PIC-SURE
Introduction to PIC-SURE
The Patient-centered Information Commons: Standard Unification of Research Elements (PIC-SURE) platform integrates different layers of clinical and genomic data from diverse data sources, providing a multifaceted approach to biomedical research.
The PIC-SURE platform was built on Informatics for Integrating Biology & the Bedside (i2b2), a data model created for electronic health record data. PIC-SURE is developed with an open-source Apache 2.0 license and deployed in Federal Information Security Modernization Act (FISMA) Moderate ATO and HI-TRUST environments.
Features of PIC-SURE
User Interface and Application Programming Interface
The PIC-SURE platform provides both an intuitive graphical user interface (UI) and an application programming interface (API) to meet different use cases and levels of experience with data manipulation. The PIC-SURE UI allows for an investigator to search for variables of interest and to conduct feasibility queries. In this way, cohorts are built in real-time and results can be retrieved for analysis.
Query Builder
The Query Builder in the PIC-SURE UI allows users to interactively build queries and returns the counts of participants that meet the criteria. This tool functions similarly to a search engine, providing the ability to search a term of interest and returning variables that contain the search term. After determining a variable of interest, users can refine their query to filter participants with certain values and build a cohort. Both single and multiple criteria queries are supported.
GitHub
PIC-SURE is developed under an Apache 2.0 license; all code is open source and accessible on the PIC-SURE All-in-one GitHub repository.
For users interested in the PIC-SURE API, several Jupyter notebooks (R and Python) and R Markdown files contain coding examples for key PIC-SURE use cases. These examples provide a starting point for users to get started with their own analyses and are publicly available on the Access to Data Using PIC-SURE API GitHub Repository.
Training Materials
Documentation
Written documentation for PIC-SURE can be found on the PIC-SURE Gitbook documentation.
Videos
The PIC-SURE YouTube channel has video demonstrations of PIC-SURE features such as searching, filtering, and building cohorts. Researchers can find videos specific to certain PIC-SURE environments by viewing the playlists.
PIC-SURE Instances
Demo PIC-SURE
The demonstration site of PIC-SURE allows users to access the National Health and Nutrition Examination Survey (NHANES) dataset. You can submit a request to access the site by emailing avillach_lab_developers@googlegroups.com.
NHLBI BioData Catalyst®
The National Heart, Lung, and Blood Institute BioData Catalyst® (BDC) is a cloud-based ecosystem that offers researchers data, analytic tools, applications, and workflows in secure workspaces.
BDC Powered by PIC-SURE is available to search hosted heart, lung, blood, and sleep data publicly.
Genomic Information Commons
The Genomic Information Commons (GIC) is a continuously updating, queryable, federated genomic data commons. Inspired by a common vision of accelerated genomic research discovery, collaboration, and improved clinical outcomes, leaders at Boston Children’s Hospital (BCH), Harvard Medical School, Cincinnati Children’s Hospital Medical Center (CCHMC), the Children’s Hospital of Philadelphia (CHOP), St. Louis Children’s Hospital (SLCH)/Washington University at St. Louis (WUSTL), and the University of Pittsburgh Medical Center (UPMC) have come together to create the GIC.
The GIC is available to those in the GIC consortium. You can learn more at the GIC website.
AIM-AHEAD
The National Institute of Health's AIM-AHEAD program has established mutually beneficial, coordinated, and trusted partnerships to enhance the participation and representation of researchers and communities currently under-resourced in the development of artificial intelligence and machine learning models and to improve the capabilities of this emerging technology, beginning with electronic health records (EHR) and extending to other data sets to address health inconsistencies.
To learn more, please refer to the AIM-AHEAD website.