a tremendous amount of money and time to integrate. As a result, they’re spending valuable time on continual data normalization, integration and analysis.
Trend 2: Transparent, integrated RWD
Data integration is an absolute necessity for manufacturers. In order to have a 360-degree view of the patient’s journey, data must be linked through common denominators. Even with AI capabilities, linking disparate data sets is a long and tedious process.
Pharma companies are looking for pre-integrated data sets that have already been reconciled with each other, so RWD is immediately useful. For example, MMIT’s Bridging as a Service solution maps multiple source entities to our data backbone, providing a single source of truth that can maintain accuracy as vendor landscapes change.
Transparency surrounding data set limitations and characteristics is particularly important in this arena, but not every vendor provides full transparency. Some RWD vendors offer robust data, but they control it via a closed platform. This means manufacturers cannot integrate this data with any additional sources to build their own longitudinal data sets.
A lack of transparency also creates downstream problems for manufacturers trying to get to the root of a business problem. Closed-platform RWD vendors may provide impressive visualizations of their data, but without transparency, manufacturers cannot operate outside of the pre-built algorithm—they can’t look under the hood, so to speak, to investigate another root cause contributing to an observed effect.
Trend 3: Desire for more oncology RWD
The demand for oncology data has intensified as the number of oncology clinical trials continues to rise. In the next few years, personalized medicine biomarkers and genomics will become instrumental in every therapeutic area, driving the need for additional RWD.
RWD is particularly well-suited to supporting pharma companies’ needs in oncology. On the development side, RWD is critical for informing clinical trial design and site selection, not to mention the enrollment of qualified patients. On the commercial side, manufacturers can use RWD for immediate physician targeting. For example, as soon as a lab test indicates that a patient is EGFR-positive for non-small-cell lung cancer, manufacturers can ensure the patient’s physician knows their drug has the best safety profile to treat it.
Trend 4: Advanced analytics and curated insights
No matter how comprehensive your data is, it’s meaningless unless you can draw insights from it on demand. Five years ago, pharma companies were satisfied building one-off trend analyses in Excel. Today, they’re looking for advanced data visualizations that will answer pressing business questions across the development life cycle. Which countries are oversaturated, and where are the best sites for a trial? What’s our coverage analysis in every state for this product over time?
To use RWD analysis to solve a particular business problem, intention and discrimination are key. Some RWD vendors offer an immense data catalog of dozens upon dozens of data sets. While this data is normalized and de-identified, it is not remotely curated; manufacturers must wade through massive amounts of data to find what they might need to devise their own queries. Few manufacturers have the resources to do this effectively, over and over again.
Many RWD vendors are willing to send data, but they will not curate it, create outcome scenarios, or provide an opinion regarding the next best step. Manufacturers today are in the market for expertise along with their RWD. They want not only analytics and algorithms, but also insights and recommendations. Once the integrated RWD has been parsed and analyzed, what next? What will the results of this query prove, and how do we decide what is the best course of action in light of this new information?
Trend 5: Predictive analysis across the development life cycle
RWD is not a static term. In addition to the more common healthcare data sets, RWD can encompass everything from wearables data to brand awareness data to payer survey data. Manufacturers are looking for strategic partners that are committed to providing the right data sets to shed new light on their patient populations.
For example, in rare diseases, social media plays a significant role in building patient communities. Patients exchange messages about new therapies, tests and regimens they’ve tried. This data is incredibly valuable to rare disease drug manufacturers, as it helps them to better understand the typical patient’s journey from specialist to specialist.
There is also a strong appetite for predictive analysis across the development life cycle. By combining clinical trial data with lab data, claims data, payer coverage data and clinical pathways data, manufacturers can look across both sides of their house at the same time. For example, knowing how a competitor’s study inclusion/exclusion criteria will impact its approval timeline down the road can change a manufacturer’s launch strategy.
With integrated RWD illuminating the full continuum of the drug life cycle—from determining unmet need through to post-market differentiation from generic competitors—a manufacturer can effectively anticipate the impact of decisions they’re making today, and the results they see tomorrow.
Learn how MMIT’s integrated real-world data and expertise can help you solve your access challenges. To learn how MMIT can apply customized business rules and logic for relevant bridging scenarios to your data, learn about Bridging as a Service.
