Merging Safety-Research Database for and From Economic Evaluations (SRD-EE) With an AI-Supported Living Review Database to Improve the Availability of Adverse Event-Related Data

Speaker(s)

Fens T1, Postma MJ2, Hanegraaf P3, Mosselman JJ4, Boersma C5, van der Schans J6
1University of Groningen,University Medical Center Groningen, Department of Health Sciences and Helth-Ecore, Groningen, GR, Netherlands, 2University of Groningen,University Medical Center Groningen, Department of Health Sciences and 4. Department of Economics, Econometrics & Finance/ Health-Ecore, Groningen, GR, Netherlands, 3Pitts, Zeist, Netherlands, 4Pitts, Zeist, UT, Netherlands, 5University of Groningen, Department of Health Sciences, UMCG; Open University, Heerlen, Department of Management Sciences and Health-Ecore Ltd, Zeist, The Netherlands, Zeist, UT, Netherlands, 6University of Groningen, Department of Health Sciences (UMCG) and Economics, Econometrics & Finance; Open University, Heerlen, Department of Management Sciences and Health-Ecore Ltd, Groningen, GR, Netherlands

Presentation Documents

OBJECTIVES: The need for improved methods and guidelines for incorporating adverse events (AEs) in health technology assessment (HTA) has recently been recognized. AEs need to be systematically and comprehensively included in HTA despite their often-reported minimal impact on cost-effectiveness or conceptually opposing the utility scores. However, a major limitation is the availability and accessibility of AE data.

METHODS: In response, we created Safety-Research Database for and from Economic Evaluations (SRD-EE) (available at srdee.com), an online interactive crowdsourcing platform. The database is designed to address multiple stakeholders: health-economic researchers can search for or add relevant safety data, regulators can verify the comprehensiveness of AE inclusion, decision makers and payers can assess the impact of safety on public health decisions, and the private sector can scope for safety-related data.

RESULTS: Experience has shown that populating such a platform with data was not something users will do volonatry therefore, necessitating an update to authomatize this process. To achieve this, we introduced another web-based platform, pitts.ai, which facilitates continuous literature updates, literature screening, and data extraction through user reviews. By integrating these two tools with an AI predictive model for data extraction we aim to automate the population of SRD-EE with data on AE-costs, -durations, -frequencies and -disutilities.

CONCLUSIONS: This automation is crucial for updating initial economic analyses when introducing new products, as the use of new product in a heterogeneous population provides additional insights into AEs.

Code

SA124

Disease

No Additional Disease & Conditions/Specialized Treatment Areas