Ryan Nikou’s work focuses on analyzing large administrative and healthcare datasets to support evidence-based decision-making. He applies statistical modeling and machine learning techniques to understand workforce dynamics, including staff participation, turnover, and system-level patterns in home healthcare. His research interests include health workforce analytics, longitudinal data analysis, and the integration of data-driven methods to improve healthcare delivery. Ryan is passionate about using tools such as R and Python to develop reproducible analyses and translate complex data into clear, actionable insights.