A Real-World Data Landscape Review of the 2023 International Society for Pharmacoeconomics and Outcomes Research Europe Conference

OBJECTIVES: The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) conference presentations and the society’s journal, Value in Health, offer insight into real-world data (RWD) use in the life sciences industry. Understanding the current RWD landscape is important for all stakeholders as regulators continue to sharpen guidance on fit-for-purpose data selection. The objective of this study is to characterize RWD sources utilized for 2023 ISPOR Europe abstracts.

METHODS: The Value in Health supplemental issue (December 2023) served as the source of abstracts presenting research that utilized real-world data. Abstracts were filtered to identify those that included terms indicating analysis of RWD (e.g., “database”, “electronic health record” (EHR), “claims”). ‘Methods’ sections were reviewed to determine characteristics of each source mentioned, including source category (e.g., claims, EHR, patient survey), use of linked data and country. Abstracts were included if methods described direct analysis of RWD (i.e., excluding literature reviews, provider/payer surveys) and identified the source either by name or source category.

RESULTS: Among the 2,409 abstracts presented at ISPOR Europe 2023, 903 (37%) mentioned utilization of RWD, and 438 met inclusion criteria for describing the source characteristics. 170 unique RWD sources were used in these 438 abstracts. The most common type of RWD was administrative claims (N=192 [44%]), followed by patient survey (N=103 [24%]), EHR (N=102 [23%]), and registries (N=33 [8%]). Linked data sources were utilized in 15 (3%) abstracts. While 219 (50%) of abstracts at this European conference utilized data from at least one European country, 96 (22%) were US only and 87 (20%) were from other geographies.

CONCLUSIONS: RWD is central to life sciences research and the current data landscape is well-developed with hundreds of sources, each having unique strengths and characteristics. A data-agnostic strategy and updated knowledge of the global data landscape are beneficial for identifying and selecting fit-for-purpose RWD.