Dr. Tsui's research interests include clinical informatics, natural language processing, artificial intelligence and machine learning, population informatics, data science, signal processing, mobile healthcare, and large real-time clinical production systems. He's published over 100 peer-reviewed papers.
Dr.Grundmeier’s research focuses on maximizing the existing and future potential of electronic health records (EHRs) for clinical research and knowledge delivery, with an overarching goal of improving health and healthcare for children.
Dr. Muthu's research interest is in the area of cognitive informatics, with a focus on the refinement and application of cognitive engineering methods to improve health information technology safety and augment clinical decision-making.
Dr. Fiks’ research is aimed at improving outcomes for ambulatory pediatric patients through primary care, practice-based scholarship with a focus on improving health and healthcare decision-making through health information technology.
Dr. Zorc's work focuses on the intersection of interventional clinical research, quality improvement (QI), and clinical informatics. He has formal certification in epidemiology, QI methodology, and clinical informatics, and has participated in multi-center research networks, guideline and improvement collaboratives, and electronic health record development locally and nationally.
Dr. Bailey’s research centers on the development of learning health systems. This endeavor includes the creation of methods to share data across hospitals, recognize the clinical implications of information gathered from different sources, and provide support to clinicians to use this information more effectively.
Dr. Bonafide’s research focus is on developing, evaluating, and implementing interventions at the intersection of patient safety and technological innovation, and measuring the impacts of these interventions on children and families. His research includes methods drawn from clinical epidemiology, biostatistics, clinical informatics, systems engineering, and implementation science.
Dr. Masino and his team research the application and development of machine learning methods to inform basic scientific discovery and the creation of predictive analytic models for personal health and clinical decision support.