A new paradigm for accelerating clinical data science at Stanford Medicine

TL;DR

Stanford Medicine is developing a secure, standardized, and scalable big data platform to accelerate clinical data science, enabling faster access, analysis, and collaboration on sensitive patient data while ensuring privacy and reproducibility.

Contribution

The paper introduces a novel secure Big Data platform that improves data access speed, standardization, and integration for clinical research at Stanford Medicine.

Findings

Reduced data access time for researchers

Enhanced data privacy through anonymization

Standardized data enables reproducible analyses

Abstract

Stanford Medicine is building a new data platform for our academic research community to do better clinical data science. Hospitals have a large amount of patient data and researchers have demonstrated the ability to reuse that data and AI approaches to derive novel insights, support patient care, and improve care quality. However, the traditional data warehouse and Honest Broker approaches that are in current use, are not scalable. We are establishing a new secure Big Data platform that aims to reduce time to access and analyze data. In this platform, data is anonymized to preserve patient data privacy and made available preparatory to Institutional Review Board (IRB) submission. Furthermore, the data is standardized such that analysis done at Stanford can be replicated elsewhere using the same analytical code and clinical concepts. Finally, the analytics data warehouse integrates with a secure data science computational facility to support large scale data analytics. The ecosystem is designed to bring the modern data science community to highly sensitive clinical data in a secure and collaborative big data analytics environment with a goal to enable bigger, better and faster science.