Our Collaborations
Partnering for Greater Impact
We collaborate with other academic organizations, research institutions, and health systems on a local and national level to build towards an integrated learning health system and advance patient-centered care.
Table of contents
Building a Learning Health System Network
Our Partnerships
Our Funded Research
Historical Projects
Research Collaborations
Building a Learning Health System Network
We work collaboratively to transform research into practice, improving care quality and advancing patient-centered outcomes.
Collaborations and support to advance learning health system capabilities and initiatives are critical to advancing the goals of CLHSS and capitalizing on the value of a national learning health system network. This growing network allows us to extend the impact of our work, for new research to generate and be integrated with external evidence, and for that knowledge to be put into practice.
As a result, we are contributing to and refining the learning health system cycle and advancing initiatives resulting in higher quality, safer, and more efficient patient-centered care. Many of these projects involve healthcare collaboration with institutions nationwide. We continually develop partnerships and initiatives that enhance healthcare data integration and clinical advancements.
Our Partnerships
LEaRN Initiative
We are training embedded scientists to improve health care in collaboration with clinical partners across Minnesota.
Solutions for Home-Based Surgical Recovery
We are designing technology-driven solutions to improve the outcomes of emergency laparotomy patients during their recovery at home.
Advanced Care Planning Delivery
Through the I CAN DO Surgical ACP subaward, we’re testing strategies to deliver advanced care planning tools, ensuring older and seriously ill patients receive care aligned with their goals and values.
ENTRUST-AI
Through the ENTRUST-AI initiative, we’re developing computational approaches to evaluate the reliability of clinical AI predictions and assess patient-specific risks and benefits from interventions.
Our Funded Research
All of Us Risk Modeling
Through the All of Us program, we’re developing innovative risk modeling methods designed to address the unique needs of minority groups.
Data for Stroke Care (DRESS)
Through the I CAN DO Surgical ACP subaward, we’re testing strategies to deliver advanced care planning tools, ensuring older and seriously ill patients receive care aligned with their goals and values.
Dietary Supplements Informatics Framework
We are creating a dietary supplement knowledge base and developing a translational informatics framework to facilitate research on dietary supplement safety and efficacy.
Evaluation of Interoperable CDS System
The SCALED (SCaling AcceptabLE cDs) approach is adapting a Clinical Decision Support system to deliver preventative guidelines for VTE in patients with traumatic brain injury.
Interventions for Alzheimer’s
Through informatics-driven approaches, we leverage multi-modal resources to evaluate the impact of drugs and non-pharmacological interventions on Alzheimer’s and related dementia.
Managing Pain After Laparoscopic Abdominal Surgery (M-PALS)
In partnership with the Minnesota Evidence-based Practice Center (MN-EPC), we are developing and evaluating a set of clinical practice guidelines to improve pain management for abdominal laparoscopic patients.
Our Research Collaborations
Federated Learning Collaborative
We are part of a national community of health data science and AI researchers who are facilitating research collaboration and the development of AI and machine learning healthcare algorithms.
TeamWISE Lab
Through the TeamWISE Lab, we’re building capabilities to use Electronic Health Record (EHR) audit logs and metadata to optimize technology-supported teaming practices.
Practice-Based Research Network
We partner with UMN’s Department of Family Medicine and Community Health’s Practice-Based Research Network (PBRN) to facilitate community-based research and innovation among Minnesota primary care providers. PBRN’s work fosters relationships between researchers and providers, builds research capacity in physicians and staff, and encourages participation in projects.
To learn more about the PBRN and request services, visit the PBRN website.
Historical Projects
Federated and Imbalanced Learning
Advancing Federated Learning for NLP in Healthcare
We developed federated learning for Natural Language Processing (NLP) algorithms to classify clinical text and create new learning methods for handling imbalanced data sets.