Case Study

A unified virtual framework for integrated data across multiple research centers to enable personalized healthcare

Situation

One of the largest US managed care consortiums, based in California, is made up of 3 interdependent entities, with over 12M patients across 39 hospitals. This organization is building a strategy for personalized healthcare in order to continue operating on the forefront of medical innovation and providing the best care to patients. Their quality of care has been highly rated and attributed to a strong emphasis on preventive care, innovative delivery models, and high quality, lower cost care. Accordingly, this organization strives for excellence, looking to continuously develop, adopt and incorporate latest medical innovations. Thus, there are significant efforts towards genetic studies, personalized vaccine developments, and more. Additionally, this organization operates a Research Biobank that houses 300k samples, with an overall goal of 500k samples to accelerate research and discovery. Their overall mission is to drive research for medical breakthroughs through data in the form of patient sample recruitment. Patients are contributing their samples as part of a large scale research effort and the biobank data is growing daily, quickly representing one of the world’s largest research biorepositories.

Challenge

This organization is continually collecting patient samples for a large-scale research effort. However, the management, harmonization, access, and analysis of data continues to present challenges to preventative medicine, discovery and personalized care. With the biobank data growing on a daily basis and quickly representing one of the world’s largest research centers, many hurdles arise:

  • How to build an architecture that houses clinical and genetic data together
  • How to maintain security, while also allowing for researchers to access this robust participant inventory through queries for their precise cohort of interest.
  • Once cohorts are identified, how to allow the request/permission for access to this dataset for analysis and use in the multiple research study efforts across all internal entities.
  • How to build a scalable system that operates across hundreds of research studies
Actions

BC Platforms provided and configured a state-of-the-art platform to address organization needs when building a precision medicine strategy

  • Implemented the discovery and research solution powered by BC|INSIGHT to hold integrated clinical and genomic data and analytics tools for research purposes
  • Data Query and Cohort building tools support organization researchers to quickly answer questions related to available data and segment patient cohorts for various study purposes
  • This framework also enables collaboration as external researchers can examine subsets of overall data through secure desktop access granted by administrators to BC|INSIGHT. Once data access is granted, researchers can view their cohorts through secure environments via BC|SAFEBOX
  • To optimize this architecture, it is deployed completely on Microsoft Azure in cloud containers, and guarantees security and integrity of the data while allowing full interoperability needed for cross study, collaborator and departmental access within a highly unique research analytics and collaboration platform
Impact

This collaboration has successfully moved one of the largest research and healthcare centers in the US from a highly manual process of searching data files to fulfill specific research questions, to an efficient, interoperable uniform platform for research and discovery.

  • Enables efficient means of targeting diverse research populations, ultimately resulting in more precise findings for personalized care and accelerated translation of research insights into clinical practice
  • Fully integrates genomic and phenotypic data which is easily organized, segmented, and queried in an iterative process until the researcher has encompassed all subject aspects needed to form a cohort of interest
  • Ensures optimal automation in the end to end process of data collection, management, harmonization and access