Danielle Holmes is a clinical research nurse and PhD degree candidate in nursing at UMass Amherst. She has nine years of nursing experience spanning critical care, trauma, and stepdown settings across level I academic medical centers, where she worked directly with the AI-assisted clinical decision support tools and consumer wearable data that anchor this session. She is dually board certified in critical care and nursing informatics. Her informatics work focuses on natural language processing applied to nursing documentation and equity evaluation of algorithmic clinical decision support. She developed an original equity evaluation framework for detecting algorithmic bias in AI-assisted clinical decision support, with a companion research proposal using MIMIC-IV sepsis data. She is an incoming NSF SHINE fellow whose dissertation focuses on NLP-driven EHR analysis and clinical AI equity, and she is the developer of a consumer wearable CPR coaching application currently in beta testing.