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

OBJECTIVES: Research presented at International Society for Pharmacoeconomics and Outcomes Research (ISPOR) conferences and published in the society’s journal, Value in Health, offer insight into real-world data (RWD) use in the life sciences industry. Regulators continue to sharpen guidance on real-world evidence, including fit-for-purpose data selection; therefore, it is important to describe the current data landscape. The objective is to quantify and characterize RWD sources utilized for 2023 ISPOR abstracts.

METHODS: The Value in Health June 2023 supplemental issue served as the source of research abstracts. First, abstracts were filtered to identify the subset that included terms to indicate RWD utilization (e.g., “database”, “EHR”, “claims”). ‘Methods’ sections were reviewed manually to determine RWD characteristics for each abstract, including data source category (e.g., claims, electronic health record (EHR), survey) and country. Abstracts were included if methods described direct analysis of RWD (i.e., excluding literature reviews) and identified a RWD source either by name or RWD source category.

RESULTS: Among the 1,816 ISPOR abstracts presented at ISPOR 2023, 834 (46%) mentioned utilization of RWD, and 505 met inclusion criteria for RWD source description. A total of 165 unique RWD sources were cited. The most common type of RWD was administrative claims (N=259 (51%)), followed by EHR (N=162 (32%)), survey (N=90 (18%)), and registries (N=23 (4.5%)). Claims-EHR linked datasets were utilized in 37 (7.3%) abstracts. The majority of RWD sources were from the U.S. (N=429; 95%); ex-U.S. sources were rarely used for this U.S.-based conference (Japan (N=15; 3.0%), and United Kingdom (N=11; 2.2%)). 16 (3.2%) abstracts utilized multiple RWD sources.

CONCLUSIONS: RWD is an essential resource for the life sciences industry. Given the abundance of current RWD options, each with unique strengths and limitations, a broad and current understanding of the RWD landscape and a data source agnostic strategy are beneficial for selecting fit-for-purpose RWD.