Artificial Intelligence
Artificial Intelligence and Human Factors in Healthcare Quality & Safety
Using a conference model, this study convenes a multidisciplinary group of experts to explore the integration of human factors engineering approaches in the implementation of artificial intelligence in healthcare, providing an opportunity for ongoing collaboration and research to disseminate knowledge and implement best practices that enhance efficiency, prevent provider burnout, and ultimately improve healthcare quality, safety, and value.
Development and Assessment of Artificial Intelligence (AI)-Enhanced Pretreatment Peer-review Process to Improve Patient Safety in Radiation Oncology
This research develops and evaluates an artificial intelligence-enhanced pretreatment peer-review process in radiation oncology, aiming to improve patient safety by reducing variability among providers in treatment planning, minimizing clinical errors, and enhancing overall treatment outcomes.
Complexity, Incidence, and Costs Related to Delayed Diagnosis of Venous Thromboembolism in Urban and Rural Primary and Urgent Care Settings
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.
Assessing the Effects of EHR Optimization Interventions in Primary Care
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.
ML-ROVER: Machine Learning to Reduce Laboratory Test Overutilization
The study will develop, validate, implement, and assess the usability of a machine learning based clinical decision support tool designed to reduce laboratory testing overutilization in pediatric intensive care unit patients.
Improving Pediatric Donor Heart Utilization with Predictive Analytics
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.
Identifying Sepsis Phenotypes Associated with Antibiotic-Resistant Pathogens Using Large Language Models and Machine Learning
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.