Immunolab: Combining targeted real-world data with advanced analytics to generate evidence at scale in immunology

Abstract

Real-world evidence (RWE) has traditionally been used by regulatory or payer authorities to inform disease burden, background risk, or conduct post-launch pharmacovigilance, but in recent years RWE has been increasingly used to inform regulatory decision-making. However, RWE data sources remain fragmented, and datasets are disparate and often collected inconsistently. To this end, we have constructed an RWE-generation platform, Immunolab, to facilitate data-driven insights, hypothesis generation and research in immunological diseases driven by type 2 inflammation. Immunolab leverages a large, anonymized patient cohort from the Optum electronic health record and claims dataset containing over 17 million patient lives. Immunolab is an interactive platform that hosts three analytical modules: the Patient Journey Mapper, to describe the drug treatment patterns over time in patient cohorts; the Switch Modeler, to model treatment switching patterns and identify its drivers; and the Head-to-Head Simulator, to model the comparative effectiveness of treatments based on relevant clinical outcomes. The Immunolab modules utilize various analytic methodologies including machine learning algorithms for result generation which can then be presented in various formats for further analysis and interpretation.