Quality Improvement


Assessing the Effects of EHR Optimization Interventions in Primary Care

Description

This research evaluates the adoption and impact of three electronic health record-optimization interventions—scribes, advanced team-based inbox management, and artificial intelligence-assisted messaging support—on primary care physicians' time, wellbeing, and patient outcomes, with the goal of identifying effective strategies to improve physician satisfaction and care quality and to reduce healthcare costs.

Grant Number
R01 HS029470
Principal Investigator(s)

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)

Improving Pediatric Donor Heart Utilization with Predictive Analytics

Description

This study aims to optimize the use of donor hearts for infants and children awaiting heart transplantation by developing predictive models to assess in real-time the potential for transplant success and to evaluate risk. Researchers plan to display these data through intuitive visualizations on a custom-built interface to reduce clinicians’ cognitive burden, enhance decision making confidence, and help ensure the best donor match for pediatric patients.

Grant Number
R21 HS029548
Principal Investigator(s)

Identifying Sepsis Phenotypes Associated with Antibiotic-Resistant Pathogens Using Large Language Models and Machine Learning

Description

This research uses large language models and machine learning to retrospectively analyze electronic health records of patients with suspected sepsis and identify patterns in treatment outcomes, with the goal of shaping future clinical guidelines that help doctors select the most effective antibiotics for each patient, reduce unnecessary use of broad-spectrum antibiotics, lower the risks of drug resistance, and ultimately improve patient outcomes.

Grant Number
K08 HS030118
Principal Investigator(s)

An AI-Directed CDS Tool to Reduce Iron Deficiency Anemia in Pregnancy: A Randomized Controlled Trial (AID-IDA Trial)

Description

This study will develop and establish the efficacy of an actionable predictive model to identify pregnant individuals at high risk for postpartum hemorrhage which can be used in combination with a clinical decision support tool to reduce the risk of hemorrhage-related morbidity and improve maternal health outcomes.

Grant Number
R21 HS030148
Principal Investigator(s)

A Roadmap for Research: The International Summit on Innovation and Technology in Care of Older People (IS-ITCOP)

Description

This conference convenes interdisciplinary experts from the United States and abroad to define priorities and goals for researching technology use in Long-Term Post-Acute Care (LTPAC), and to identify factors that support or hinder its adoption in LTPAC settings, aiming to promote safer, higher quality, more accessible, and equitable LTPAC globally.

Grant Number
R13 HS030051
Principal Investigator(s)

Digital EMS Point-of-Care Innovation to Improve Rural STEMI Outcomes

Description

This research will develop, implement, refine, and evaluate an app to support clinical decisions for ST-Elevation Myocardial Infarction care in rural areas by emergency medical services providers, reducing the time between first medical contact and reperfusion therapy to reduce morbidity and mortality, and improve health outcomes.

Grant Number
R21 HS029234
Principal Investigator(s)
Previous Principal Investigator(s)

AR-CPR: Refinement and Large-Scale Simulation-Based Testing of a Novel Augmented Reality Point of Care Chest Compression Feedback System

Description

This research will enhance an augmented reality headset used to provide real-time feedback on pediatric chest compressions. Researchers will evaluate the usability and user experience of the augmented reality cardiopulmonary resuscitation tool in an international multicenter randomized simulation study, with the aim of improving the quality of chest compressions and saving lives.

Grant Number
R21 HS029372
Principal Investigator(s)