How real-world evidence can inform pharma strategies in 2025

Before we dive in, this team has transitioned from being the HEOR to value and evidence solutions team. Why the change?

Stacey: The name reflects a shift that I am noticing – HEOR is not just HEOR. Clients are coming to discuss how HEOR evidence meets the goals across their teams such as reimbursement, market access or healthcare implementation. HEOR data is needed to meet all these different business goals for various stakeholders and regulatory agencies.

What we’re offering is more than HEOR, and we wanted to represent that in our team name. Our solutions include:

• Value-based contracting 
• Health economic modeling 
• Observational research 
• Primary data collection 
• Advanced analytics

We can work across the enterprise through formal channels to collaborate in ways that provide all of these different services as a unified team. The shift to working more cross functionally within our business and in collaboration with teams across the enterprise, with appropriate firewalls, reflects the experience of our team as a part of a large healthcare organization.

What has stood out to you in recent conversations with clients?

Ami:  A common theme I am hearing from clients is transformation in delivery of healthcare and finding ways to collaborate across the healthcare industry in order to make that happen. Life sciences organizations are thinking more broadly about how they can impact the healthcare system to generate change and the role their portfolio plays into that dynamic.

Pharma manufacturers are also working more cross functionally across teams within their organizations, leading to more complex analytics and research requests. HEOR and other real-world evidence generation teams have traditionally been more insulated from the commercial and medical affairs sides of the business. For certain engagements, it’s increasingly necessary to communicate and collaborate across the broader organization, with HEOR teams engaged early on as valued partners.

Switching gears, has there been much discussion on patient engagement and how it’s evolving, especially considering the regulatory landscape?

Ami:  Patient engagement and the integration of the patient voice into HEOR and RWE are central themes. Regulatory bodies in the US and Europe are increasingly emphasizing the importance of patient-centered data. They’re asking for patient-report outcomes (PROs) from clinical trials in areas where it makes sense. They want quality of life data not just for clinical trials, but for RWE.

This shift means our clients should incorporate more real-world evidence that represents the patient perspective, which can support regulatory submissions and reimbursement negotiations. Clients creating IRA packages are including more evidence about the experiences of patients, caregivers and people with disabilities based on guidance from the Centers for Medicare and Medicaid Services (CMS) and that information can be used in pricing negotiations.

Stacey:  Right, and it’s not just about meeting regulatory requirements, although this focus has clearly shifted in recent years and has become a greater focus. I think that integrating the patient voice can significantly enrich the datasets we generate, making them more reflective of true patient experiences and outcomes. This enriched data can provide our clients with a competitive edge by differentiating their products in the marketplace.

As the focus on GLP-1s continues, especially in the context of obesity treatment and newer indications, how are we helping clients?

Ami:  The number of sessions and presentations on GLP-1 treatments at conferences recently highlights the interest and the rapidly evolving landscape, particularly noting that insurance coverage for obesity treatments is expanding. It’s crucial for our clients to stay informed about these policy changes as they have significant implications for market access and reimbursement strategies.

Our therapeutic leads in endocrine/metabolic, cardiovascular diseases, and even kidney disease are closely following these developments as access to GLP-1 therapy expands, and our value-based services team is involved in numerous discussions about the costs and relative value of these therapies for patients and payers to inform policy decisions.

Stacey:  And beyond policy and reimbursement, the competitive dynamics within the GLP-1 market are intensifying. Our clients need to stay informed not only about the clinical and economic value of their offerings, but also about how to communicate this effectively to payers and patients:.

• What differentiates their product?
• What are the other unexpected benefits of these products?
• What economic factors will drive payers to put the treatment on formulary?

Our expertise in generating robust evidence, including combining the patient voice with data from their medical and pharmacy claims and electronic health records, can be invaluable to support strategic messaging here.

Social determinants of health (SDOH) and predictive analytics continue to garner a lot of attention. What are you seeing in these areas?

Stacey: We continue to see interest in finding ways to responsibly use predictive analytics, especially related to SDOH. Increasingly, we’re able to tap into RWD sources that can help us identify SDOH and other health drivers. In addition, with our primary data collection capabilities, we can simply ask patients about their own experiences to directly capture social determinants and other barriers to care. Patient-reported information can also be combined with other RWD for analyses of treatments, clinical outcomes or total cost of care.

Ami: Predictive analytics also has the potential to help support improving patient care. For example, Optum is developing a number of different models to help identify social determinants of health — either through natural language processing with our clinical notes data or through more traditional machine learning models on our claims and EHR. The data from these models are integrated into our RWD sources and help provide a more holistic picture of the drivers of outcomes and costs associated with therapy.

Other responsible use cases include helping to identify under-diagnosed and undiagnosed patients to improve their care, identifying patients that might need a change in therapy, or identifying patients who might be candidates for an intervention, and these are just a few examples of how predictive analytics may contribute to a patient-centered strategy.

For our clients, this means there's an opportunity to utilize predictive analytics to help change how patients are supported and care is provided, which could have a big impact on improving health more broadly.