What internal changes are most necessary for life sciences organizations to make in 2022?

Continued restrictions on pharmaceutical representatives’ in-person interactions with HCPs, coupled with clinicians’ growing comfort accessing medical information online, are causing many life sciences organizations to reorganize both their sales and medical outreach teams. This is raising new questions about optimal ways to leverage MSLs as part of a broader evidence-generation and evidence-engagement strategy.

Even as the most recent Omicron wave of the COVID-19 pandemic subsides, continued restrictions on in-person HCP interactions, local surges, and clinician burnout are all causing life sciences organizations to question the future of the traditional sales representative and consider the expertise needed to interact with clinical customers.

On one hand, the pandemic illuminated the need for more medical/scientific expertise, along with reps who can field complex questions about products, evidence, and value. As a result, some companies are looking to expand the purview of the MSL beyond traditional off-label and science-centered conversations. They’re looking to leverage RWD and HEOR research in new ways, such as helping HCPs understand their patient populations more discretely or engaging new kinds of customers (e.g., employers). Other companies, like Pfizer and Amgen, are cutting back on the number of reps. These companies are reallocating some of those funds toward more digital-first content creation in recognition of the greater presence of HCPs searching for and discussing evidence online.

On the other hand, gaining the attention of HCPs is more difficult than ever. Providers are still restricting most reps from in-person interactions. Individual HCPs continue to suffer from burnout and trauma and are turning down virtual visits. More HCPs are utilizing online channels to access and discuss clinical evidence and products, which is calling the traditional MSL role into question and causing life sciences leaders to rethink what value such a clinically trained field force can provide to their customers. In fact, a recent survey published by Reuters for Within3 found that 40% to 49% of medical affairs and life science engagements are expected to be virtual in the next three years. And this doesn’t just impact current customer engagements. It’s also becoming increasingly difficult for life sciences leaders to identify the right decision-makers to target in the future, and to understand who has influence in the digital world.

Implications for life sciences

The role of the rep has been at an inflection point for years, but the future will be determined by whether life sciences leaders can take advantage of emerging opportunities to leverage real-world data more effectively answer HCPs’ and customers’ open questions. In fact, a new report from Accenture found that 65% of oncologists want pharma reps to be able to discuss real-world data with them, and 51% “will need more discussion” on real-world data from reps in the future.

Moving forward, field teams may look for opportunities to generate localized, RWD-derived insights about individual customers’ patient populations, disparities, and care gaps. Field teams can use personalized analysis to take their interactions to the next level and help customers identify eligible patients in their population, understand appropriate use cases, and support the creation of clinical guidelines and standards. As real-world data and technology companies continue to invest in and expand access to new and linked sources of RWD, many other potential use cases may arise that could support MSL-to-HCP engagement.

Questions to consider

• How can your field teams support customers by providing provide data-driven, tailored analysis?
• Are your reps fully trained in the needed data and communication skills to be able to explain RWE to customers and to field complex questions?
• How are your field teams adding value to your customers, who are increasingly burned out and have limited time to meet with reps?

Rapidly expanding sources and accepted uses of real-world data will force most life sciences companies to invest in the necessary governance, infrastructure, and talent required to support collaborative, cross-functional RWD initiatives across portfolios and product lifecycles.

Together, the ten previously discussed trends are fueling unprecedented investments in real-world data and analytics. Many life sciences companies appear to be locked in a virtual “arms race” for data. They are accumulating new data assets through a combination of licenses, partnerships, and proprietary tools in the hope of generating insights that can fuel innovation and growth. Not surprisingly, the number of data and analytics vendors has expanded rapidly as well, leaving many life sciences companies with a massive swath of disparate and disconnected data sets. Complicating matters further, that data is often housed in multiple, separate silos and owned by a variety of different functional or therapeutically focused teams.

Real-world data and evidence-generation are no longer the exclusive purview of HEOR or medical affairs leaders. Market access teams (and a burgeoning, integrated function known as medical access) are expanding their interest in a variety of real-world data sources to support more value-centered conversations with payers, PBMs, and employers. And as the FDA looks to establish clearer guidelines about the use of real-world evidence for regulatory purposes, the use cases for RWD in R&D are likely to expand as well. To avoid redundant data purchases or underutilized data assets, life sciences companies will need to develop enterprise-wide models for evaluating vendors, purchasing data, scoping projects, managing cross-functional RWE initiatives, and sharing critical research insights across the firm.

Implications for life sciences

Just as the past few years showcased a growing cross-industry acceptance of real-world data, the coming years will be marked by an increasingly urgent need for companies to integrate and optimize the use of all these data sets. Doing so will require visionary leaders who can design the structures, processes, and staffing models that can support the RWE use cases of today—and tomorrow. It will require the commitment of additional capital (above and beyond the investments in data) to ensure teams have the right talent, analytical tools, and data sharing/storage infrastructure to generate the insights that can fuel decisions, actions, and impactful changes in care. And it will require the careful selection of data and consulting partners to ensure that life sciences companies not only work with high-quality, representative, fit-for-purpose data, but also ask the questions likely to yield the most salient, actionable results.

While the following list of infrastructure elements and required expertise may seem obvious, it differentiates the more sophisticated organizational consumers of real-world data from those still trying to figure it out:

• Data strategy that is clearly linked to enterprise strategy
• Centralized data infrastructure and governance
• Enterprise-wide data access
• Easily accessible educational resources that define contents and limitations of available data sets
• Early cross-functional input into RWE project design and scope
• Broad and clear firm-wide visibility into past and current projects
• Appropriate analytical talent (either in-house or through consulting partners) to analyze the data

Questions to consider

• Can you (and your colleagues) clearly articulate your firm’s real-world data strategy?
• Are you doing enough to break down the technical and organizational silos that can hinder optimal use of real-world data assets?
• Does your firm have a centralized function or team fully focused on real-world data (e.g., RWE Center of Excellence)? If not, should you?
• Do you have processes in place to ensure you get early, cross-functional input on RWD projects that could have multiple applications across the product lifecycle?
• Does your organization make it easy for colleagues from different functional areas to explore sources, methods, and results of past RWD projects to avoid repeating past mistakes and increase the likelihood of future success?