Primary Care

This research aims to improve the early detection of venous thromboembolism in primary and urgent care by using mixed methods (stakeholder interviews and surveys, electronic health records, and machine learning) to better understand missed and delayed diagnoses, identify risk factors, and develop tools to enhance patient safety.

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 study will develop a prototype guide for the safe and effective integration of ambient digital scribes into primary care, providing insights into how this artificial intelligence-driven technology transforms workflows while addressing critical safety and burnout concerns in diverse healthcare settings.

The study will create, implement, and test a patient-centric web app to support older adults with chronic conditions in comprehending, managing, and acting upon their lab test results.

This research will enhance, refine, and evaluate an artificial intelligence-supported tool that collects patient-reported outcomes to inform care and improve glycemic control for patients with poorly controlled diabetes.

This research will enhance an existing mobile platform to support self-management and shared decision making for those with asthma. Researchers will use patient-reported outcomes and calculate a Respiratory Symptoms Risk Score using voice biomarkers, with the aim of improving outcomes. 

This research is developing and evaluating a machine learning algorithm that uses existing electronic health record data to predict childhood asthma treatment response and better inform personalized treatment. 

This research will utilize user-centered design to develop and implement a self-management education and support module called Support-Engage-Empower-Diabetes (SEE-Diabetes) to improve diabetes management in older adults.

This research will develop, implement, and evaluate an artificial intelligence-enabled workflow that would allow for online reading and same-day diagnostic imaging following screening mammography in breast cancer screening clinics.