What’s informing HEOR strategies in 2025? Insights from our experts

There continues to be more regulatory guidance on the use of real-world data (RWD) and real-world evidence (RWE), which helps us serve our clients’ needs to innovate and evolve in the marketplace, as well as the role RWE plays in understanding the clinical and economical value of therapies.

We recently sat down with Ami Buikema, vice president, research consulting in HEOR, and Rifky Tkatch, Ph.D., director, primary data collection in HEOR, Optum Life Sciences, to talk about how they’re helping clients prepare for 2025.

What really stood out to you in your conversations with clients regarding HEOR strategies?

Ami:  A common theme I heard 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 RWE 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 as valued partners.

Rifky:  I think a shift I am noticing is, that HEOR is not just HEOR. Clients are coming to discuss how HEOR data meets the goals across their teams such as reimbursement, market access or primary data collection. HEOR data is needed to meet all of these different business goals for various stakeholders and regulatory agencies.

And what we’re offering is more than HEOR. We offer value-based contracting, modeling or primary data collection, we can work across the enterprise through formal channels to collaborate in that way to provide all of these different services as a unified team. The shift to working more cross functionally reflects the experience of our team as a part of a large healthcare organization.

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 are indeed 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 prioritize collecting RWE that reflects PROs, which is vital for supporting regulatory submissions and reimbursement negotiations. Clients creating IRA packages are including more experiences of patient, caregiver and people with disabilities based on guidance from the Centers for Medicare and Medicaid Services (CMS) that information can be used in negotiations.

Rifky:  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. For example, a study published in 2021 found that 20% of drug labels included patient reported outcome data on their drug labels between 2006-2015. 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 in this current landscape?

Ami:  The number of sessions and presentations on GLP-1 treatments at conferences such as ISPOR 2024 really 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 teams are involved in numerous discussions about the cost and relative value of these therapies for patients and payers to inform policy decisions

Rifky:  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, linking the patients voice to their medical, pharmacy claims, and electronic health records, and strategic messaging can be invaluable here.

Health equity and AI are themes that garnered a lot of attention recently. What are you seeing in these areas? 

Rifky:  We continue to see interest in finding ways to identify gaps in care, especially related to social determinants of health. Increasingly, we’re able to tap into RWD sources that can help us identify social determinants of health 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 provide valuable insights and can be combined with other RWD for analyses of treatments, clinical outcomes or total cost of care.

Ami: AI also has the potential to play a role in health equity and improving patient care. For example, Optum is developing a number of different AI 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 use cases of AI include helping to identify under-diagnosed and undiagnosed patients, 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 AI may assist a healthcare provider.

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