CD2H Labs is our platform for innovation and is a critical component of the CD2H’s Identify, Invent, and Implement approach. The Labs is where we share project prototypes being developed by the CD2H team so that they can be tested and improved by the community. We welcome and encourage the CTSA community to engage with our prototypes here and to provide feedback and suggestions. Your input is essential to the CD2H’s mission and to accelerating the iterative development of impactful and innovative informatics solutions for the CTSA Program, and beyond, to improve patient care.
Project lead: Philip Payne
An open science environment for health analytics
CIELO is an integrated environment that allows teams of researchers and technologists to develop, discover, and adopt or adapt software, tools, and algorithms, along with corresponding data sets, that collectively can support the conduct of clinical and translational research.
Acknowledgements: Washington University in St. Louis, AcademyHealth
Project lead: Kari Stephens
Cloud Data Sharing Demonstration Project
The demonstration project will provide opportunities for CD2H to discover a pathway to: 1) data sharing governance structures that could be a model for the CTSA Program, 2) cloud infrastructure that could be adapted for use by multiple CTSAs, operating multiple data models, and 3) cloud vendor partnerships. It will scale an existing front end tool for self-service against an OMOP repository that can be co-opted by other CTSA institutes in the near term. This project will create groundwork for building scalable solutions that allow more nimble data sharing within and across CTSAs (i.e., API solutions for multiple self-service tools to operate against multiple shared data models like OMOP, i2b2, and FHIR).
Acknowledgements: University of Washington, Washington University in St. Louis
Project lead: Dave Eichmann
CTSAsearch is a integrated discovery engine using VIVO ontology compliant Linked Open Data published by 87 institutions using a number of open source and commercial research profiling systems.
CTSAsearch is an integrated discovery engine using Linked Open Data published by members of the CTSA Consortium and other interested parties using a number of different tools (VIVO, Harvard Profiles, Elsevier Pure, …). Results are available in both text and graphical forms, along with multiple analytic visualizations. Services are also available for Consortium members to embed data into their local sites.
Acknowledgements: Grants: 3UL1TR000128-10S1 and U24TR002306
Project lead: Chunlei Wu
Reusable Datasets through Data Discovery
This data Workgroup project is targeted to bridge the community-developed metadata standards and the CTSA translational and clinical datasets with a set of metadata authoring tools, allowing better data discovery across CTSA hubs and facilitating the new collaboration between sites.
Data-sharing is becoming a more and more common practice among today’s research community. Do you have a dataset/study ready to share across the CTSA Program or the entire research community? CD2H is here to help you promote your dataset/study and work together with you to standardize the dataset metadata to enhance visibility. Nominate your dataset here and your dataset will be considered to be highlighted in our coming CD2H newsletter. CD2H’s Data Workgroup will also contact you to offer help on the metadata-authoring.
Acknowledgements: Scripps Research Institute, Northwestern University, and everyone participated in the breakout session at the recent CD2H All hands meeting for their contributions of ideas and comments.
Project lead: Peter Robinson
Semantically linking laboratory observations in EHRs to medical phenotypes
LOINC is a standard for encoding laboratory observations that is widely used by labs, healthcare organizations, etc. The Human Phenotype Ontology (HPO) is widely used in the rare-disease community as a standard for reporting and analyzing phenotypic abnormalities. This project is creating a computable semantic bridge between both resources that will allow LOINC-encoded laboratory data in EHRs to be transformed in the corresponding HPO codes.
Acknowledgements: LOINC (Regenstrief Institute), NCATS
If you have thoughts on how to improve our prototypes or are working on a cool related project that we should know about, please fill out the form below.