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 examines whether remote therapeutic monitoring can improve physical therapy outcomes by increasing patient engagement, adherence to home exercises, and communication with providers, while also assessing its impact on healthcare costs and feasibility for broader implementation.
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 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.
