Our Data and Technology Infrastructure

Learning Enabled by Data Infrastructure in Healthcare 

We work with health system partners to develop data infrastructure in healthcare that provides Learning Health System (LHS) researchers with the technology needed to improve care through practice change implementations. 

Our data technology partnerships also broadly benefit researchers who want to work with Fairview Health Services IT. By adhering to FAIR data principles—Findability, Accessibility, Interoperability, and Reuse—we ensure data usability and transparency.

About the LHS Data Platform

Our Learning Health System data platform is a collaborative framework that stores and processes real world electronic health data to be used in LHS research and healthcare operations. Key aspects of the data platform include:

• Compliant data infrastructure in healthcare aligned with data security regulations and research ethics based on the Academic Health Center–Information Exchange Infrastructure
• Datamarts (subsets of data) that make data accessible and easier to utilize by providing nearly all the variables needed to study specific outcomes, procedures, or disease states across acute and surgical care settings. It provides researchers with the ability to look at the patient’s visit in its entirety, from admission to discharge, including transfers, procedures, medications, and more.
• Data management that makes data more standardized and reusable, including mapping data to the Observational Medical Outcomes Partnership (OMOP) common data model
• A Fast Healthcare Interoperability Resources (FHIR) façade that translates data stored in different formats into a standardized form
• The data platform is developed with support from the Clinical and Translational Science Institute, the Center for Clinical Quality & Outcomes Discovery and Evaluation (CQODE), the Department of Surgery, investigators from the Academic Investment Clinical Program (AICP), and Fairview Health Services.

CLHSSFederated Machine Learning Collaborative

Federated Machine Learning in healthcare is an approach to artificial intelligence (AI) machine learning (ML) development where copies of a model are trained in independent sessions using different sets of data. The trained copies of the model are uploaded to a server and integrated into one, centralized model. This allows models to be trained across organizations without sharing sensitive electronic health record data. Together with M Health Fairview and other partners, CLHSS leads a Federated Computer Vision in Healthcare Collaborative with other partner institutions. In addition to computer vision, CLHSS applies federated learning to other AI/ML uses like Natural Language Processing (NLP).

Interoperable Clinical Decision Support

Clinical Decision Support (CDS) tools give patients and healthcare workers the right information at the right time to improve health outcomes. CLHSS collaborates with other institutions and industry partners to develop CDS solutions that can be used with different data formats through the SCALED Venous Thromboembolism Prevention project.

Customer-Centric Design of Digital Solutions

In addition to our own Digital Technology Innovation (DTI) program, we collaborate with M Health Fairview to build innovative digital solutions that engage patients and care teams in their health.