Academic Medical Center


Machine-Learning Prediction Model for Personalized Urinary Tract Infection Care in Children

Description

The study will develop and implement a validated machine learning model to optimize voiding cystourethrogram timing and use for diagnosing vesicoureteral reflux (VUR) in children, aiming to reduce the significant health and economic impacts of VUR and recurrent febrile urinary tract infections (fUTIs) by standardizing practices, minimizing unnecessary procedures, and ensuring timely diagnosis for those at highest risk, ultimately seeking to prevent renal injury from fUTIs.

Grant Number
K08 HS029526
Principal Investigator(s)

Using Large Language Models to Identify Social Determinants of Health to Enhance Healthcare Services and Equity

Description

This research explores using natural language processing and generative AI to capture and structure social determinants of health from patient narratives, aiming to improve data completeness and quality, enhance clinical decision support, and reduce the manual burden on clinical staff in routine care.

Grant Number
R21 HS029991
Principal Investigator(s)

Peer PLUS: A Client-Centered Digital Intervention for Addressing the Needs of Individuals with Substance Use Disorder

Description

This research will culminate in a randomized controlled trial of the Peer PLUS (People Leveraging Urgent Support) mobile app and companion web-based platform that supports communication between those with substance use disorder and peer recovery coaches to determine if it contributes to long-term recovery for these individuals.

Grant Number
R21 HS028880
Principal Investigator(s)

Clinical Decision Support for Disseminating and Implementing Patient-Centered Outcomes Research Clinical Evidence

Description

This research will create clinical decision support artifacts for three patient-centered outcomes research guidelines around advanced diagnostic imaging using standards to allow them to be shareable, interoperable, and scalable; and implement them in different workflows and settings measuring their impact. 

Grant Number
R18 HS028616
Principal Investigator(s)