USA, MA, Boston

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.

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.

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.

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.

This research will develop and evaluate the Perioperative Optimization of Senior Health (myPOSH) mobile application that supports care transitions and the needs of older adults undergoing surgery and their care partners.

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. 

This research will adapt and extend Intervention INC, a family-centered, interactive, health promotion tool, and evaluate its effectiveness on reducing childhood obesity risk.

This research will refine a current health information exchange platform to improve data exchange for inter-hospital transfers, evaluate its impact, and create a dissemination toolkit so that others may adopt this model.

This study will develop, evaluate, and disseminate a multicomponent intervention including a mobile health application and digital navigator training to support safe care transitions for patients with multiple chronic conditions.

This research will use digital health tools leveraging patient-reported outcomes and data from electronic health records to engage individuals with multiple chronic conditions to improve understanding of individualized risk of adverse events during care transitions.