Advancing Healthcare with AI

At CHIDS, we are proud to be leaders in AI-infused healthcare transformation. Our industry research collaborations help companies and health systems design, deploy and evaluate next-generation AI technologies for better health care and outcomes. We welcome new partners and trainees on our collective journey to effectively combine AI, information systems and behavior science in creative new ways.

1/5 of unmet clinical demand could be addressed by AI alone.

AI is estimated to save more than $150 billion in healthcare by 2025.

Half of all hospitals plan to invest in AI solutions by 2021.

 

Healthcare Insights AI Lab

 

The Healthcare Insights AI Lab was launched in 2017 under the direction of professor Gordon Gao and is staffed by CHIDS-affiliated faculty scientists and graduate students, who work in close collaboration with Inovalon (NYSE: INOV) and the New Jersey Institute of Technology under the direction of professor Yi Chen to envision, create and grow impactful AI-based projects.

Friend or Foe? The Influence of Artificial Intelligence on Human Performance in Medical Chart Coding

A Novel Deep Learning System for Patient Disease Extraction in Clinical Notes

 

Predictive Analytics for Clinical and Operational Support

 

CHIDS researchers are at the forefront of leveraging cutting-edge predictive analytics to enable better care delivery and health outcomes. Funded projects range from identifying individuals at risk of opioid abuse, predicting which physicians will engage in fraud, and predicting health trajectories of patients with prediabetes. 

 

Precise Digital Health

 

The rise of digital health provides unprecedented opportunity to deliver precise, timely help to people trying to stay healthy and manage complex conditions. Our digital health initiatives aim to use insights from data and behavioral science to deliver the right intervention to the right person at the right time. Current collaborators include Welldoc, Vheda Health, and other leaders in the field.

A Novel Approach to Assess Patient Burden Using Data from a Digital Therapeutic for Type 2 Diabetes Predicts Glucose Outcomes.

Early Engagement Measures Can Accurately Identify Users at Risk of Abandoning Digital Therapeutics in Type 2 Diabetes.

Interested in working on an AI project with the team? Please reach out to us at chids@rhsmith.umd.edu.