USA, MA, Boston


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)

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)

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)