Tokenization-Linked Social Determinants of Health (SDoH) Data: A Gateway to Enhanced Understanding of Rare Disease Clinical Trial Populations

OBJECTIVES: This presentation explores the linkage of clinical trial data to Social Determinants of Health (SDoH) data to enrich our understanding of patients in rare disease clinical trials via clinical trial tokenization. Tokenization refers to the process of converting sensitive patient data into unique identifiers to support privacy-preserving data linkages, allowing for secure and anonymous linked data analysis.

METHODS: By linking trial patients with SDoH metrics such as housing status, employment, food security, and other socio-economic factors with social risk metrics related to financial strain, food insecurity, housing instability, health literacy, and transportation, we can uncover insights into the complexities influencing health outcomes and trial participation. The amalgamation of tokenized medical data with SDoH indicators provides a comprehensive, 360-degree view of the patient. This approach enables researchers to identify and understand the intricate interplay between a patient's socio-economic circumstances and their health.

RESULTS: We will present a framework and emerging case studies demonstrating how this method has identified correlations between SDoH factors and trial recruitment, adherence, and outcomes. For instance, understanding the impact of food deserts on medication adherence or the influence of financial opportunity on patient retention in trials offers invaluable insights for tailoring trial design and patient support programs. Furthermore, we will discuss the ethical considerations and data privacy implications of using tokenization in conjunction with SDoH data. The presentation will highlight how this methodology complies with data protection regulations and respects patient confidentiality and promotes trust.

CONCLUSIONS: The presentation will argue that the integration of tokenization with SDoH data represents a significant advancement in rare disease clinical trials. It not only enhances our understanding of patients in clinical trials, but also paves the way for more personalized, equitable, and effective healthcare interventions, ultimately contributing to improved patient outcomes for rare disease patients.